system

Abstract

To provide a system.SOLUTION: A system includes: means to accumulate sales activity to a client, attempts to realize success of the client using an in-house product and an in-house service, a corresponding log for customer support, a progress situation for a contract with the client, and information including the contract content to client relationship management (CRM) system; creation means for creating a health score which is a digitized index of a health state and satisfaction of the client; extraction means for extracting a company where the health score is a constant threshold or less and a company with which a constant period has passed from an immediate contract renewal as company groups with specific conditions; means for creating a list of clients selected based on a specific condition targeting a customer success manager (CSM) who is a person to carry out a roll for supporting the success of a client; means for distributing the list; a setting unit for setting timing for a next contact to the client when transmission of reference material to the client is completed; an engine for recognizing emotions of the client; and means for digitizing the emotions.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In traditional customer success management, understanding customer situations and responding in a timely manner required manually analyzing large amounts of data and planning actions, which required a great deal of time and effort and, because it relied on human judgment, was prone to errors and oversights. [Means for solving the problem]

[0005] The disclosed system includes a means for accumulating information including sales activities to customers, efforts to realize the success of customers who use the company's products and services, customer support response history, progress of contracts with customers, and the contents of contracts with customers in a customer relationship management (CRM) system, a creation means for using a generative AI model to create a health score that is an index that quantifies the health status and satisfaction of customers, an extraction means for extracting companies whose health score is below a certain threshold and companies for which a certain period of time has passed since their most recent contract renewal as a group of companies that meet certain conditions by inputting specific prompt sentences into the generative AI model, and a customer success manager who is a person responsible for supporting customer success. The health score system includes: a means for creating a list of customers selected based on specific conditions for a customer success manager (CSM); a means for distributing the list to the customer success manager (CSM); a setting unit for setting the next timing of contacting the customer when the customer success manager (CSM) has confirmed the distributed list and completed sending materials to the customer; an emotion engine for recognizing the emotions of the customer who is a user communicating with the customer success manager (CSM); and a means for quantifying the emotions, wherein the creation means uses a generative AI model to create the health score based on the information stored in the customer relationship management (CRM) system and the quantified emotions. [Brief explanation of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] One embodiment of the present invention provides a means for storing all sales activities, customer success and customer support response history, contract status, etc. in a customer relationship management (CRM) system. Specifically, a system is built that includes an interface and API for storing various customer information in the CRM. This allows various communications and transaction histories with customers to be managed in a unified manner, creating a database for analysis.

[0029] "Example 2"

[0030] Furthermore, a means of creating health scores using generative AI will be provided. Specifically, an AI engine will be built that analyzes customer situations and behaviors based on various data accumulated in CRM and calculates a health score for each customer. This health score will be used to quantitatively evaluate important business indicators such as the probability of customer contract renewal and the possibility of upselling.

[0031] "Example 3"

[0032] It also provides a means to extract groups of companies that meet certain conditions, such as signs of cancellation, changes in schedule, or upselling. Specifically, a filtering function will be built that automatically extracts groups of companies that meet specific conditions based on health scores and other analysis results. For example, it is possible to automatically list companies that meet certain conditions, such as companies with health scores below a certain threshold, or companies where a certain amount of time has passed since their most recent contract renewal.

[0033] "Example 4"

[0034] Furthermore, a means will be provided for CSMs to generate "customer lists to contact today, this week, and this month" and draft emails and content. Specifically, a function will be built that plans contact with the extracted group of companies at the appropriate timing and content, and automatically generates action lists and content drafts based on that. This means that CSMs only need to check, distribute, and respond, and after entering the results, the timing of the next contact will be set automatically.

[0035] The processing flow of each embodiment will be described below.

[0036] "Example 1"

[0037] Step 1: Using an interface or API to store customer information in the CRM, store all sales activities, customer success and customer support response history, contract status, etc. in the CRM.

[0038] "Example 2"

[0039] Step 1: Obtain various data stored in CRM.

[0040] Step 2: Based on the acquired data, generative AI is used to analyze the customer's situation and behavior. Step 3: Based on the analysis results, a health score is calculated for each customer.

[0041] "Example 3"

[0042] Step 1: Get your health score and other analytics.

[0043] Step 2: Based on the results obtained, automatically extract a group of companies that meet specific conditions. "Example 4"

[0044] Step 1: Plan appropriate timing and content for contacting the selected group of companies.

[0045] Step 2: Automatically generate action lists and content drafts based on your plan.

[0046] Step 3: The CSM reviews the generated action list and content drafts and distributes or responds to them.

[0047] Step 4: After entering the results, automatically set the next contact timing.

[0048] Example 1

[0049] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0050] With traditional customer relationship management systems, it is difficult to centrally manage customer information such as sales activities, customer success and customer support response history, and contract status, which makes it easy for data to be scattered and duplicated. Furthermore, analyzing customer information, creating health scores, detecting signs of churn, and identifying upselling opportunities are all done manually, which is inefficient and prone to human error. Furthermore, creating contact lists and drafting emails for customer success management is time-consuming, making it difficult to respond quickly.

[0051] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0052] In this invention, the server includes means for accumulating all sales activity, customer success and customer support response history, contract status, etc. in a customer relationship management system, means for creating a health score using generative artificial intelligence, means for extracting groups of companies that meet conditions such as signs of cancellation, changes in circumstances, and upselling, means for generating a "customer list to contact today, this week, and this month" for customer success management and drafting emails and content, means for customer success management to check, distribute, and respond, means for setting the timing of the next contact after inputting the results, means for a user to input customer information from a terminal, means for the terminal to send the input data to the server via an application program interface, means for the server to save the received data in the customer relationship management system database, means for the server to process the saved data for analysis, and means for the server to provide the analysis results to the user. This enables centralized management of customer information, efficient analysis, and rapid response.The server includes a means for accumulating information including sales activities to customers, efforts to realize the success of customers who use the company's products and services, customer support response history, progress of contracts with customers, and the contents of contracts with customers in a customer relationship management (CRM) system, a creation means for creating a health score that is an index that quantifies the health status and satisfaction of customers using a generative AI model, an extraction means for extracting companies whose health score is below a certain threshold and companies for which a certain period of time has passed since the most recent contract renewal as a group of companies that meet certain conditions by inputting specific prompt sentences into the generative AI model, and a customer support person who is a person who plays a role in supporting customer success. The system includes: a means for creating a list of customers selected based on specific conditions for a customer success manager (CSM); a means for distributing the list to the customer success manager; a setting unit for setting the next timing of contacting the customer when the customer success manager, who has reviewed the distributed list, has completed sending materials to the customer; an emotion engine for recognizing the emotions of the customer who is a user communicating with the customer success manager; and a means for quantifying the emotions. The creation means may use a generative AI model to create the health score based on the information accumulated in the customer relationship management system and the quantified emotions. The server may include means for including customers who are likely to churn and upsell in the list based on the emotions. The server may include means for displaying the health score on a smartphone.

[0053] "Sales activities" are a series of actions taken to propose products and services to customers and promote sales.

[0054] "Customer success" refers to support activities that enable customers to make the most of the products and services they have purchased and increase their satisfaction. "Customer success" refers to efforts to ensure that customers have a successful experience. "Customer success" refers to efforts to ensure that customers who use your company's products and services are successful.

[0055] "Customer support response history" is a record of responses to customer inquiries and problems.

[0056] "Contract status" is information indicating the content and progress of the contract concluded with the customer.

[0057] A "customer relationship management system" is a software system that centrally manages customer information and efficiently conducts sales activities and customer support.

[0058] "Generative AI" is an AI technology that automatically analyzes and predicts data.

[0059] A "health score" is a numerical indicator of a customer's health status and satisfaction.

[0060] "Cancellation warning signs" are signs that indicate the possibility that a customer may cancel a service or contract.

[0061] "Change in equipment" refers to a change in the service or product that a customer is currently using.

[0062] "Upselling" is a sales technique that encourages customers to purchase more expensive products or services.

[0063] "Customer success management" is a management activity aimed at supporting customer success and building long-term relationships.

[0064] An "application program interface" is an interface for exchanging data between different software systems.

[0065] A "terminal" is a device such as a computer or smartphone operated by a user.

[0066] A "server" is a computer system for storing and processing data.

[0067] A "database" is a system for efficiently storing, searching, and managing data.

[0068] "Analysis" is the process of extracting information and finding meaning from data.

[0069] "Email" is a digital message sent and received over the Internet.

[0070] "Content" is a general term for information and materials provided to users.

[0071] "Drafting" refers to creating a draft of a document or email.

[0072] This invention is a system that uses a customer relationship management system (CRM) to centrally manage customer information such as sales activities, customer success and customer support response history, and contract status, thereby achieving efficient analysis and rapid response. A specific embodiment of this system is described below.

[0073] Hardware and software used

[0074] Hardware: A powerful server (e.g., Dell PowerEdge R740)

[0075] Software: CRM systems (e.g., Salesforce), database management systems (e.g., MySQL®), API management tools (e.g., Postman)

[0076] Program processing

[0077] The server receives the customer information entered by the user from the terminal and stores it in the CRM system. Specifically, it performs the following processes:

[0078] 1. The user enters customer information from the terminal.

[0079] The user opens a browser on the device and accesses the input form of the CRM system. For example, the user enters "Customer name: Company A, Business content: New product proposal, Date: October 1, 2023."

[0080] 2. The device sends the entered data to the server via API.

[0081] The terminal converts the input data into JSON format and sends it to an API endpoint using an HTTP POST request, for example, " / api / v1 / customers".

[0082] 3. The server saves the received data in the CRM system database

[0083] The server parses the received JSON data and generates an SQL query to insert it into the database. For example, it executes the query "INSERT INTO customers (name, deal_content, date) VALUES ('Company A', 'New product proposal', '2023-10-01')".

[0084] 4. The server processes the stored data for analysis.

[0085] The server aggregates the stored data and filters it based on certain conditions, for example, by running the query "SELECT COUNT() FROM deals WHERE status = 'Success' AND date BETWEEN® '2023-01-01' AND '2023-12-31'".

[0086] 5. The server provides the analysis results to the user.

[0087] The server converts the analysis results into graphs and charts and displays them on a dashboard, for example, a pie chart showing success rates, which is displayed in the user's browser.

[0088] Examples of concrete examples and prompts

[0089] Examples:

[0090] When a user wants to enter information about a business deal with a new customer, he or she follows the steps below.

[0091] 1. The user enters business negotiation information (customer name, business details, date, etc.) from the terminal.

[0092] 2. The device sends the entered data to the server via the API.

[0093] 3. The server stores the received data in the CRM system database.

[0094] 4. The stored data is later used to analyze sales activities and generate reports.

[0095] Example prompt sentence:

[0096] "Please enter the details of your new customer opportunity. Include the customer name, details of the opportunity, and the date."

[0097] In this way, a system is realized in which the server, terminals, and users work together to centrally manage customer information and form a database for analysis.

[0098] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0099] Step 1:

[0100] The user enters customer information from the terminal.

[0101] The user opens a browser on their device and accesses the input form of the CRM system. For example, they enter "Customer name: Company A, Negotiation content: New product proposal, Date: October 1, 2023." The entered data is divided into fields such as customer name, negotiation content, and date.

[0102] Step 2:

[0103] The terminal sends the entered data to the server via the API.

[0104] The terminal converts the input data into JSON format and sends it to the API endpoint using an HTTP POST request, for example, to " / api / v1 / customers." The input data is structured as a JSON object and sent to the API endpoint.

[0105] Step 3:

[0106] The server stores the received data in the CRM system database.

[0107] The server parses the received JSON data and generates a SQL query to insert into the database, for example "INSERT INTO customers (name, deal_content, date) VALUES ('Company A', 'New Product Proposal', '2023-10-01')". The input data is saved in the appropriate table in the database.

[0108] Step 4:

[0109] The server processes the stored data for analysis.

[0110] The server aggregates the stored data and filters it based on certain conditions, for example, by running the query "SELECT COUNT() FROM deals WHERE status = 'success' AND date BETWEEN '2023-01-01' AND '2023-12-31'". The input data is processed for aggregation and filtering, and analytical results are generated.

[0111] Step 5:

[0112] The server provides the analysis results to the user.

[0113] The server converts the analysis results into graphs and charts and displays them on a dashboard. For example, a pie chart showing success rates may be generated and displayed in the user's browser. The input data is processed for visualization and provided to the user.

[0114] In this way, a system is realized in which the server, terminals, and users work together to centrally manage customer information and form a database for analysis.

[0115] (Application example 1)

[0116] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0117] Conventional CRM systems do not centrally manage customer information and interaction information, making it difficult for store staff to check the information they need in real time when responding to customers. Furthermore, there was a lack of a way to effectively utilize customer visit history, purchase history, and customer support response history, making it difficult to improve the quality of customer support.

[0118] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0119] In this invention, the server includes: means for accumulating all sales activity, customer success and customer support response history, contract status, etc. in CRM; means for creating a health score using generative AI; means for extracting groups of companies that meet conditions such as signs of cancellation, time changes, and upselling; means for generating a "customer list to contact today, this week, and this month" for the CSM and drafting emails and content; means for the CSM to check, distribute, and respond; means for setting the timing of the next contact after entering the results; means for checking customer visit history, purchase history, and customer support response history in real time for use by store staff when responding to customers; and means for checking customer information and interaction information in real time to provide better customer service. This allows store staff to check the information they need when responding to customers in real time, improving the quality of customer service. The server includes a means for accumulating information including sales activities to customers, efforts to realize the success of customers who use the company's products and services, customer support response history, progress of contracts with customers, and the contents of contracts with customers in a customer relationship management (CRM) system, a creation means for creating a health score that is an index that quantifies the health status and satisfaction of customers using a generative AI model, an extraction means for extracting companies whose health score is below a certain threshold and companies for which a certain period of time has passed since the most recent contract renewal as a group of companies that meet certain conditions by inputting specific prompt sentences into the generative AI model, and a customer support person who is a person who plays a role in supporting customer success. The system includes: a means for creating a list of customers selected based on specific conditions for a customer success manager (CSM); a means for distributing the list to the customer success manager; a setting unit for setting the next timing of contacting the customer when the customer success manager has checked the distributed list and completed sending materials to the customer; an emotion engine for recognizing the emotions of the customer who is the user communicating with the customer success manager; and a means for quantifying the emotions, wherein the creation means may use a generative AI model to create the health score based on the information stored in the customer relationship management system and the quantified emotions.The server may include means for including in the list customers who are likely to churn and be upsold based on the emotion. The server may include means for displaying the health score on the smartphone.

[0120] "Sales activities" are a series of activities a company undertakes to sell its products and services.

[0121] "Customer Success" is the activity of helping customers get the most out of products and services and achieve success.

[0122] "Customer support response history" is a record of responses to customer inquiries and problems.

[0123] "Contract status" is information indicating the progress and content of a contract with a customer.

[0124] "CRM" stands for Customer Relationship Management System, a system for centrally managing customer information.

[0125] "Generative AI" is a system that uses artificial intelligence to analyze data and generate information for specific purposes.

[0126] A "health score" is a numerical indicator of a customer's health status and satisfaction.

[0127] "Cancelers" are signs that indicate the possibility that a customer will cancel a service.

[0128] "Kihen" is an abbreviation for "functional change" and refers to a change in the functionality of a product or service.

[0129] "Upselling" is a sales technique that offers customers more expensive products or services.

[0130] A "group of companies" is a collection of multiple companies that meet certain conditions.

[0131] "CSM" stands for Customer Success Manager, a person whose role is to support customer success.

[0132] A "customer list" is a list of customers selected based on specific conditions.

[0133] "Drafting" refers to creating a draft of a document or content.

[0134] A "brick and mortar store" is a retail outlet that exists in a physical location.

[0135] "Store visit history" is a record of a customer's visits to a store.

[0136] "Purchase history" is a record of products and services purchased by a customer.

[0137] "Interaction information" is information about interactions and exchanges with customers.

[0138] The system for implementing this invention consists of three main components: a server, a terminal, and a user. The server centrally manages customer information and analyzes the data using a generative AI model. The terminal is a device used by store staff when dealing with customers, such as a smartphone or tablet. The user is a store staff member or a customer success manager (CSM).

[0139] The server has a means of storing all sales activity, customer success and customer support response history, contract status, etc. in CRM. This allows for centralized management of various communication and transaction histories with customers. In addition, it has a means of creating a health score using generative AI and extracting companies that meet conditions such as signs of cancellation, feature changes, and upselling. This makes it possible to quantify customer health and satisfaction and classify customers based on specific conditions.

[0140] The terminals are used by store staff when dealing with customers and provide a means to check customer visit history, purchase history, and customer support response history in real time, allowing store staff to check customer information and interaction information in real time and provide better customer service.

[0141] Users contact customers at the appropriate time based on the "Customer list to contact today, this week, this month" provided by the server. In addition, users can create drafts of emails and content, which are then checked, distributed, and handled by the CSM. After entering the results, there is also a means to set the timing of the next contact.

[0142] As a concrete example, the following prompt sentence can be input to a generative AI model:

[0143] Example prompt sentence:

[0144] "Get the information for the customer whose customer ID is 1."

[0145] Using this prompt, the server retrieves customer information and displays it on the terminal, allowing store staff to check the information they need in real time when assisting customers and improve the quality of their customer service.

[0146] Also, the prompt statement to obtain interaction information for a customer with customer ID 1 is as follows:

[0147] Example prompt sentence:

[0148] "Get interaction information for customer with customer ID 1."

[0149] Using this prompt, the server retrieves interaction information and displays it on the device, allowing store staff to review past interactions with customers and provide more appropriate responses.

[0150] In this way, by linking the server, terminal, and user, it is possible to achieve centralized management of customer information and provide information in real time, thereby improving the quality of customer service.

[0151] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0152] Step 1:

[0153] The server stores customer information such as sales activities, customer success and customer support response history, and contract status in CRM. As input, it receives data on various communications and transactions with customers and stores this in a database. As output, the stored customer information is managed centrally.

[0154] Step 2:

[0155] The server uses a generative AI model to create a health score based on the information stored in the CRM. Customer history data is received as input, and the generative AI model analyzes it. The output is a health score that quantifies the customer's health status and satisfaction.

[0156] Step 3:

[0157] The server extracts companies that meet certain criteria, such as those predicting churn, changing features, or upselling. As input, it receives health scores and other customer data, filters the data based on specific criteria, and generates a list of companies that meet the criteria as output.

[0158] Step 4:

[0159] The server generates a "customer list to contact today, this week, and this month" for the CSM and creates email and content drafts. The server receives the extracted company list and health score as input, and the generative AI model uses this to create the list and draft. The output is a customer list to contact and email content drafts.

[0160] Step 5:

[0161] The user (CSM) checks the customer list and draft provided by the server and makes corrections as necessary. As input, the list and draft provided by the server are received, and the user checks and corrects them. As output, the final customer list and email content are confirmed.

[0162] Step 6:

[0163] The user (CSM) contacts customers based on the confirmed customer list. As input, the confirmed customer list and email content are received, and the user sends this to the customer. As output, contact with the customer is completed.

[0164] Step 7:

[0165] The user (CSM) inputs the contact results into the server and sets the timing of the next contact. The server receives the contact results and the timing of the next contact as input, and stores them in the database. The timing of the next contact is set as output.

[0166] Step 8:

[0167] When a store staff member assists a customer, the terminal checks the customer's visit history, purchase history, and customer support response history in real time. It receives a customer ID as input and retrieves related customer information from the server. As output, the customer information is displayed on the terminal.

[0168] Step 9:

[0169] The terminal displays customer information and interaction information in real time, helping staff to provide better customer service. As input, it receives customer information obtained from the server and staff responds to customers based on this information. As output, the quality of customer service improves.

[0170] Example 2

[0171] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0172] Conventional customer relationship management systems make it difficult to quantitatively evaluate customer contract renewal rates and upselling potential, resulting in insufficient efficiency in sales activities and customer support. Furthermore, the preprocessing and analysis of customer data requires a significant amount of time and effort, making them inadequate in a business environment where rapid response is required. Furthermore, the calculation of health scores using generative artificial intelligence and the generation of customer lists based on them were not automated, placing a heavy burden on customer success managers.

[0173] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0174] In this invention, the server includes means for accumulating all information such as sales activities, customer success and customer support response history, and contract status in a database; means for generating a health score using generative artificial intelligence; means for extracting a group of companies that meet conditions such as signs of cancellation, changes in circumstances, and upselling; means for generating a "customer list to contact today, this week, and this month" for a customer success manager and drafting emails and content; means for the customer success manager to check, distribute, and respond; means for setting the timing of the next contact after entering the results; means for acquiring customer data from the database; means for preprocessing the acquired data; means for generating prompt statements to be input into a generative artificial intelligence model based on the preprocessed data; means for inputting the generated prompt statements into the generative artificial intelligence model and calculating a health score; and means for saving the calculated health score in the database and displaying it for user viewing. This enables quantitative evaluation of a customer's contract renewal probability and the possibility of upselling, thereby improving the efficiency of sales activities and customer support.

[0175] "Sales activities" refers to a series of actions and processes for selling products or services to customers.

[0176] "Customer success" refers to activities that help customers get the most out of products and services and achieve success.

[0177] "Customer support response history" refers to a record of responses to customer inquiries and problems.

[0178] "Contract status" refers to information indicating the status and content of a contract with a customer.

[0179] "Database" refers to a system for efficiently managing and storing data.

[0180] "Generative AI" refers to AI that has the ability to generate new information and predictions based on input data.

[0181] A "health score" is an indicator that quantitatively evaluates a customer's probability of continuing their contract or the possibility of upselling.

[0182] "Cancellation indicators" refer to signs that indicate the possibility that a customer may cancel a contract.

[0183] "Device change" refers to changes in the customer's contract details or usage status.

[0184] "Upselling" refers to selling a customer an additional, more expensive product or service.

[0185] A "Customer Success Manager" is a person whose role is to support customer success and improve customer satisfaction.

[0186] A "prompt sentence" refers to an instruction sentence to be input into generative artificial intelligence.

[0187] "Preprocessing" refers to the process of preparing data in a format suitable for analysis or model input.

[0188] This invention is a customer relationship management system that uses generative artificial intelligence to calculate customer health scores and improve the efficiency of sales activities and customer support. Specific embodiments of this system are described below.

[0189] The server first retrieves customer data from a database. This data includes customer purchase history, support inquiry history, contract status, and so on. A common relational database management system (RDBMS) can be used as the database. Suitable examples include MySQL and PostgreSQL.

[0190] Next, the server preprocesses the acquired data. Preprocessing includes imputing missing values, removing outliers, and normalizing the data. For this, the Python Pandas library can be used. For example, to impute missing values ​​with the mean, use code like "df.fillna(df.mean())".

[0191] Based on the preprocessed data, the server generates a prompt to be input to the generative AI model. The prompt includes information such as customer ID, purchase history, and support inquiry history. As a concrete example, the following prompt is generated:

[0192] "Customer ID: 12345 has made three purchases and two support inquiries in the past six months. Please rate this customer's likelihood of contract renewal and upselling potential."

[0193] Based on the generated prompt text, the server inputs it into a generative AI model. An example of a suitable generative AI model is OpenAI's (registered trademark) GPT-4 (registered trademark). The server inputs the prompt text into the generative AI model and analyzes the customer's behavioral patterns, contract renewal probability, and upselling possibility. The model outputs results such as "contract renewal probability: 85%, upselling possibility: 70%."

[0194] The calculated health scores are stored in the database again. The server inserts the results into the database using an SQL query, for example, "INSERT INTO health_scores (customer_id, retention_probability, upsell_probability) VALUES (12345, 85, 70)".

[0195] Finally, users can view these health scores through the CRM system interface. Based on the health scores, users can prioritize customer responses and develop marketing strategies. The CRM system interface displays the results using HTML and JavaScript (registered trademark).

[0196] In this way, the server utilizes generative artificial intelligence to calculate a health score based on customer data, providing a system that improves the efficiency of sales activities and customer support.

[0197] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0198] Step 1:

[0199] The server retrieves customer data from the database. As input, it accesses the database using an SQL query to extract information such as the customer's purchase history, support inquiry history, and contract status. Specifically, it executes a command such as "SELECT FROM customer_data WHERE date >= '2023-01-01'". As output, it obtains the retrieved customer data.

[0200] Step 2:

[0201] The server preprocesses the acquired data. It uses the customer data acquired in step 1 as input. Specifically, it creates a data frame using Python's Pandas library, imputes missing values ​​using "df.fillna(df.mean())", and removes outliers using "df = df[df['purchase_amount'] < threshold]". The preprocessed data is obtained as output.

[0202] Step 3:

[0203] The server generates a prompt sentence based on the preprocessed data to be input into the generative artificial intelligence model. The data preprocessed in step 2 is used as input. Specifically, the prompt sentence is generated using Python string manipulation. For example, execute code such as "prompt = f'Customer ID: {customer_id} has made {purchase_count} purchases and {support_count} support inquiries over the past six months. Please evaluate this customer's contract renewal probability and upsell potential.'" The generated prompt sentence is obtained as output.

[0204] Step 4:

[0205] The server inputs the generated prompt into a generative AI model. The prompt generated in step 3 is used as input. Specifically, the prompt is input into a generative AI model (for example, OpenAI's GPT-4) to analyze customer behavior patterns, contract renewal probability, and upselling possibilities. For example, a request is sent as "response = openai.Completion.create(engine='gpt-4', prompt=prompt)". The output includes results such as contract renewal probability and upselling possibilities.

[0206] Step 5:

[0207] The server stores the calculated health scores in the database. It uses the results from step 4 as input. It inserts the results into the database using an SQL query. For example, it executes a command like "INSERT INTO health_scores (customer_id, retention_probability, upsell_probability) VALUES (12345, 85, 70)". The output is the health scores stored in the database.

[0208] Step 6:

[0209] Users view health scores through the CRM system interface. The input is the health scores stored in the database. The specific operation is to display the results using HTML or JavaScript. Users can use these health scores to prioritize customer interactions and develop marketing strategies. The output is a health score that users can view.

[0210] (Application example 2)

[0211] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0212] Conventional CRM systems lacked indicators for quantitatively evaluating customer contract renewal probability and upselling potential, resulting in reduced efficiency in sales activities and customer support. It was also difficult to provide personalized product recommendations and promotions based on customer purchase and browsing history.

[0213] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for storing all information such as sales activities, customer success and customer support response history, and contract status in CRM; means for creating a health score using generative AI; means for extracting a group of companies that meet conditions such as signs of cancellation, time changes, and upselling; means for generating a "customer list to contact today, this week, and this month" for the CSM and drafting emails and content; means for the CSM to check, distribute, and respond; means for setting the timing of the next contact after entering the results; means for collecting data such as the user's purchase history, browsing history, and products added to the cart; means for calculating each user's health score using generative AI; and means for providing personalized product recommendations and promotions based on the health score. This makes it possible to quantitatively evaluate a customer's contract renewal probability and upselling potential and provide personalized services.

[0214] "Sales activities" are a series of activities that a company undertakes to sell its products and services.

[0215] "Customer Success" is the activity of helping customers get the most out of a product or service.

[0216] "Customer support response history" is a record of responses to customer inquiries and problems.

[0217] "Contract status" is information indicating the progress and content of a contract with a customer.

[0218] "CRM" stands for Customer Relationship Management System, a system for managing relationships with customers.

[0219] "Generative AI" is artificial intelligence that generates new information and predictions based on data.

[0220] A "health score" is an indicator that quantitatively evaluates a customer's probability of contract renewal and the possibility of upselling.

[0221] "Cancellation indicators" are signs that indicate the possibility that a customer may cancel a contract.

[0222] "Kihen" is an abbreviation for "function change" and refers to a change in the functions of a system or service.

[0223] "Upselling" is a sales technique that offers customers more expensive products or services.

[0224] A "group of companies" is a collection of multiple companies that meet certain conditions.

[0225] "CSM" stands for Customer Success Manager, a person whose role is to support customer success.

[0226] "Purchase history" is a record of products that a customer has purchased in the past.

[0227] "Browse history" is a record of products that a customer has browsed in the past.

[0228] "Cart Items" is a record of items that a customer has added to their online shopping cart.

[0229] "Personalized product recommendations" are products suggested based on a customer's individual preferences and behavior.

[0230] "Promotion" refers to activities or campaigns aimed at promoting the sale of a product or service.

[0231] A system for implementing the present invention has the following configuration.

[0232] First, the server has a means to store all sales activities, customer success and customer support response history, contract status, etc. in CRM. This allows various data related to customers to be managed centrally.

[0233] Next, the server has a means to create a health score using generative AI, which generates a health score that quantitatively evaluates the probability of a customer continuing their contract or the possibility of upselling based on the information accumulated in the CRM.

[0234] Furthermore, the server has a means for extracting a group of companies that meet certain conditions, such as signs of cancellation, time changes, upselling, etc. This means allows a group of companies to be extracted based on specific conditions, and target customers to be identified.

[0235] The server also has a means for generating a "list of customers to contact today, this week, and this month" for the CSM and drafting emails and content, which allows the CSM to efficiently respond to customers.

[0236] Additionally, the server provides a means for the CSM to review, distribute, and respond, allowing the CSM to review the generated lists and drafts and contact customers at the appropriate time.

[0237] Next, the server has a means for setting the timing of the next contact after the result is input, which automatically sets the timing of the next contact based on the result of the customer interaction.

[0238] The server also has a means for collecting data such as the user's purchase history, browsing history, products added to the cart, etc. This allows the collection of user behavior data, which becomes the basic data for analysis.

[0239] Furthermore, the server has a means for calculating a health score for each user using generative AI, which generates a personalized health score for each user.

[0240] Finally, the server has a means for providing personalized product recommendations and promotions based on the health score, thereby suggesting optimal products and promotions to the user.

[0241] For example, it can recommend products that users are likely to purchase next based on their past purchases and browsing history, and it can offer special promotions to users with low health scores to encourage them to continue using the service.

[0242] An example of a prompt is as follows:

[0243] "User purchase history: Product A, Product B, browsing history: Product C, Product D, products added to cart: Product E. Please generate a health score for this user."

[0244] In this way, the server can use generative AI to calculate a health score based on customer behavioral data and provide personalized services.

[0245] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0246] Step 1:

[0247] The server accumulates data such as sales activities, customer success and customer support response history, and contract status in CRM. As input, various customer data is provided and saved in the CRM system. As output, the accumulated data is recorded in CRM.

[0248] Step 2:

[0249] The server uses generative AI to create health scores based on data stored in the CRM. Customer data stored in the CRM is provided as input, which the generative AI model analyzes. A health score for each customer is generated as output.

[0250] Step 3:

[0251] The server extracts companies that meet certain criteria, such as churn predictions, changes in behavior, and upselling. The generated health scores and other criteria are provided as input, and a database query is executed. The output is a list of companies that meet the criteria.

[0252] Step 4:

[0253] The server generates a "customer list to contact today, this week, and this month" for the CSM and creates email and content drafts. As input, the extracted company list and health score are provided, and email and content drafts are generated based on templates. As output, a customer list to contact and drafts are generated.

[0254] Step 5:

[0255] The CSM checks the lists and drafts generated by the server and contacts the customer at the appropriate time. As input, the CSM checks the lists and drafts provided by the server. As output, contact is made to the customer.

[0256] Step 6:

[0257] The server sets the timing of the next contact based on the results entered by the CSM. As input, the contact results entered by the CSM are provided and the timing of the next contact is calculated. As output, the timing of the next contact is set.

[0258] Step 7:

[0259] The server collects data such as user purchase history, browsing history, and items added to the cart. As input, user behavior data is provided and stored in a database. As output, the collected data is recorded.

[0260] Step 8:

[0261] The server uses generative AI to calculate each user's health score. Collected user behavior data is provided as input, which the generative AI model analyzes. The output is a health score for each user.

[0262] Step 9:

[0263] The server provides personalized product recommendations and promotions based on the health score. As input, the generated health score is provided and analyzed by a recommendation algorithm. As output, personalized product recommendations and promotions are generated.

[0264] Specifically, the system recommends products that users are likely to purchase next based on their past purchases and browsing history. It also offers special promotions to users with low health scores to encourage them to continue their subscriptions.

[0265] An example of a prompt is as follows:

[0266] "User purchase history: Product A, Product B, browsing history: Product C, Product D, products added to cart: Product E. Please generate a health score for this user."

[0267] Example 3

[0268] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0269] With traditional customer relationship management systems, it was difficult to properly identify and efficiently respond to a company's signs of cancellation and upselling opportunities. Customer success managers also had to manually create customer lists and consider how to contact them, which was time-consuming and labor-intensive. This reduced the efficiency of customer support and potentially led to missed revenue opportunities for the company.

[0270] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0271] In this invention, the server includes means for accumulating all sales activities, customer success and customer support response history, contract status, etc. in a customer relationship management system, means for creating a health score using generative artificial intelligence, means for extracting a group of companies that meet conditions such as signs of churn, changes in circumstances, and upselling, means for generating a "customer list to contact today, this week, and this month" for the customer success manager and drafting emails and content, means for the customer success manager to check, distribute, and respond, means for setting the timing of the next contact after entering the results, means for collecting data from a database management system, means for analyzing the data using a generative artificial intelligence model, means for executing a filtering function to extract a group of companies that meet specific conditions, means for generating email drafts using templates, and means for automatically setting the timing of the next contact. This enables appropriate identification of companies' signs of churn and upselling opportunities and efficient responses.

[0272] "Sales activities" are a series of activities that a company undertakes to provide products and services to customers.

[0273] "Customer Success" is the activity of helping customers get the most out of products and services and achieve success.

[0274] "Customer support response history" is a record of responses to customer inquiries and problems.

[0275] "Contract status" is information indicating the status and content of the contract concluded between the company and the customer.

[0276] A "customer relationship management system" is a system for managing relationships with customers and efficiently conducting sales activities and customer support.

[0277] "Generative AI" is an AI technology that makes predictions and analyses based on data.

[0278] A "health score" is a numerical indicator of a customer's health status and satisfaction.

[0279] "Cancellation indicators" are signs that indicate the possibility that a customer may cancel a contract.

[0280] "Change in contract" refers to a customer changing the terms of their current contract.

[0281] "Upselling" refers to offering a customer a more expensive product or service.

[0282] A "Customer Success Manager" is a person whose role is to support customer success and manage customer relationships.

[0283] A "database management system" is a system for efficiently managing data and quickly obtaining necessary information.

[0284] A "generative artificial intelligence model" is a specific implementation of artificial intelligence that makes predictions and analyses based on data.

[0285] The "filtering function" is a function that selects data based on specific conditions.

[0286] A "template" is a template used when creating emails or content.

[0287] "Next contact timing" refers to the next time you should contact the customer.

[0288] This invention is a system for accurately identifying signs of cancellation and upselling opportunities for a company and responding efficiently. This system includes elements of a server, a terminal, and a user, and operates as follows.

[0289] Hardware and software used

[0290] Hardware: Servers, terminals

[0291] Software: Generative AI models, database management systems (e.g., MySQL), email sending systems (e.g., SendGrid)

[0292] Data collection and analysis

[0293] The server collects data such as the company's sales activities, customer success and customer support response history, and contract status from a database management system. The server periodically accesses the database to obtain the latest data. Specifically, the server executes SQL queries to extract the necessary data and loads it into memory.

[0294] Next, the server uses the collected data to analyze the company's signs of churn and the possibility of upselling using a generative AI model. The server inputs the data into the generative AI model and obtains the prediction results. Specifically, the server runs a Python script and calls the generative AI model (e.g., TENSORFLOW (registered trademark) or PyTorch) to make predictions.

[0295] Extraction of company groups

[0296] The server executes a filtering function that automatically extracts a group of companies that meet specific conditions based on the analysis results. The server filters the data based on the conditions and generates a list of target companies. Specifically, the server lists the IDs of companies that meet the conditions and saves the list in a database.

[0297] Contact list and content generation

[0298] The server then plans appropriate timing and content for contacting the extracted group of companies. Based on the plan, the server generates a "customer list to contact today, this week, and this month" and creates draft emails and content. Specifically, the server uses templates to generate email drafts and saves them in a database along with the list.

[0299] CSM Delivery and Automation

[0300] Using a terminal, the user (customer success manager) checks the generated contact list and content draft and distributes them as necessary. After entering the results, the user uses the function to automatically set the timing of the next contact. Specifically, the user checks the list through the terminal interface and clicks the send email button. After sending, the server automatically sets the timing of the next contact.

[0301] Specific examples

[0302] Example of extracting a group of companies:

[0303] Extract companies with a health score of 50 or less.

[0304] Extract companies that have been in business for more than 90 days since their most recent contract renewal.

[0305] Example prompt sentence:

[0306] List companies with a health score of 50 or below.

[0307] Please list companies whose most recent contract renewal date is more than 90 days ago.

[0308] Generate a list of customers to contact today.

[0309] Generate a list of customers to contact this week.

[0310] Generate a list of customers to contact this month.

[0311] This system allows customer success managers to efficiently handle customer inquiries and to detect signs of potential cancellations and upsell opportunities without missing any. The flow of the identification process in the third embodiment will be described with reference to FIG. 15.

[0312] Step 1:

[0313] The server collects data such as the company's sales activities, customer success and customer support response history, and contract status from a database management system (e.g., MySQL). The server periodically accesses the database to obtain the latest data. Specifically, the server executes SQL queries to extract the necessary data and loads it into memory.

[0314] Input: Company data stored in a database management system

[0315] Output: Company data loaded into memory

[0316] Step 2:

[0317] The server uses a generative AI model based on the collected data to analyze the company's signs of churn and the possibility of upselling. The server inputs the data into the generative AI model and obtains the prediction results. Specifically, the server runs a Python script and calls the generative AI model (e.g., TensorFlow or PyTorch) to make predictions.

[0318] Input: Company data loaded into memory

[0319] Output: Predictions made by the generative AI model

[0320] Step 3:

[0321] The server executes a filtering function that automatically extracts a group of companies that meet specific conditions based on the analysis results. The server filters the data based on the conditions and generates a list of target companies. Specifically, the server lists the IDs of companies that meet the conditions and saves the list in a database.

[0322] Input: Prediction results from a generative AI model

[0323] Output: A list of companies that match the criteria

[0324] Step 4:

[0325] The server then plans appropriate timing and content for contacting the extracted group of companies. Based on the plan, the server generates a "customer list to contact today, this week, and this month" and creates draft emails and content. Specifically, the server uses templates to generate email drafts and saves them in a database along with the list.

[0326] Input: A list of companies that match the criteria

[0327] Output: Contact list and email draft

[0328] Step 5:

[0329] Using a terminal, the user (customer success manager) checks the generated contact list and content draft and distributes them as necessary. After entering the results, the user uses the function to automatically set the timing of the next contact. Specifically, the user checks the list through the terminal interface and clicks the send email button. After sending, the server automatically sets the timing of the next contact.

[0330] Input: Contact list and email drafts

[0331] Output: Email sent and next contact timing setting

[0332] (Application example 3)

[0333] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0334] With conventional customer management systems, it was difficult to accurately predict customer churn and upsell opportunities, making it difficult to contact customers at the right time. Furthermore, creating customer lists to contact and drafting emails was a time-consuming and labor-intensive process.

[0335] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes: means for storing all data such as sales activities, customer success and customer support response history, and contract status in CRM; means for creating a health score using generative AI; means for extracting a group of companies that meet conditions such as signs of churn, time changes, and upselling; means for generating a "customer list to contact today, this week, and this month" for the CSM and drafting emails and content; means for the CSM to check, distribute, and respond; means for setting the timing of the next contact after entering the results; means for predicting signs of churn based on customer data and generating a list of customers to contact; and means for automatically drafting emails and sending emails using an SMTP server. This makes it possible to accurately predict signs of churn and upselling opportunities and contact customers at the appropriate time.

[0336] "Sales activities" are a series of activities that a company undertakes to sell its products and services.

[0337] "Customer Success" is the activity of helping customers get the most out of products and services and achieve success.

[0338] "Customer support response history" is a record of responses to customer inquiries and problems.

[0339] "Contract status" is information indicating the state of the contract with the customer and the status of its fulfillment.

[0340] "CRM" stands for Customer Relationship Management System, a software for managing relationships with customers.

[0341] "Generative AI" is artificial intelligence that generates new information and predictions based on data.

[0342] A "health score" is a numerical indicator of a customer's health status and satisfaction.

[0343] "Cancellation indicators" are signs that indicate the possibility that a customer may cancel a contract.

[0344] "Kihen" is an abbreviation for "functional change" and refers to a change in the functionality of a product or service.

[0345] "Upselling" is a sales technique that offers customers more expensive products or additional services.

[0346] A "group of companies" is a collection of multiple companies that meet certain conditions.

[0347] "CSM" stands for Customer Success Manager, a person whose role is to support customer success.

[0348] A "customer list" is a list of customers who need to be contacted or dealt with.

[0349] "Email and Content Draft" means a draft of an email or content to be sent to a customer.

[0350] An "SMTP server" is a server for sending emails.

[0351] The system for implementing this invention has the following configuration. First, the server stores all data, such as sales activities, customer success and customer support response history, and contract status, in CRM. Next, generative AI is used to create a health score based on the information stored in CRM. A group of companies that meet certain conditions is extracted, such as companies with a health score below a certain threshold or companies that have been in the business for a certain period of time since their most recent contract renewal.

[0352] Furthermore, the server generates a "list of customers to contact today, this week, and this month" for the CSM and automatically creates draft emails and content. The CSM checks these, distributes them, and responds. After entering the results, the timing of the next contact is also automatically set.

[0353] Specifically, the server predicts signs of churn based on customer data and generates a list of customers to contact. It automatically creates email drafts and sends them using an SMTP server. This makes it possible to accurately predict signs of customer churn and upselling opportunities, and contact customers at the appropriate time.

[0354] The hardware used is a server or PC for data processing and email sending. The software used is Python, pandas, scikit-learn, and smtplib. These software are used to read data, calculate health scores, generate customer lists, draft emails, and send emails.

[0355] For example, customer data may include information such as:

[0356] Customer name

[0357] Purchase frequency (purchase_frequency)

[0358] Average Order Value (average_order_value)

[0359] Last purchase date (last_purchase_date)

[0360] Last contact date (last_contact_date)

[0361] Email address

[0362] Example prompts to input to a generative AI model:

[0363] Write a Python program that uses customer data to predict which customers are likely to churn and generate a list of customers to contact. The program should include the following steps:

[0364] 1. Loading customer data

[0365] 2. Calculating your Health Score

[0366] 3. Identifying customers who are at risk of churn

[0367] 4. Create a contact list

[0368] 5. Drafting an email

[0369] 6. Sending emails

[0370] In this way, a system is provided that enables the operator of a mail-order site to predict signs of customer cancellation and contact the customer at the appropriate time.

[0371] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0372] Step 1:

[0373] The server stores data such as sales activities, customer success and customer support response history, and contract status in CRM. The input is historical data on various sales activities and customer support, and the output is data stored in CRM. Specifically, the data entered by sales representatives and customer support representatives is automatically saved in CRM.

[0374] Step 2:

[0375] The server uses generative AI to create health scores based on data stored in the CRM. The input is customer data stored in the CRM, and the output is each customer's health score. Specifically, it reads the data using pandas and calculates the health score using a random forest model using scikit-learn.

[0376] Step 3:

[0377] The server extracts companies whose health scores are below a certain threshold or whose most recent contract renewal date has been a certain amount of time. The input is the health score and contract renewal date, and the output is a list of companies that are likely to cancel. Specifically, it filters out companies whose health scores are below a threshold and lists companies whose contract renewal date has been longer than a certain amount of time.

[0378] Step 4:

[0379] The server generates a "list of customers to contact today, this week, and this month" for the CSM, and automatically creates emails and content drafts. The input is a list of companies that are at risk of churn, and the output is a list of customers to contact and email drafts. Specifically, it sets the priority of contact based on the list of companies that are at risk of churn, and creates drafts using email templates.

[0380] Step 5:

[0381] The CSM checks the customer list and email draft generated by the server, makes any necessary corrections, and delivers them. The input is the customer list and email draft generated by the server, and the output is the corrected email draft and delivery results. Specifically, the CSM checks the email draft, corrects the content as necessary, and sends the email using the SMTP server.

[0382] Step 6:

[0383] The server automatically sets the timing of the next contact based on the results entered by the CSM. The input is the distribution result entered by the CSM, and the output is the timing of the next contact. Specifically, the server analyzes the distribution result and calculates and sets the timing of the next required contact.

[0384] In this way, the server can accurately predict customer churn and upsell opportunities, and contact customers at the right time.

[0385] Furthermore, an emotion engine that estimates the user's emotion (customer's emotion) may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0386] "Example 1"

[0387] One embodiment of the present invention is a system that incorporates an emotion engine. This system recognizes a user's emotions and reflects the results in the calculation of a health score. Specifically, the system extracts the user's emotions from communication with the user (e.g., phone calls, emails, chats, etc.) and quantifies those emotions. This quantified emotion data is used to calculate the health score.

[0388] "Example 2"

[0389] Furthermore, the emotion recognition results from the emotion engine are also used to generate a "customer list to contact today, this week, this month" for the CSM. Specifically, if a user is judged to have a high probability of canceling or upselling based on their emotion data, that user will be included in the list with priority. This allows the CSM to respond efficiently while taking into account the user's emotions. "Example 3"

[0390] Furthermore, the emotion engine also has the ability to automatically set the next timing of contact based on the user's emotional data. For example, if it determines that the user's emotions are unstable, it will set the next timing of contact to allow for an earlier follow-up. This increases user satisfaction and reduces the risk of cancellation.

[0391] The processing flow of each embodiment will be described below.

[0392] "Example 1"

[0393] Step 1: Extract user emotions from communication with the user (e.g., phone calls, emails, chats, etc.). User emotions can be interpreted as the emotions of customers communicating with the CSM.

[0394] Step 2: Quantify the extracted emotions.

[0395] Step 3: Use the quantified emotional data to calculate a health score.

[0396] "Example 2"

[0397] Step 1: Based on user sentiment data, determine whether the user is likely to churn or upsell.

[0398] Step 2: Based on the results of the judgment, the user is prioritized for inclusion in the "Customer Contact List for Today, This Week, and This Month."

[0399] "Example 3"

[0400] Step 1: Automatically set the next contact timing based on the user's emotional data.

[0401] Step 2: For example, if the user's emotions are determined to be unstable, the next contact timing is set to allow for early follow-up.

[0402] Example 1

[0403] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0404] With conventional customer relationship management systems, it was difficult to centrally manage information such as sales activities, customer support response history, and contract status, making it difficult to accurately evaluate customer satisfaction and risk. Furthermore, because it was not possible to grasp user sentiment in real time and calculate a health score based on that, it was often the case that signs of cancellation and upselling opportunities were overlooked. This created challenges for improving customer response efficiency and customer satisfaction.

[0405] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0406] In this invention, the server includes: means for storing all sales activities, customer success and customer support response history, contract status, etc. in a customer relationship management system; means for creating a health score using generative artificial intelligence; means for extracting a group of companies that meet conditions such as signs of cancellation, changes in circumstances, and upselling; means for generating a "customer list to contact today, this week, and this month" for the customer success manager and drafting emails and content; means for the customer success manager to check, distribute, and respond; means for setting the timing of the next contact after entering the results; means for extracting and quantifying emotions from communication data with the user; and means for calculating a health score based on the quantified emotion data. This enables centralized management of customer information and calculation of a health score based on the user's emotions.

[0407] "Sales activities" are a series of actions that a company takes to promote sales and conduct business with customers.

[0408] "Customer success" is the process of helping customers get the most out of products and services and achieve success.

[0409] "Customer support response history" is a record of responses to customer inquiries and problems.

[0410] "Contract status" is information indicating the content and progress of the contract with the customer.

[0411] A "customer relationship management system" is an information system for managing relationships with customers and streamlining sales activities and support responses.

[0412] "Generative AI" is AI that has the ability to generate new information and predictions based on data.

[0413] A "health score" is an indicator used to evaluate customer satisfaction and risk.

[0414] "Cancellation indicators" are signs that indicate the possibility that a customer may cancel a contract.

[0415] "Change in contract" refers to a change made by the customer to the contents of the contract.

[0416] "Upselling" is a sales technique that offers customers more expensive products or services.

[0417] A "Customer Success Manager" is a person responsible for supporting customer success and improving customer satisfaction.

[0418] "Extracting emotions" means identifying and extracting emotions from user communication data.

[0419] "Quantifying" means expressing the extracted emotions numerically.

[0420] This invention is a system that centrally manages customer information such as sales activities, customer success and customer support response history, and contract status, and calculates a health score based on the user's emotions. This system is realized by combining a customer relationship management (CRM) system and generative artificial intelligence (AI).

[0421] System configuration

[0422] The system consists of the following major components:

[0423] 1. Terminal: A device used by sales or customer support representatives that provides an interface for entering customer information.

[0424] 2. Server: Collects, converts, and stores customer information, extracts and quantifies emotional data, and calculates health scores.

[0425] 3. CRM system: Provides a database and interface for centrally managing customer information.

[0426] 4. Generative AI model: An artificial intelligence model that analyzes user emotions and calculates a health score.

[0427] Hardware and software used

[0428] Device: A device such as a computer, tablet, or smartphone.

[0429] Server: A server with high-performance computing power (e.g., a cloud server).

[0430] CRM system: A customer relationship management system such as Salesforce or HubSpot.

[0431] Generative AI models: Natural language processing models such as BERT and GPT-3 (registered trademark).

[0432] Voice recognition software: such as Google® Speech-to-Text.

[0433] Data processing and calculation

[0434] 1. Collection of Customer Information:

[0435] The terminal collects data on sales activities, customer support response history, contract status, etc. For example, a sales representative inputs visit records and business negotiation details, while a customer support representative inputs response history.

[0436] 2. Data conversion and storage:

[0437] The server receives the JSON formatted data sent from the terminal, converts it into an SQL query, and stores it in the CRM system database.

[0438] 3. Extracting emotion data:

[0439] The server collects communication data such as phone calls, emails, and chats with users and converts it into text data using voice recognition software.

[0440] 4. Quantifying Emotional Data:

[0441] The server inputs the extracted text data into a sentiment analysis model and quantifies the type and intensity of emotions.

[0442] 5. Calculating your Health Score:

[0443] The server calculates the user's health score based on the quantified emotional data and stores it in the CRM system.

[0444] Specific examples

[0445] For example, when a user calls customer support, the following specific actions are taken:

[0446] 1. The user calls customer support.

[0447] 2. The server converts the call into text data using speech recognition software.

[0448] 3. The server inputs the converted text data into a sentiment analysis model, recognizes the emotion as "anger," and quantifies its intensity as "75 / 100."

[0449] 4. The server stores the quantified emotion data in the CRM system.

[0450] 5. The server calculates the user's health score based on the stored emotional data and stores it in the CRM system.

[0451] Prompt Sentence Examples

[0452] Examples of prompts to be input to a generative AI model include:

[0453] The following is a transcript of a conversation with a user. Please extract the user's emotions from this transcript and quantify them.

[0454] Caller response: "I'm very unhappy with your recent service. It's been too slow."

[0455] Expected Output: Emotion: Anger, Intensity: 75 / 100

[0456] In this way, the system recognizes the user's emotions and calculates a health score based on the data accumulated in the CRM.

[0457] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0458] Step 1:

[0459] The terminal collects customer information such as sales activities, customer support response history, and contract status. Sales representatives and customer support representatives enter data using a CRM application as input. Specifically, the terminal collects information such as the date and time of visit, the location of the visit, the details of the business negotiation, the date and time of support response, the response details, the response results, the contract start date, the contract end date, and the contract details. As output, this data is sent to the server in JSON format.

[0460] Step 2:

[0461] The server receives customer information data in JSON format sent from the terminal. As input, it receives the JSON data sent from the terminal. Specifically, the server converts the received data into an SQL query and inserts it into the CRM system database. As output, the converted data is saved in the CRM system.

[0462] Step 3:

[0463] Users communicate with customer support via phone, email, chat, etc. The user's call content and text data are sent to the server as input. Specifically, the server uses voice recognition software to convert the call content into text data and directly collects email and chat text data. Text data is generated as output.

[0464] Step 4:

[0465] The server inputs the extracted text data into a sentiment analysis model to quantify the emotion. The text data generated in step 3 is input into the sentiment analysis model. Specifically, the server uses a natural language processing model such as BERT or GPT-3 to obtain the type of emotion (joy, anger, sadness, etc.) and its intensity as a numerical value. The output is quantified emotion data.

[0466] Step 5:

[0467] The server calculates the user's health score based on the quantified emotion data. As input, it uses the quantified emotion data generated in step 4. Specifically, the server stores the quantified emotion data in the CRM system and runs an algorithm to calculate a health score based on the stored data. As output, the calculated health score is stored in the CRM system.

[0468] Step 6:

[0469] The server extracts a group of companies that meet certain conditions, such as signs of churn, changes in behavior, and upselling. As input, it uses customer information and health scores stored in the CRM system. Specifically, the server filters the group of companies based on specific conditions and extracts companies that are likely to be signs of churn or upselling. As output, a list of the extracted group of companies is generated.

[0470] Step 7:

[0471] The server generates a "list of customers to contact today, this week, and this month" for the customer success manager and creates email and content drafts. As input, it uses the list of companies generated in step 6 and the customer information stored in the CRM system. Specifically, the server uses a generative AI model to create a list of customers to contact and draft emails and content for them. As output, a customer list and drafts are generated.

[0472] Step 8:

[0473] The customer success manager reviews the generated customer list and draft, makes any necessary corrections, and distributes them to the customer. The customer list and draft generated in step 7 are used as input. Specifically, the customer success manager reviews the list and draft, and distributes them to the customer after making any necessary corrections. The output is the email or content that is distributed.

[0474] Step 9:

[0475] The customer success manager inputs the customer's response and results and sets the timing of the next contact. As input, the customer's response and results are entered into the CRM system. As a specific operation, the customer success manager inputs the results and sets the timing of the next contact. As output, the timing of the next contact is saved in the CRM system.

[0476] (Application example 1)

[0477] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0478] Conventional CRM systems accumulate data such as customer communication history and contract status, but are unable to recognize customer emotions and reflect them in health scores. This makes it difficult to grasp customer satisfaction and dissatisfaction in real time, making it difficult to follow up at the appropriate time. Furthermore, because analysis based on emotion data is not performed, there is a lack of specific feedback to improve the quality of customer support.

[0479] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes: means for storing all data, such as sales activities, customer success and customer support response history, and contract status, in a CRM; means for creating a health score using generative AI; means for extracting a group of companies that meet conditions such as signs of cancellation, changes in customer behavior, and upselling; means for generating a "customer list to contact today, this week, and this month" for the CSM and drafting emails and content; means for the CSM to check, distribute, and respond; means for setting the timing of the next contact after entering the results; means for recognizing emotions from communication with the user and storing the emotion data in the CRM; means for calculating a health score based on the emotion data; means for quantifying the emotion data and sending it to the CRM; and means for calculating a health score based on customer data obtained from the CRM. This enables real-time understanding of customer emotions and appropriate follow-up.

[0480] "Sales activities" are a series of activities a company undertakes to sell its products and services.

[0481] "Customer Success" is the support provided to help customers get the most out of products and services and achieve success.

[0482] "Customer support response history" is a record of responses to customer inquiries and problems.

[0483] "Contract status" is information indicating the progress and content of a contract with a customer.

[0484] "CRM" stands for Customer Relationship Management System, a system for managing relationships with customers.

[0485] "Generative AI" is artificial intelligence that generates new information and predictions based on data.

[0486] A "health score" is a numerical indicator of customer satisfaction and health status.

[0487] "Cancellation warning signs" are signs that indicate the possibility that a customer may cancel a service or contract.

[0488] "Kihen" is an abbreviation for "functional change" and refers to a change in the functionality of a product or service.

[0489] "Upselling" is a sales technique that offers customers more expensive products or services.

[0490] A "group of companies" is a collection of multiple companies that meet certain conditions.

[0491] "CSM" stands for Customer Success Manager, a person whose role is to support customer success.

[0492] "Emotion recognition" is a technology that analyzes and recognizes a user's emotions.

[0493] "Emotion data" is data that quantifies the user's emotions.

[0494] "Follow-up" refers to any additional actions or contact you take to maintain and strengthen your relationship with a customer.

[0495] The system for implementing this invention has the following configuration. The server has a means for storing all sales activities, customer success and customer support response history, contract status, etc. in CRM. This allows for centralized management of various communications and transaction histories with customers.

[0496] Next, the server has a means to create a health score using generative AI. The generative AI calculates the health score based on the information and emotional data accumulated in the CRM. The emotional data is acquired by recognizing emotions from communication with the user, converting the emotional data into numerical values, and sending them to the CRM.

[0497] Furthermore, the server has a means to extract a group of companies that meet certain conditions, such as signs of cancellation, feature changes, upselling, etc. This makes it possible to classify customers based on specific conditions and provide appropriate responses.

[0498] For CSMs, a means is provided to generate a "list of customers to contact today, this week, and this month" and draft emails and content. CSMs can then review these and distribute or respond to them. After entering the results, a means is also provided to set the timing of the next contact.

[0499] A concrete example of this system is an "emotion-aware customer support app" that is installed on smartphones. This app recognizes users' emotions through chat, phone, and email interactions with the user, and stores that data in CRM. Furthermore, it calculates the user's health score based on the emotional data and provides feedback to improve the quality of customer support.

[0500] The hardware used includes a smartphone. The software used is Python, an emotion recognition library (EmotionRecognizer), and a CRM API library (CRM_API). The emotion recognition engine receives the user's text data as input, recognizes their emotions, and quantifies the results. The quantified emotion data is sent to the CRM system via the CRM API. The CRM system calculates a health score based on the accumulated data and provides information for appropriate follow-up.

[0501] For example, if a user sends a chat message saying, "There was a problem with my recent order," this message is input into an emotion recognition engine, which recognizes the user's emotion as "dissatisfied." This emotion is quantified and stored in the CRM. Based on the accumulated data, the user's health score is calculated and feedback is provided to improve the quality of customer support.

[0502] An example of a prompt is as follows:

[0503] "Please input the user's chat message into the emotion recognition engine and recognize the emotion. Then, convert the recognized emotion into a numerical value and send it to the CRM. Then, calculate a health score based on the customer data obtained from the CRM."

[0504] The above is an embodiment of the present invention.

[0505] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0506] Step 1:

[0507] A user accesses a customer support app on their smartphone and initiates a chat, email, or phone call. Input includes the user's text message and voice data, which is sent to an emotion recognition engine.

[0508] Step 2:

[0509] The server uses an emotion recognition engine (EmotionRecognizer) to recognize emotions from the user's text messages and voice data. The input includes the user's text messages and voice data. The emotion recognition engine uses natural language processing technology to analyze emotions and quantify them. The output is the recognized emotions and their quantified data.

[0510] Step 3:

[0511] The server sends the quantified emotion data to the CRM system through the CRM API. The input contains the quantified emotion data. The CRM system stores the received emotion data in the customer profile. The output is an updated customer profile.

[0512] Step 4:

[0513] The server uses generative AI to calculate a health score based on customer data and emotional data stored in the CRM. The input includes customer data and emotional data stored in the CRM. The generative AI analyzes this data and calculates the customer's health score. The output is the calculated health score.

[0514] Step 5:

[0515] The server extracts a group of companies that meet certain conditions, such as signs of cancellation, feature changes, and upselling. The input includes customer data and health scores stored in the CRM. The server uses this data to classify and extract companies based on specific conditions. The output is a list of the extracted companies.

[0516] Step 6:

[0517] The server generates a "list of customers to contact today, this week, and this month" for the CSM and drafts emails and content. The input includes customer data and health scores stored in the CRM. The server generates a list of customers to contact based on this data and drafts emails and content. The output is the generated customer list and drafts.

[0518] Step 7:

[0519] The CSM checks the generated customer list and draft, makes any necessary corrections, and distributes and responds to them. The input includes the generated customer list and draft. The CSM contacts the customer based on these and responds to them. The output is the results of contacting and responding to the customer.

[0520] Step 8:

[0521] The CSM inputs the response results into the CRM system and sets the timing of the next contact. The inputs include the response results and the timing of the next contact. The CRM system updates the customer profile based on this data and sets the timing of the next contact. The output is the updated customer profile and the timing of the next contact.

[0522] Example 2

[0523] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0524] With traditional customer management systems, it is difficult to quantitatively evaluate a customer's contract renewal probability or the possibility of upselling, leaving customer success managers with a lack of information to respond efficiently. Furthermore, because they are unable to take user sentiment data into account, appropriate responses to customers who are likely to cancel or upsell can be delayed. This can lead to lower customer satisfaction and lost business opportunities.

[0525] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0526] In this invention, the server includes a means for accumulating all sales activity, customer success and customer support response history, contract status, etc. in a customer management system; a means for creating a health score using generative artificial intelligence; a means for extracting groups of companies that meet conditions such as signs of churn, changes in behavior, and upselling; a means for generating a "customer list to contact today, this week, and this month" for the customer success manager and drafting emails and content; a means for the customer success manager to review, distribute, and respond; a means for setting the timing of the next contact after entering the results; and a means for analyzing user emotion data using an emotion engine to prioritize users who are likely to churn or upsell. This allows for quantitative evaluation of customer contract renewal probability and upsell potential, enabling the customer success manager to respond efficiently. Furthermore, by taking user emotion data into consideration, appropriate responses can be made quickly to customers who are likely to churn or upsell.

[0527] "Sales activities" are a series of activities that a company undertakes to sell products and services to customers.

[0528] "Customer Success" is the activity of helping customers get the most out of products and services and achieve success.

[0529] "Customer support response history" is a record of responses to customer inquiries and problems.

[0530] "Contract status" is information indicating the status and content of the contract concluded between the customer and the company.

[0531] A "customer management system" is a system that centrally manages information about customers and supports sales activities and customer support.

[0532] "Generative AI" is an artificial intelligence technology that generates new information and predictions based on data.

[0533] A "health score" is an indicator used to quantitatively evaluate a customer's contract renewal probability and upselling potential.

[0534] "Cancellation indicators" are signs that indicate the possibility that a customer may cancel a contract.

[0535] "Changes" refer to changes in the customer's situation or behavior.

[0536] "Upselling" is a sales technique that offers customers more expensive products or additional services.

[0537] A "Customer Success Manager" is a person who works to support customer success and improve customer satisfaction.

[0538] An "emotion engine" is a technology for analyzing a user's emotional data and evaluating their emotional state.

[0539] "User emotion data" refers to information about emotions obtained from the content of a user's emails and chats, social media posts, etc.

[0540] A "customer list" is a list of customers selected based on specific conditions.

[0541] This invention is a system that uses generative artificial intelligence to calculate customer health scores based on data accumulated in a customer management system, enabling customer success managers to respond efficiently. It also uses an emotion engine to analyze user emotion data and prioritize users who are likely to cancel or upsell.

[0542] Hardware and software used

[0543] The server uses the following hardware and software:

[0544] Hardware: A server with a powerful processor, sufficient memory, and ample storage.

[0545] Software: Customer Relationship Management (CRM), generative AI models (e.g., TensorFlow, PyTorch), sentiment analysis APIs (e.g., IBM Watson®, Microsoft® Azure®)

[0546] Data processing and calculation

[0547] The server processes and calculates data in the following procedure.

[0548] 1. Data collection: The server collects customer data (purchase history, inquiry history, website visit history, etc.) stored in the customer management system.

[0549] 2. Data preprocessing: The server preprocesses the collected data, including imputing missing values, normalizing data, and encoding categorical data.

[0550] 3. Applying the model: The server inputs the preprocessed data into a generative artificial intelligence model, which is built using TensorFlow or PyTorch.

[0551] 4. Calculation of health score: The server calculates each customer's health score based on the output of the AI ​​model. The health score is an index used to quantitatively evaluate the customer's contract renewal probability and upsell potential.

[0552] 5. Emotional Data Collection: The server collects users' emotional data (emails, chat content, social media posts, etc.).

[0553] 6. Emotion data analysis: The server inputs the collected emotion data into an emotion engine, which is built using IBM Watson and Microsoft Azure emotion analysis APIs.

[0554] 7. Customer list generation: Based on the analysis results of the emotion engine, the server generates a list of customers to contact today, this week, and this month for the customer success manager. If a user is determined to have a high probability of canceling or upselling, that user will be included in the list with priority.

[0555] Specific examples

[0556] Customer A: Extensive purchase history and many positive recent inquiries. Health score: 80.

[0557] Customer B: Has expressed dissatisfaction in recent emails and is likely to churn. Based on the sentiment engine analysis, this customer is included in the "Customers to Contact Today" list.

[0558] Prompt Sentence Examples

[0559] "Calculate a customer health score based on the data stored in your CRM system. Then use a sentiment engine to analyze user sentiment data and generate a list of customers for your customer success managers to contact."

[0560] In this way, the server is a system that uses CRM data and sentiment data to calculate a customer health score and enable customer success managers to respond efficiently.

[0561] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0562] Step 1:

[0563] The server collects customer data stored in the customer management system. The input includes customer purchase history, inquiry history, website visit history, etc. Specifically, the server obtains this data through API and stores it in an internal database. The output is the collected raw data.

[0564] Step 2:

[0565] The server preprocesses the collected data. The input includes the raw data collected in step 1. Specific operations performed by the server include imputing missing values, normalizing data, and encoding categorical data. The output is the preprocessed, clean data.

[0566] Step 3:

[0567] The server inputs the preprocessed data into a generative AI model. The input includes the data preprocessed in step 2. Specifically, the server inputs the data into an AI model built using TensorFlow or PyTorch and executes inference on the model. The output is a health score for each customer.

[0568] Step 4:

[0569] The server calculates a health score for each customer based on the output of the AI ​​model. The input includes the model output obtained in step 3. Specifically, the server calculates the health score and assigns a score to each customer. The output is a health score for each customer.

[0570] Step 5:

[0571] The server collects user emotional data. Inputs include users' emails, chat messages, and social media posts. Specifically, the server retrieves emotional data from these data sources and stores it in an internal database. The output is the collected emotional data.

[0572] Step 6:

[0573] The server inputs the collected emotion data into the emotion engine. The input includes the emotion data collected in step 5. Specifically, the server analyzes the emotion data using IBM Watson or Microsoft Azure emotion analysis APIs. The output is the analyzed emotion data.

[0574] Step 7:

[0575] Based on the analysis results of the emotion engine, the server generates a "list of customers to contact today, this week, and this month" for the customer success manager. The input includes the health score obtained in step 4 and the emotion data obtained in step 6. Specifically, the server integrates this data and prioritizes users who are likely to churn or upsell in the list. The output is a customer list for the customer success manager.

[0576] In this way, the server is a system that collects, pre-processes, and analyzes data at each step, ultimately enabling customer success managers to respond efficiently.

[0577] (Application example 2)

[0578] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0579] Traditional CRM systems lacked the means to quantitatively evaluate customer contract renewal probability and upselling potential, making it difficult to implement efficient marketing measures that took into account customer sentiment data. Additionally, there was a lack of tools to check customer health scores in real time and take appropriate action.

[0580] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0581] In this invention, the server includes: means for accumulating all sales activities, customer success and customer support response history, contract status, etc. in CRM; means for creating a health score using generative AI; means for extracting groups of companies that meet conditions such as signs of cancellation, changes in circumstances, and upselling; means for generating a "customer list to contact today, this week, and this month" for the CSM and drafting emails and content; means for the CSM to check, distribute, and respond; means for setting the timing of the next contact after entering the results; means for analyzing customer emotion data using an emotion engine and generating a list of customers to contact; and means for checking health scores in real time through an application installed on a smartphone and implementing marketing measures. This enables efficient marketing measures that take customer emotions and health scores into account.

[0582] "Sales activities" are a series of activities a company undertakes to sell its products and services.

[0583] "Customer Success" is the activity of helping customers get the most out of products and services and achieve success.

[0584] "Customer support response history" is a record of responses to customer inquiries and problems.

[0585] "Contract status" is information indicating the progress and content of a contract with a customer.

[0586] "CRM" stands for Customer Relationship Management System, a system for managing relationships with customers.

[0587] "Generative AI" is artificial intelligence that generates new information and predictions based on data.

[0588] A "health score" is an indicator that quantitatively evaluates a customer's probability of contract renewal and the possibility of upselling.

[0589] "Cancellation indicators" are signs that indicate the possibility that a customer may cancel a contract.

[0590] "Kihen" is an abbreviation for "functional change" and refers to a change in the functionality of a product or service.

[0591] "Upselling" is a sales technique that offers customers more expensive products or services.

[0592] A "group of companies" is a collection of multiple companies that meet certain conditions.

[0593] "CSM" stands for Customer Success Manager, a person whose role is to support customer success.

[0594] The "emotion engine" is an artificial intelligence for analyzing customer emotional data.

[0595] A "smartphone" is a mobile phone that can connect to the Internet and use applications.

[0596] An "application" is software that provides specific functions or services.

[0597] A "marketing initiative" is a series of activities aimed at promoting a product or service.

[0598] The system for implementing this invention is configured as follows: The server has a means for storing all sales activities, customer success and customer support response history, contract status, etc. in CRM, which allows detailed customer data to be managed in a unified manner.

[0599] Next, the server has a means to create a health score using generative AI. This generative AI quantitatively evaluates the customer's contract renewal probability and upselling potential based on the information accumulated in the CRM. Specifically, it uses Python's Scikit-learn library to build a model using RandomForestClassifier and calculates the health score.

[0600] Furthermore, the server has the means to extract groups of companies that meet certain conditions, such as signs of cancellation, changes in schedule, and upselling. This makes it possible to classify customers based on specific conditions and implement appropriate marketing measures.

[0601] The server also generates a list of customers to be contacted today, this week, and this month for the CSM, and has a means for drafting emails and content. This allows the CSM to efficiently respond to customers. It also includes a means for the CSM to check, distribute, and respond to emails, and a means for setting the timing of the next contact after entering the results.

[0602] The server also includes means for analyzing customer sentiment data using an emotion engine to generate a list of customers to contact, the emotion engine using a sentiment analysis model from the Transformers library to analyze customer feedback.

[0603] Finally, a smartphone application will allow marketers to check their customers' health scores in real time and implement marketing measures, enabling them to understand their customers' situations anytime, anywhere and respond quickly.

[0604] For example, if Customer A sends feedback saying, "I'm dissatisfied with the service I received recently," the sentiment engine will analyze this as a negative sentiment, and if the health score is low, the customer will be added to the priority contact list. This allows the CSM to respond quickly and resolve the customer's dissatisfaction.

[0605] An example prompt is, "Perform sentiment analysis based on customer feedback and generate a list of customers with low health scores to prioritize contact."

[0606] In this way, efficient marketing measures that take into account customer emotions and health scores become possible.

[0607] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0608] Step 1:

[0609] The server accumulates data such as sales activities, customer success and customer support response history, and contract status in the CRM. Various customer data is provided as input and saved in the CRM system. The accumulated customer data is obtained as output.

[0610] Step 2:

[0611] The server uses generative AI to create a health score based on data stored in the CRM. Customer data obtained from the CRM is provided as input, and a generative AI model (e.g., RandomForestClassifier) ​​is used to calculate a health score based on this data. The output is a health score for each customer.

[0612] Step 3:

[0613] The server extracts companies that meet certain criteria, such as signs of churn, changes in behavior, and upselling. Health scores and other customer data are provided as input, and companies that meet certain criteria are extracted based on this. The output is a list of companies that meet the criteria.

[0614] Step 4:

[0615] The server generates a "customer list to contact today, this week, and this month" for the CSM and creates email and content drafts. The extracted list of companies and customer data are provided as input, and the server generates the contact list and drafts based on this. The output is the contact list and email drafts.

[0616] Step 5:

[0617] The CSM checks the contact list and email draft generated by the server, makes any necessary corrections, and delivers them to the customer. The contact list and email draft are provided as input, and the CSM contacts the customer based on these. The output is the result of contacting the customer.

[0618] Step 6:

[0619] The server sets the timing of the next contact based on the results entered by the CSM. The input is the result of contacting the customer, and the server sets the timing of the next contact based on this. The output is the schedule for the next contact.

[0620] Step 7:

[0621] The server uses an emotion engine to analyze customer emotion data and generate a list of customers to contact. Customer feedback is provided as input, and the emotion engine (e.g., a sentiment analysis model from the Transformers library) is used to analyze the emotion data. The output is a contact list based on the emotion data.

[0622] Step 8:

[0623] Users check their health score in real time through an application installed on their smartphone and execute marketing measures. The input is the health score and contact list provided by the server, and marketing measures are planned and executed based on this. The output is the results of the executed marketing measures.

[0624] Example 3

[0625] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0626] Conventional customer relationship management systems make it difficult to centrally manage data such as sales activities, customer support history, and contract status, making it difficult to extract groups of companies based on specific criteria. Customer success managers also had to manually create contact lists and draft content, which hindered efficient customer support. Furthermore, they lacked the functionality to automatically set the next contact timing based on user sentiment data, which hindered efforts to improve customer satisfaction and reduce the risk of cancellation.

[0627] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0628] In this invention, the server includes means for storing all data such as sales activities, customer success and customer support response history, and contract status in a customer relationship management system, means for creating a health score using generative artificial intelligence, means for extracting groups of companies that meet conditions such as signs of cancellation, equipment changes, and additional sales, means for generating a "customer list to contact today, this week, and this month" for the customer success manager and drafting emails and content, means for the customer success manager to check, distribute, and respond, means for setting the timing of the next contact after entering the results, and means for automatically setting the timing of the next contact based on emotion data. This makes it possible to efficiently extract groups of companies based on the data stored in the customer relationship management system and automate the work of the customer success manager, thereby improving customer satisfaction and reducing the risk of cancellation.

[0629] "Sales activities" are a series of activities that a company undertakes to sell products and services to customers.

[0630] Customer success is the activity of helping customers get the most out of products and services and achieve their desired outcomes.

[0631] "Customer support response history" is a record of responses to customer inquiries and support requests.

[0632] "Contract status" is information indicating the content and progress of a contract concluded between a company and a customer.

[0633] A "customer relationship management system" is an information system for managing relationships with customers and efficiently conducting sales activities and customer support.

[0634] "Generative AI" is an AI technology that generates specific results based on data.

[0635] A "health score" is an index used to evaluate the status of a company or customer, and is a score calculated based on specific criteria.

[0636] A "cancellation warning sign" is a sign that indicates a high probability that a customer will cancel a contract.

[0637] "Equipment Change" refers to changing the equipment used by the customer to a new one.

[0638] "Add-on sales" refers to selling additional products or services to existing customers.

[0639] A "customer success manager" is someone whose role is to help customers get the most out of a product or service.

[0640] A "contact list" is a list of customers to be contacted within a specific time period.

[0641] "Email" means a digital message sent or received over the Internet.

[0642] A "content draft" is an early version of a message or material that you will send to a client.

[0643] "Emotion data" is data that indicates the user's emotional state and is collected from feedback, chat logs, etc.

[0644] "Next contact timing" refers to the next time you should contact the customer.

[0645] MODE FOR CARRYING OUT THE INVENTION

[0646] This invention is a system for improving the efficiency of sales activities and customer success management based on data accumulated in a customer relationship management system (CRM). A specific embodiment of this system will be described below.

[0647] Data collection and storage

[0648] The server stores data such as sales activities, customer success stories, customer support response history, and contract status in a customer relationship management system (CRM). For this purpose, a database management system (e.g., MySQL) is used. The server executes SQL queries to collect the necessary data and stores it in the CRM.

[0649] Creating a Health Score

[0650] The server uses generative artificial intelligence (AI) to create a health score based on the data stored in the CRM. This AI uses Python's Pandas library to analyze the data and calculate the health score based on a specific algorithm.

[0651] Extraction of company groups

[0652] Based on the health score and other analysis results, the server automatically extracts companies that meet certain conditions, such as signs of cancellation, equipment changes, or additional sales. For this, a data analysis tool (e.g., Python's Pandas library) is used. The server extracts companies that meet the conditions from the data frame and generates a list.

[0653] Generate contact lists and content drafts

[0654] For the selected companies, the server generates a "customer list to contact today, this week, and this month" for the customer success manager (CSM). This list is added to the schedule using the Google Calendar API. The server also uses an email template generation tool such as Mailchimp to create a draft email to send to each company.

[0655] Setting the next contact timing

[0656] After the CSM checks, distributes, and responds, the server automatically sets the timing of the next contact after entering the results. This is done using the Google Calendar API. It also has a function to automatically set the timing of the next contact based on emotional data. It uses an emotional analysis tool (e.g., IBM Watson Tone Analyzer) to analyze the user's emotions and set the timing of the next contact.

[0657] Specific examples

[0658] For example, the server extracts companies using the following prompt:

[0659] "List companies with a health score of 50 or less."

[0660] Also, when creating a contact list, use the following prompt:

[0661] "Generate a list of companies I need to contact this week."

[0662] To set the next contact timing based on emotion data, use the following prompt:

[0663] "If the user's emotions are unstable, schedule your next contact earlier."

[0664] The above is a specific embodiment for carrying out the present invention. This system makes it possible to improve customer relationship management efficiency, improve customer satisfaction, and reduce the risk of cancellation. The flow of the identification process in the third embodiment will be described with reference to FIG. 21.

[0665] System program processing flow

[0666] Step 1: Data collection and storage

[0667] The server stores data such as sales activities, customer success and support history, and contract status in a customer relationship management system (CRM).

[0668] Input: Sales activity data, customer support history, contract information

[0669] Output: Data stored in CRM

[0670] Specific operation: The server executes SQL queries to gather the necessary data and save it to the CRM. For example, it retrieves sales activity data from a MySQL database and saves it to the CRM.

[0671] Step 2: Create a Health Score

[0672] The server uses generative artificial intelligence (AI) to create a health score based on the data stored in the CRM.

[0673] Input: Data stored in CRM

[0674] Output: Health score

[0675] How it works: The server analyzes the data using Python's Pandas library and calculates a health score based on a specific algorithm, such as each company's sales activities and customer support history.

[0676] Step 3: Extracting companies

[0677] Based on the health score and other analytical results, the server automatically extracts a group of companies that meet the conditions for cancellation, equipment changes, additional sales, etc.

[0678] Input: Health score, other analysis results

[0679] Output: A list of companies that match the criteria

[0680] Specific operation: The server uses a data analysis tool (e.g., Python's Pandas library) to extract companies that match the criteria from the data frame and generate a list. For example, it extracts companies with a health score of 50 or less.

[0681] Step 4: Generate a contact list

[0682] The server generates a "list of customers to contact today, this week, and this month" for the customer success manager (CSM) for the extracted group of companies.

[0683] Input: A list of companies that match the criteria

[0684] Output: Contact list

[0685] Specific operation: The server uses the Google Calendar API to add a contact list to a calendar. For example, it generates a list of companies that need to be contacted this week and adds it to Google Calendar.

[0686] Step 5: Create a content draft

[0687] The server generates a draft email to send to each company based on the contact list.

[0688] Input: Contact list

[0689] Output: Email draft

[0690] Specific operation: The server uses an email template generation tool such as Mailchimp to create a draft of the email to be sent to each company. For example, it uses the Mailchimp API to create a draft of the email to be sent to each company.

[0691] Step 6: Set the next contact timing

[0692] After the CSM checks, distributes, and responds, the server automatically sets the timing for the next contact after the results are entered.

[0693] Input: CSM confirmation results, distribution results

[0694] Output: Next contact timing

[0695] Specific operation: The server uses the Google Calendar API to add the next contact timing to the calendar. For example, after the CSM confirms, the next contact timing is automatically added to Google Calendar.

[0696] Step 7: Determine next contact timing based on emotional data

[0697] The server automatically sets the next contact timing based on the emotion data.

[0698] Input: Emotion data

[0699] Output: Next contact timing

[0700] Specific operation: The server analyzes the user's emotions using a sentiment analysis tool (e.g., IBM Watson Tone Analyzer) and sets the timing of the next contact. For example, if the user's emotions are judged to be unstable, it sets a follow-up contact three days later.

[0701] The above are the specific processing steps of the program of this system.

[0702] (Application example 3)

[0703] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0704] In traditional customer management systems, data analysis to predict signs of churn and upselling opportunities is often done manually, resulting in inefficiency. It is also difficult to set the timing of follow-ups that take into account customer sentiment data, which means that improvements in customer satisfaction and reductions in the risk of churn are not being fully implemented. Furthermore, automation of customer management using smartphones has not progressed, requiring rapid on-site responses.

[0705] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0706] In this invention, the server includes: means for accumulating all sales activities, customer success and customer support response history, contract status, etc. in CRM; means for creating a health score using generative AI; means for extracting groups of companies that meet conditions such as signs of cancellation, changes in schedule, and upselling; means for generating a "customer list to contact today, this week, and this month" for the CSM and drafting emails and content; means for the CSM to check, distribute, and respond; means for setting the timing of the next contact after entering the results; means for automatically setting the timing of the next contact based on customer emotion data; and means for executing a customer filtering function, a contact list generation function, and an emotion engine using an application installed on a smartphone. This enables automated and efficient customer management, improving customer satisfaction and reducing the risk of cancellation.

[0707] "Sales activities" are a series of activities that a company undertakes to sell its products and services.

[0708] "Customer Success and Customer Support Response History" means a record of past responses to assist Customer success and provide support.

[0709] "Contract status" is information that indicates the current state of the contract concluded between the company and the customer.

[0710] "CRM" stands for Customer Relationship Management System, a software for managing relationships with customers.

[0711] "Generative AI" is an artificial intelligence technology that generates new information and predictions based on data.

[0712] A "health score" is a numerical indicator of a customer's health status and satisfaction.

[0713] "Cancellation indicators" are signs that indicate the possibility that a customer may cancel a contract.

[0714] "Kihen" is an abbreviation for "function change" and refers to a change in the functions of a system or service.

[0715] "Upselling" is a sales technique that offers customers more expensive products or additional services.

[0716] A "group of companies" is a collection of multiple companies that meet certain conditions.

[0717] "CSM" stands for Customer Success Manager, a person whose role is to support customer success.

[0718] A "customer list" is a list of customers who need to be contacted or dealt with.

[0719] "Email and Content Draft" means a draft of an email or content to be sent to a customer.

[0720] "Emotion data" is data that indicates the emotional state of the customer.

[0721] A "smartphone" is a mobile phone that can connect to the Internet and use applications.

[0722] An "application" is a software program designed to perform a particular function.

[0723] The "customer filtering function" is a function for selecting customers based on specific conditions.

[0724] The "contact list generation function" is a function that automatically creates a list of customers who need to be contacted.

[0725] The "emotion engine" is a system that analyzes customer emotional data and determines the appropriate response.

[0726] The system for implementing this invention has the following configuration: The server has a means for storing all sales activities, customer success and customer support response history, contract status, etc. in CRM. This allows all customer-related information to be managed centrally.

[0727] Next, there is a method to create a health score using generative AI. This generative AI quantifies the customer's health status and satisfaction based on the information accumulated in CRM, making it possible to quantitatively evaluate the customer's condition.

[0728] Additionally, there is a method to extract a group of companies that meet certain conditions, such as signs of cancellation, changes in contract status, and upselling. This method automatically lists specific companies based on conditions such as the generated health score and contract renewal period.

[0729] For CSMs, it provides a way to generate a list of customers to contact today, this week, and this month, and to draft emails and content, allowing CSMs to respond to customers efficiently.

[0730] It also includes a means for the CSM to check, distribute, and respond, as well as a means for setting the timing of the next contact after entering the results. This automates the entire customer response process.

[0731] Additionally, there is a way to automatically schedule the next contact based on customer sentiment data. This sentiment engine analyzes the customer's emotional state and determines the appropriate timing for follow-up.

[0732] Finally, there is a way to use an application installed on a smartphone to perform customer filtering, contact list generation, and sentiment engines, which allows for quick response on the spot.

[0733] For example, the following prompts could be fed into a generative AI model:

[0734] Customer ID: 1

[0735] Health score: 40

[0736] Last contact date: 60 days ago

[0737] Emotion score: 30

[0738] Set a date for your next contact with this customer and generate an email draft.

[0739] By inputting this prompt into a generative AI model, the appropriate timing for contact and email draft are automatically generated.

[0740] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0741] Step 1:

[0742] The server stores all data such as sales activities, customer success and customer support response history, contract status, etc. in CRM. The input is sales activity and customer support history data, and by saving this in CRM, all customer information is managed centrally. The output is the data stored in CRM.

[0743] Step 2:

[0744] The server uses generative AI to create a health score based on the information stored in the CRM. The input is customer data stored in the CRM, which the generative AI analyzes to calculate a health score. The output is a health score for each customer.

[0745] Step 3:

[0746] The server extracts companies that meet certain criteria, such as signs of cancellation, changes in contract status, and upselling. The inputs include the generated health score and contract renewal period, and specific companies are listed based on these. The output is a list of companies that meet the criteria.

[0747] Step 4:

[0748] The server generates a "customer list to contact today, this week, and this month" for the CSM and creates email and content drafts. The input is the extracted company list and contact timing conditions, and based on this, the server generates the contact list and email draft. The output is the contact list and email draft.

[0749] Step 5:

[0750] The CSM checks the contact list and email draft generated by the server and delivers them to the customer. As input, it receives the contact list and email draft provided by the server and contacts the customer based on this. As output, it completes the contact with the customer.

[0751] Step 6:

[0752] The CSM inputs the results of customer interactions into the server and sets the timing of the next contact. The input is the result data of the customer interaction, and the timing of the next contact is automatically set based on this. The output is the timing of the next contact.

[0753] Step 7:

[0754] The server automatically sets the timing of the next contact based on the customer's emotional data. The input is the customer's emotional data, which the emotion engine analyzes to determine the appropriate timing for follow-up. The output is the timing of the next contact based on the emotional data.

[0755] Step 8:

[0756] The device (smartphone) executes the customer filtering function, contact list generation function, and emotion engine provided by the server. Data from the server is input, and each function is executed on the smartphone based on this data. The output is a filtered customer list, a generated contact list, and the next contact timing determined by the emotion engine.

[0757] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0758] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0759] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[0760] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0761] [Second embodiment]

[0762] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0763] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0764] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0765] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0766] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0767] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0768] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0769] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0770] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0771] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0772] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0773] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0774] "Example 1"

[0775] One embodiment of the present invention provides a means for storing all sales activities, customer success and customer support response history, contract status, etc. in CRM. Specifically, a system is built with an interface and API for storing various customer information in CRM. This allows various communications and transaction histories with customers to be managed in a unified manner, and a database for analysis is created.

[0776] "Example 2"

[0777] Furthermore, a means of creating health scores using generative AI will be provided. Specifically, an AI engine will be built that analyzes customer situations and behaviors based on various data accumulated in CRM and calculates a health score for each customer. This health score will be used to quantitatively evaluate important business indicators such as the probability of customer contract renewal and the possibility of upselling.

[0778] "Example 3"

[0779] It also provides a means to extract groups of companies that meet certain conditions, such as signs of cancellation, changes in schedule, or upselling. Specifically, a filtering function will be built that automatically extracts groups of companies that meet specific conditions based on health scores and other analysis results. For example, it is possible to automatically list companies that meet certain conditions, such as companies with health scores below a certain threshold, or companies where a certain amount of time has passed since their most recent contract renewal.

[0780] "Example 4"

[0781] Furthermore, a means will be provided for CSMs to generate "customer lists to contact today, this week, and this month" and draft emails and content. Specifically, a function will be built that plans contact with the extracted group of companies at the appropriate timing and content, and automatically generates action lists and content drafts based on that. This means that CSMs only need to check, distribute, and respond, and after entering the results, the timing of the next contact will be set automatically.

[0782] The processing flow of each embodiment will be described below.

[0783] "Example 1"

[0784] Step 1: Using an interface or API to store customer information in the CRM, store all sales activities, customer success and customer support response history, contract status, etc. in the CRM.

[0785] "Example 2"

[0786] Step 1: Obtain various data stored in CRM.

[0787] Step 2: Based on the acquired data, generative AI is used to analyze the customer's situation and behavior. Step 3: Based on the analysis results, a health score is calculated for each customer.

[0788] "Example 3"

[0789] Step 1: Get your health score and other analytics.

[0790] Step 2: Based on the results obtained, automatically extract a group of companies that meet specific conditions. "Example 4"

[0791] Step 1: Plan appropriate timing and content for contacting the selected group of companies.

[0792] Step 2: Automatically generate action lists and content drafts based on your plan.

[0793] Step 3: The CSM reviews the generated action list and content drafts and distributes or responds to them.

[0794] Step 4: After entering the results, automatically set the next contact timing.

[0795] Example 1

[0796] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0797] With traditional customer relationship management systems, it is difficult to centrally manage customer information such as sales activities, customer success and customer support response history, and contract status, resulting in data fragmentation and duplication. Furthermore, analyzing customer information, creating health scores, detecting signs of churn, and identifying upselling opportunities are all done manually, which is inefficient and prone to human error. Furthermore, creating contact lists and drafting emails for customer success management is time-consuming, making it difficult to respond quickly.

[0798] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0799] In this invention, the server includes means for accumulating all sales activity, customer success and customer support response history, contract status, etc. in a customer relationship management system, means for creating a health score using generative artificial intelligence, means for extracting groups of companies that meet conditions such as signs of cancellation, changes in circumstances, and upselling, means for generating a "customer list to contact today, this week, and this month" for customer success management and drafting emails and content, means for customer success management to check, distribute, and respond, means for setting the timing of the next contact after inputting the results, means for a user to input customer information from a terminal, means for the terminal to send the input data to the server via an application program interface, means for the server to save the received data in the customer relationship management system database, means for the server to process the saved data for analysis, and means for the server to provide the analysis results to the user. This enables centralized management of customer information, efficient analysis, and rapid response.

[0800] "Sales activities" are a series of actions taken to propose products and services to customers and promote sales.

[0801] "Customer success" refers to support activities that help customers make the most of the products and services they purchase and increase their satisfaction.

[0802] "Customer support response history" is a record of responses to customer inquiries and problems.

[0803] "Contract status" is information indicating the content and progress of the contract concluded with the customer.

[0804] A "customer relationship management system" is a software system that centrally manages customer information and efficiently conducts sales activities and customer support.

[0805] "Generative AI" is an AI technology that automatically analyzes and predicts data.

[0806] A "health score" is a numerical indicator of a customer's health status and satisfaction.

[0807] "Cancellation warning signs" are signs that indicate the possibility that a customer may cancel a service or contract.

[0808] "Change in equipment" refers to a change in the service or product that a customer is currently using.

[0809] "Upselling" is a sales technique that encourages customers to purchase more expensive products or services.

[0810] "Customer success management" is a management activity aimed at supporting customer success and building long-term relationships.

[0811] An "application program interface" is an interface for exchanging data between different software systems.

[0812] A "terminal" is a device such as a computer or smartphone operated by a user.

[0813] A "server" is a computer system for storing and processing data.

[0814] A "database" is a system for efficiently storing, searching, and managing data.

[0815] "Analysis" is the process of extracting information and finding meaning from data.

[0816] "Email" is a digital message sent and received over the Internet.

[0817] "Content" is a general term for information and materials provided to users.

[0818] "Drafting" refers to creating a draft of a document or email.

[0819] This invention is a system that uses a customer relationship management system (CRM) to centrally manage customer information such as sales activities, customer success and customer support response history, and contract status, thereby achieving efficient analysis and rapid response. A specific embodiment of this system is described below.

[0820] Hardware and software used

[0821] Hardware: A powerful server (e.g., Dell PowerEdge R740)

[0822] Software: CRM systems (e.g., Salesforce), database management systems (e.g., MySQL), API management tools (e.g., Postman)

[0823] Program processing

[0824] The server receives the customer information entered by the user from the terminal and stores it in the CRM system. Specifically, it performs the following processes:

[0825] 1. The user enters customer information from the terminal.

[0826] The user opens a browser on the device and accesses the input form of the CRM system. For example, the user enters "Customer name: Company A, Business content: New product proposal, Date: October 1, 2023."

[0827] 2. The device sends the entered data to the server via API.

[0828] The terminal converts the input data into JSON format and sends it to an API endpoint using an HTTP POST request, for example, " / api / v1 / customers".

[0829] 3. The server saves the received data in the CRM system database

[0830] The server parses the received JSON data and generates an SQL query to insert it into the database. For example, it executes the query "INSERT INTO customers (name, deal_content, date) VALUES ('Company A', 'New product proposal', '2023-10-01')".

[0831] 4. The server processes the stored data for analysis.

[0832] The server aggregates the stored data and filters it based on certain conditions, for example, by running the query "SELECT COUNT() FROM deals WHERE status = 'success' AND date BETWEEN '2023-01-01' AND '2023-12-31'".

[0833] 5. The server provides the analysis results to the user.

[0834] The server converts the analysis results into graphs and charts and displays them on a dashboard, for example, a pie chart showing success rates, which is displayed in the user's browser.

[0835] Examples of concrete examples and prompts

[0836] Examples:

[0837] When a user wants to enter information about a business deal with a new customer, he or she follows the steps below.

[0838] 1. The user enters business negotiation information (customer name, business details, date, etc.) from the terminal.

[0839] 2. The device sends the entered data to the server via the API.

[0840] 3. The server stores the received data in the CRM system database.

[0841] 4. The stored data is later used to analyze sales activities and generate reports.

[0842] Example prompt sentence:

[0843] "Please enter the details of your new customer opportunity. Include the customer name, details of the opportunity, and the date."

[0844] In this way, a system is realized in which the server, terminals, and users work together to centrally manage customer information and form a database for analysis.

[0845] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0846] Step 1:

[0847] The user enters customer information from the terminal.

[0848] The user opens a browser on their device and accesses the input form of the CRM system. For example, they enter "Customer name: Company A, Negotiation content: New product proposal, Date: October 1, 2023." The entered data is divided into fields such as customer name, negotiation content, and date.

[0849] Step 2:

[0850] The terminal sends the entered data to the server via the API.

[0851] The terminal converts the input data into JSON format and sends it to the API endpoint using an HTTP POST request, for example, to " / api / v1 / customers." The input data is structured as a JSON object and sent to the API endpoint.

[0852] Step 3:

[0853] The server stores the received data in the CRM system database.

[0854] The server parses the received JSON data and generates a SQL query to insert into the database, for example "INSERT INTO customers (name, deal_content, date) VALUES ('Company A', 'New Product Proposal', '2023-10-01')". The input data is saved in the appropriate table in the database.

[0855] Step 4:

[0856] The server processes the stored data for analysis.

[0857] The server aggregates the stored data and filters it based on certain conditions, for example, by running the query "SELECT COUNT() FROM deals WHERE status = 'success' AND date BETWEEN '2023-01-01' AND '2023-12-31'". The input data is processed for aggregation and filtering, and analytical results are generated.

[0858] Step 5:

[0859] The server provides the analysis results to the user.

[0860] The server converts the analysis results into graphs and charts and displays them on a dashboard. For example, a pie chart showing success rates may be generated and displayed in the user's browser. The input data is processed for visualization and provided to the user.

[0861] In this way, a system is realized in which the server, terminals, and users work together to centrally manage customer information and form a database for analysis.

[0862] (Application example 1)

[0863] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0864] Conventional CRM systems do not centrally manage customer information and interaction information, making it difficult for store staff to check the information they need in real time when responding to customers. Furthermore, there was a lack of a way to effectively utilize customer visit history, purchase history, and customer support response history, making it difficult to improve the quality of customer support.

[0865] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0866] In this invention, the server includes: means for accumulating all sales activity, customer success and customer support response history, contract status, etc. in CRM; means for creating a health score using generative AI; means for extracting groups of companies that meet conditions such as signs of cancellation, time changes, and upselling; means for generating a "customer list to contact today, this week, and this month" for the CSM and drafting emails and content; means for the CSM to check, distribute, and respond; means for setting the timing of the next contact after entering the results; means for checking customer visit history, purchase history, and customer support response history in real time for use by store staff when responding to customers; and means for checking customer information and interaction information in real time to provide better customer service. This allows store staff to check the information they need when responding to customers in real time, improving the quality of customer service.

[0867] "Sales activities" are a series of activities a company undertakes to sell its products and services.

[0868] "Customer Success" is the activity of helping customers get the most out of products and services and achieve success.

[0869] "Customer support response history" is a record of responses to customer inquiries and problems.

[0870] "Contract status" is information indicating the progress and content of a contract with a customer.

[0871] "CRM" stands for Customer Relationship Management System, a system for centrally managing customer information.

[0872] "Generative AI" is a system that uses artificial intelligence to analyze data and generate information for specific purposes.

[0873] A "health score" is a numerical indicator of a customer's health status and satisfaction.

[0874] "Cancelers" are signs that indicate the possibility that a customer will cancel a service.

[0875] "Kihen" is an abbreviation for "functional change" and refers to a change in the functionality of a product or service.

[0876] "Upselling" is a sales technique that offers customers more expensive products or services.

[0877] A "group of companies" is a collection of multiple companies that meet certain conditions.

[0878] "CSM" stands for Customer Success Manager, a person whose role is to support customer success.

[0879] A "customer list" is a list of customers selected based on specific conditions.

[0880] "Drafting" refers to creating a draft of a document or content.

[0881] A "brick and mortar store" is a retail outlet that exists in a physical location.

[0882] "Store visit history" is a record of a customer's visits to a store.

[0883] "Purchase history" is a record of products and services purchased by a customer.

[0884] "Interaction information" is information about interactions and exchanges with customers.

[0885] The system for implementing this invention consists of three main components: a server, a terminal, and a user. The server centrally manages customer information and analyzes the data using a generative AI model. The terminal is a device used by store staff when dealing with customers, such as a smartphone or tablet. The user is a store staff member or a customer success manager (CSM).

[0886] The server has a means of storing all sales activity, customer success and customer support response history, contract status, etc. in CRM. This allows for centralized management of various communication and transaction histories with customers. In addition, it has a means of creating a health score using generative AI and extracting companies that meet conditions such as signs of cancellation, feature changes, and upselling. This makes it possible to quantify customer health and satisfaction and classify customers based on specific conditions.

[0887] The terminals are used by store staff when dealing with customers and provide a means to check customer visit history, purchase history, and customer support response history in real time, allowing store staff to check customer information and interaction information in real time and provide better customer service.

[0888] Users contact customers at the appropriate time based on the "Customer list to contact today, this week, this month" provided by the server. In addition, users can create drafts of emails and content, which are then checked, distributed, and handled by the CSM. After entering the results, there is also a means to set the timing of the next contact.

[0889] As a concrete example, the following prompt sentence can be input to a generative AI model:

[0890] Example prompt sentence:

[0891] "Get the information for the customer whose customer ID is 1."

[0892] Using this prompt, the server retrieves customer information and displays it on the terminal, allowing store staff to check the information they need in real time when assisting customers and improve the quality of their customer service.

[0893] Also, the prompt statement to obtain interaction information for a customer with customer ID 1 is as follows:

[0894] Example prompt sentence:

[0895] "Get interaction information for customer with customer ID 1."

[0896] Using this prompt, the server retrieves interaction information and displays it on the device, allowing store staff to review past interactions with customers and provide more appropriate responses.

[0897] In this way, by linking the server, terminal, and user, it is possible to achieve centralized management of customer information and provide information in real time, thereby improving the quality of customer service.

[0898] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0899] Step 1:

[0900] The server stores customer information such as sales activities, customer success and customer support response history, and contract status in CRM. As input, it receives data on various communications and transactions with customers and stores this in a database. As output, the stored customer information is managed centrally.

[0901] Step 2:

[0902] The server uses a generative AI model to create a health score based on the information stored in the CRM. Customer history data is received as input, and the generative AI model analyzes it. The output is a health score that quantifies the customer's health status and satisfaction.

[0903] Step 3:

[0904] The server extracts companies that meet certain criteria, such as those predicting churn, changing features, or upselling. As input, it receives health scores and other customer data, filters the data based on specific criteria, and generates a list of companies that meet the criteria as output.

[0905] Step 4:

[0906] The server generates a "customer list to contact today, this week, and this month" for the CSM and creates email and content drafts. The server receives the extracted company list and health score as input, and the generative AI model uses this to create the list and draft. The output is a customer list to contact and email content drafts.

[0907] Step 5:

[0908] The user (CSM) checks the customer list and draft provided by the server and makes corrections as necessary. As input, the list and draft provided by the server are received, and the user checks and corrects them. As output, the final customer list and email content are confirmed.

[0909] Step 6:

[0910] The user (CSM) contacts customers based on the confirmed customer list. As input, the confirmed customer list and email content are received, and the user sends this to the customer. As output, contact with the customer is completed.

[0911] Step 7:

[0912] The user (CSM) inputs the contact results into the server and sets the timing of the next contact. The server receives the contact results and the timing of the next contact as input, and stores them in the database. The timing of the next contact is set as output.

[0913] Step 8:

[0914] When a store staff member assists a customer, the terminal checks the customer's visit history, purchase history, and customer support response history in real time. It receives a customer ID as input and retrieves related customer information from the server. As output, the customer information is displayed on the terminal.

[0915] Step 9:

[0916] The terminal displays customer information and interaction information in real time, helping staff to provide better customer service. As input, it receives customer information obtained from the server and staff responds to customers based on this information. As output, the quality of customer service improves.

[0917] Example 2

[0918] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0919] Conventional customer relationship management systems make it difficult to quantitatively evaluate customer contract renewal rates and upselling potential, resulting in insufficient efficiency in sales activities and customer support. Furthermore, the preprocessing and analysis of customer data requires a significant amount of time and effort, making them inadequate in a business environment where rapid response is required. Furthermore, the calculation of health scores using generative artificial intelligence and the generation of customer lists based on them were not automated, placing a heavy burden on customer success managers.

[0920] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0921] In this invention, the server includes means for accumulating all information such as sales activities, customer success and customer support response history, and contract status in a database; means for generating a health score using generative artificial intelligence; means for extracting a group of companies that meet conditions such as signs of cancellation, changes in circumstances, and upselling; means for generating a "customer list to contact today, this week, and this month" for a customer success manager and drafting emails and content; means for the customer success manager to check, distribute, and respond; means for setting the timing of the next contact after entering the results; means for acquiring customer data from the database; means for preprocessing the acquired data; means for generating prompt statements to be input into a generative artificial intelligence model based on the preprocessed data; means for inputting the generated prompt statements into the generative artificial intelligence model and calculating a health score; and means for saving the calculated health score in the database and displaying it for user viewing. This enables quantitative evaluation of a customer's contract renewal probability and the possibility of upselling, thereby improving the efficiency of sales activities and customer support.

[0922] "Sales activities" refers to a series of actions and processes for selling products or services to customers.

[0923] "Customer success" refers to activities that help customers get the most out of products and services and achieve success.

[0924] "Customer support response history" refers to a record of responses to customer inquiries and problems.

[0925] "Contract status" refers to information indicating the status and content of a contract with a customer.

[0926] "Database" refers to a system for efficiently managing and storing data.

[0927] "Generative AI" refers to AI that has the ability to generate new information and predictions based on input data.

[0928] A "health score" is an indicator that quantitatively evaluates a customer's probability of continuing their contract or the possibility of upselling.

[0929] "Cancellation indicators" refer to signs that indicate the possibility that a customer may cancel a contract.

[0930] "Device change" refers to changes in the customer's contract details or usage status.

[0931] "Upselling" refers to selling a customer an additional, more expensive product or service.

[0932] A "Customer Success Manager" is a person whose role is to support customer success and improve customer satisfaction.

[0933] A "prompt sentence" refers to an instruction sentence to be input into generative artificial intelligence.

[0934] "Preprocessing" refers to the process of preparing data in a format suitable for analysis or model input.

[0935] This invention is a customer relationship management system that uses generative artificial intelligence to calculate customer health scores and improve the efficiency of sales activities and customer support. Specific embodiments of this system are described below.

[0936] The server first retrieves customer data from a database. This data includes customer purchase history, support inquiry history, contract status, and so on. A common relational database management system (RDBMS) can be used as the database. Suitable examples include MySQL and PostgreSQL.

[0937] Next, the server preprocesses the acquired data. Preprocessing includes imputing missing values, removing outliers, and normalizing the data. For this, the Python Pandas library can be used. For example, to impute missing values ​​with the mean, use code like "df.fillna(df.mean())".

[0938] Based on the preprocessed data, the server generates a prompt to be input to the generative AI model. The prompt includes information such as customer ID, purchase history, and support inquiry history. As a concrete example, the following prompt is generated:

[0939] "Customer ID: 12345 has made three purchases and two support inquiries in the past six months. Please rate this customer's likelihood of contract renewal and upselling potential."

[0940] Based on the generated prompt text, the server inputs it into a generative AI model. An example of a suitable generative AI model is OpenAI's GPT-4. The server inputs the prompt text into the generative AI model and analyzes the customer's behavioral patterns, contract renewal probability, and upselling possibility. The model outputs results such as "contract renewal probability: 85%, upselling possibility: 70%."

[0941] The calculated health scores are stored in the database again. The server inserts the results into the database using an SQL query, for example, "INSERT INTO health_scores (customer_id, retention_probability, upsell_probability) VALUES (12345, 85, 70)".

[0942] Finally, users can view these health scores through the CRM system interface. Users can use the health scores to prioritize customer interactions and develop marketing strategies. The CRM system interface displays the results using HTML and JavaScript.

[0943] In this way, the server utilizes generative artificial intelligence to calculate a health score based on customer data, providing a system that improves the efficiency of sales activities and customer support.

[0944] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0945] Step 1:

[0946] The server retrieves customer data from the database. As input, it accesses the database using an SQL query to extract information such as the customer's purchase history, support inquiry history, and contract status. Specifically, it executes a command such as "SELECT FROM customer_data WHERE date >= '2023-01-01'". As output, it obtains the retrieved customer data.

[0947] Step 2:

[0948] The server preprocesses the acquired data. It uses the customer data acquired in step 1 as input. Specifically, it creates a data frame using Python's Pandas library, imputes missing values ​​using "df.fillna(df.mean())", and removes outliers using "df = df[df['purchase_amount'] < threshold]". The preprocessed data is obtained as output.

[0949] Step 3:

[0950] The server generates a prompt sentence based on the preprocessed data to be input into the generative artificial intelligence model. The data preprocessed in step 2 is used as input. Specifically, the prompt sentence is generated using Python string manipulation. For example, execute code such as "prompt = f'Customer ID: {customer_id} has made {purchase_count} purchases and {support_count} support inquiries over the past six months. Please evaluate this customer's contract renewal probability and upsell potential.'" The generated prompt sentence is obtained as output.

[0951] Step 4:

[0952] The server inputs the generated prompt into a generative AI model. The prompt generated in step 3 is used as input. Specifically, the prompt is input into a generative AI model (for example, OpenAI's GPT-4) to analyze customer behavior patterns, contract renewal probability, and upselling possibilities. For example, a request is sent as "response = openai.Completion.create(engine='gpt-4', prompt=prompt)". The output includes results such as contract renewal probability and upselling possibilities.

[0953] Step 5:

[0954] The server stores the calculated health scores in the database. It uses the results from step 4 as input. It inserts the results into the database using an SQL query. For example, it executes a command like "INSERT INTO health_scores (customer_id, retention_probability, upsell_probability) VALUES (12345, 85, 70)". The output is the health scores stored in the database.

[0955] Step 6:

[0956] Users view health scores through the CRM system interface. The input is the health scores stored in the database. The specific operation is to display the results using HTML or JavaScript. Users can use these health scores to prioritize customer interactions and develop marketing strategies. The output is a health score that users can view.

[0957] (Application example 2)

[0958] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0959] Conventional CRM systems lacked indicators for quantitatively evaluating customer contract renewal probability and upselling potential, resulting in reduced efficiency in sales activities and customer support. It was also difficult to provide personalized product recommendations and promotions based on customer purchase and browsing history.

[0960] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for storing all information such as sales activities, customer success and customer support response history, and contract status in CRM; means for creating a health score using generative AI; means for extracting a group of companies that meet conditions such as signs of cancellation, time changes, and upselling; means for generating a "customer list to contact today, this week, and this month" for the CSM and drafting emails and content; means for the CSM to check, distribute, and respond; means for setting the timing of the next contact after entering the results; means for collecting data such as the user's purchase history, browsing history, and products added to the cart; means for calculating each user's health score using generative AI; and means for providing personalized product recommendations and promotions based on the health score. This makes it possible to quantitatively evaluate a customer's contract renewal probability and upselling potential and provide personalized services.

[0961] "Sales activities" are a series of activities that a company undertakes to sell its products and services.

[0962] "Customer Success" is the activity of helping customers get the most out of a product or service.

[0963] "Customer support response history" is a record of responses to customer inquiries and problems.

[0964] "Contract status" is information indicating the progress and content of a contract with a customer.

[0965] "CRM" stands for Customer Relationship Management System, a system for managing relationships with customers.

[0966] "Generative AI" is artificial intelligence that generates new information and predictions based on data.

[0967] A "health score" is an indicator that quantitatively evaluates a customer's probability of contract renewal and the possibility of upselling.

[0968] "Cancellation indicators" are signs that indicate the possibility that a customer may cancel a contract.

[0969] "Kihen" is an abbreviation for "function change" and refers to a change in the functions of a system or service.

[0970] "Upselling" is a sales technique that offers customers more expensive products or services.

[0971] A "group of companies" is a collection of multiple companies that meet certain conditions.

[0972] "CSM" stands for Customer Success Manager, a person whose role is to support customer success.

[0973] "Purchase history" is a record of products that a customer has purchased in the past.

[0974] "Browse history" is a record of products that a customer has browsed in the past.

[0975] "Cart Items" is a record of items that a customer has added to their online shopping cart.

[0976] "Personalized product recommendations" are products suggested based on a customer's individual preferences and behavior.

[0977] "Promotion" refers to activities or campaigns aimed at promoting the sale of a product or service.

[0978] A system for implementing the present invention has the following configuration.

[0979] First, the server has a means to store all sales activities, customer success and customer support response history, contract status, etc. in CRM. This allows various data related to customers to be managed centrally.

[0980] Next, the server has a means to create a health score using generative AI, which generates a health score that quantitatively evaluates the probability of a customer continuing their contract or the possibility of upselling based on the information accumulated in the CRM.

[0981] Furthermore, the server has a means for extracting a group of companies that meet certain conditions, such as signs of cancellation, time changes, upselling, etc. This means allows a group of companies to be extracted based on specific conditions, and target customers to be identified.

[0982] The server also has a means for generating a "list of customers to contact today, this week, and this month" for the CSM and drafting emails and content, which allows the CSM to efficiently respond to customers.

[0983] Additionally, the server provides a means for the CSM to review, distribute, and respond, allowing the CSM to review the generated lists and drafts and contact customers at the appropriate time.

[0984] Next, the server has a means for setting the timing of the next contact after the result is input, which automatically sets the timing of the next contact based on the result of the customer interaction.

[0985] The server also has a means for collecting data such as the user's purchase history, browsing history, products added to the cart, etc. This allows the collection of user behavior data, which becomes the basic data for analysis.

[0986] Furthermore, the server has a means for calculating a health score for each user using generative AI, which generates a personalized health score for each user.

[0987] Finally, the server has a means for providing personalized product recommendations and promotions based on the health score, thereby suggesting optimal products and promotions to the user.

[0988] For example, it can recommend products that users are likely to purchase next based on their past purchases and browsing history, and it can offer special promotions to users with low health scores to encourage them to continue using the service.

[0989] An example of a prompt is as follows:

[0990] "User purchase history: Product A, Product B, browsing history: Product C, Product D, products added to cart: Product E. Please generate a health score for this user."

[0991] In this way, the server can use generative AI to calculate a health score based on customer behavioral data and provide personalized services.

[0992] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0993] Step 1:

[0994] The server accumulates data such as sales activities, customer success and customer support response history, and contract status in CRM. As input, various customer data is provided and saved in the CRM system. As output, the accumulated data is recorded in CRM.

[0995] Step 2:

[0996] The server uses generative AI to create health scores based on data stored in the CRM. Customer data stored in the CRM is provided as input, which the generative AI model analyzes. A health score for each customer is generated as output.

[0997] Step 3:

[0998] The server extracts companies that meet certain criteria, such as churn predictions, changes in behavior, and upselling. The generated health scores and other criteria are provided as input, and a database query is executed. The output is a list of companies that meet the criteria.

[0999] Step 4:

[1000] The server generates a "customer list to contact today, this week, and this month" for the CSM and creates email and content drafts. As input, the extracted company list and health score are provided, and email and content drafts are generated based on templates. As output, a customer list to contact and drafts are generated.

[1001] Step 5:

[1002] The CSM checks the lists and drafts generated by the server and contacts the customer at the appropriate time. As input, the CSM checks the lists and drafts provided by the server. As output, contact is made to the customer.

[1003] Step 6:

[1004] The server sets the timing of the next contact based on the results entered by the CSM. As input, the contact results entered by the CSM are provided and the timing of the next contact is calculated. As output, the timing of the next contact is set.

[1005] Step 7:

[1006] The server collects data such as user purchase history, browsing history, and items added to the cart. As input, user behavior data is provided and stored in a database. As output, the collected data is recorded.

[1007] Step 8:

[1008] The server uses generative AI to calculate each user's health score. Collected user behavior data is provided as input, which the generative AI model analyzes. The output is a health score for each user.

[1009] Step 9:

[1010] The server provides personalized product recommendations and promotions based on the health score. As input, the generated health score is provided and analyzed by a recommendation algorithm. As output, personalized product recommendations and promotions are generated.

[1011] Specifically, the system recommends products that users are likely to purchase next based on their past purchases and browsing history. It also offers special promotions to users with low health scores to encourage them to continue their subscriptions.

[1012] An example of a prompt is as follows:

[1013] "User purchase history: Product A, Product B, browsing history: Product C, Product D, products added to cart: Product E. Please generate a health score for this user."

[1014] Example 3

[1015] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1016] With traditional customer relationship management systems, it was difficult to properly identify and efficiently respond to a company's signs of cancellation and upselling opportunities. Customer success managers also had to manually create customer lists and consider how to contact them, which was time-consuming and labor-intensive. This reduced the efficiency of customer support and potentially led to missed revenue opportunities for the company.

[1017] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1018] In this invention, the server includes means for accumulating all sales activities, customer success and customer support response history, contract status, etc. in a customer relationship management system, means for creating a health score using generative artificial intelligence, means for extracting a group of companies that meet conditions such as signs of churn, changes in circumstances, and upselling, means for generating a "customer list to contact today, this week, and this month" for the customer success manager and drafting emails and content, means for the customer success manager to check, distribute, and respond, means for setting the timing of the next contact after entering the results, means for collecting data from a database management system, means for analyzing the data using a generative artificial intelligence model, means for executing a filtering function to extract a group of companies that meet specific conditions, means for generating email drafts using templates, and means for automatically setting the timing of the next contact. This enables appropriate identification of signs of churn and upselling opportunities for companies and efficient response.

[1019] "Sales activities" are a series of activities that a company undertakes to provide products and services to customers.

[1020] "Customer Success" is the activity of helping customers get the most out of products and services and achieve success.

[1021] "Customer support response history" is a record of responses to customer inquiries and problems.

[1022] "Contract status" is information indicating the status and content of the contract concluded between the company and the customer.

[1023] A "customer relationship management system" is a system for managing relationships with customers and efficiently conducting sales activities and customer support.

[1024] "Generative AI" is an AI technology that makes predictions and analyses based on data.

[1025] A "health score" is a numerical indicator of a customer's health status and satisfaction.

[1026] "Cancellation indicators" are signs that indicate the possibility that a customer may cancel a contract.

[1027] "Change in contract" refers to a customer changing the terms of their current contract.

[1028] "Upselling" refers to offering a customer a more expensive product or service.

[1029] A "Customer Success Manager" is a person whose role is to support customer success and manage customer relationships.

[1030] A "database management system" is a system for efficiently managing data and quickly obtaining necessary information.

[1031] A "generative artificial intelligence model" is a specific implementation of artificial intelligence that makes predictions and analyses based on data.

[1032] The "filtering function" is a function that selects data based on specific conditions.

[1033] A "template" is a template used when creating emails or content.

[1034] "Next contact timing" refers to the next time you should contact the customer.

[1035] This invention is a system for accurately identifying signs of cancellation and upselling opportunities for a company and responding efficiently. This system includes elements of a server, a terminal, and a user, and operates as follows.

[1036] Hardware and software used

[1037] Hardware: Servers, terminals

[1038] Software: Generative AI models, database management systems (e.g., MySQL), email sending systems (e.g., SendGrid)

[1039] Data collection and analysis

[1040] The server collects data such as the company's sales activities, customer success and customer support response history, and contract status from a database management system. The server periodically accesses the database to obtain the latest data. Specifically, the server executes SQL queries to extract the necessary data and loads it into memory.

[1041] Next, the server uses the collected data to analyze the company's churn predictions and upselling potential using a generative AI model. The server inputs the data into the generative AI model and obtains prediction results. Specifically, the server runs a Python script and calls the generative AI model (e.g., TensorFlow or PyTorch) to make predictions.

[1042] Extraction of company groups

[1043] The server executes a filtering function that automatically extracts a group of companies that meet specific conditions based on the analysis results. The server filters the data based on the conditions and generates a list of target companies. Specifically, the server lists the IDs of companies that meet the conditions and saves the list in a database.

[1044] Contact list and content generation

[1045] The server then plans appropriate timing and content for contacting the extracted group of companies. Based on the plan, the server generates a "customer list to contact today, this week, and this month" and creates draft emails and content. Specifically, the server uses templates to generate email drafts and saves them in a database along with the list.

[1046] CSM Delivery and Automation

[1047] Using a terminal, the user (customer success manager) checks the generated contact list and content draft and distributes them as necessary. After entering the results, the user uses the function to automatically set the timing of the next contact. Specifically, the user checks the list through the terminal interface and clicks the send email button. After sending, the server automatically sets the timing of the next contact.

[1048] Specific examples

[1049] Example of extracting a group of companies:

[1050] Extract companies with a health score of 50 or less.

[1051] Extract companies that have been in business for more than 90 days since their most recent contract renewal.

[1052] Example prompt sentence:

[1053] List companies with a health score of 50 or below.

[1054] Please list companies whose most recent contract renewal date is more than 90 days ago.

[1055] Generate a list of customers to contact today.

[1056] Generate a list of customers to contact this week.

[1057] Generate a list of customers to contact this month.

[1058] This system allows customer success managers to efficiently handle customer inquiries and to detect signs of potential cancellations and upsell opportunities without missing any. The flow of the identification process in the third embodiment will be described with reference to FIG. 15.

[1059] Step 1:

[1060] The server collects data such as the company's sales activities, customer success and customer support response history, and contract status from a database management system (e.g., MySQL). The server periodically accesses the database to obtain the latest data. Specifically, the server executes SQL queries to extract the necessary data and loads it into memory.

[1061] Input: Company data stored in a database management system

[1062] Output: Company data loaded into memory

[1063] Step 2:

[1064] The server uses a generative AI model based on the collected data to analyze the company's signs of churn and the possibility of upselling. The server inputs the data into the generative AI model and obtains the prediction results. Specifically, the server runs a Python script and calls the generative AI model (e.g., TensorFlow or PyTorch) to make predictions.

[1065] Input: Company data loaded into memory

[1066] Output: Predictions made by the generative AI model

[1067] Step 3:

[1068] The server executes a filtering function that automatically extracts a group of companies that meet specific conditions based on the analysis results. The server filters the data based on the conditions and generates a list of target companies. Specifically, the server lists the IDs of companies that meet the conditions and saves the list in a database.

[1069] Input: Prediction results from a generative AI model

[1070] Output: A list of companies that match the criteria

[1071] Step 4:

[1072] The server then plans appropriate timing and content for contacting the extracted group of companies. Based on the plan, the server generates a "customer list to contact today, this week, and this month" and creates draft emails and content. Specifically, the server uses templates to generate email drafts and saves them in a database along with the list.

[1073] Input: A list of companies that match the criteria

[1074] Output: Contact list and email draft

[1075] Step 5:

[1076] Using a terminal, the user (customer success manager) checks the generated contact list and content draft and distributes them as necessary. After entering the results, the user uses the function to automatically set the timing of the next contact. Specifically, the user checks the list through the terminal interface and clicks the send email button. After sending, the server automatically sets the timing of the next contact.

[1077] Input: Contact list and email drafts

[1078] Output: Email sent and next contact timing setting

[1079] (Application example 3)

[1080] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1081] With conventional customer management systems, it was difficult to accurately predict customer churn and upsell opportunities, making it difficult to contact customers at the right time. Furthermore, creating customer lists to contact and drafting emails was a time-consuming and labor-intensive process.

[1082] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes: means for storing all data such as sales activities, customer success and customer support response history, and contract status in CRM; means for creating a health score using generative AI; means for extracting a group of companies that meet conditions such as signs of churn, time changes, and upselling; means for generating a "customer list to contact today, this week, and this month" for the CSM and drafting emails and content; means for the CSM to check, distribute, and respond; means for setting the timing of the next contact after entering the results; means for predicting signs of churn based on customer data and generating a list of customers to contact; and means for automatically drafting emails and sending emails using an SMTP server. This makes it possible to accurately predict signs of churn and upselling opportunities and contact customers at the appropriate time.

[1083] "Sales activities" are a series of activities that a company undertakes to sell its products and services.

[1084] "Customer Success" is the activity of helping customers get the most out of products and services and achieve success.

[1085] "Customer support response history" is a record of responses to customer inquiries and problems.

[1086] "Contract status" is information indicating the state of the contract with the customer and the status of its fulfillment.

[1087] "CRM" stands for Customer Relationship Management System, a software for managing relationships with customers.

[1088] "Generative AI" is artificial intelligence that generates new information and predictions based on data.

[1089] A "health score" is a numerical indicator of a customer's health status and satisfaction.

[1090] "Cancellation indicators" are signs that indicate the possibility that a customer may cancel a contract.

[1091] "Kihen" is an abbreviation for "functional change" and refers to a change in the functionality of a product or service.

[1092] "Upselling" is a sales technique that offers customers more expensive products or additional services.

[1093] A "group of companies" is a collection of multiple companies that meet certain conditions.

[1094] "CSM" stands for Customer Success Manager, a person whose role is to support customer success.

[1095] A "customer list" is a list of customers who need to be contacted or dealt with.

[1096] "Email and Content Draft" means a draft of an email or content to be sent to a customer.

[1097] An "SMTP server" is a server for sending emails.

[1098] The system for implementing this invention has the following configuration. First, the server stores all data, such as sales activities, customer success and customer support response history, and contract status, in CRM. Next, generative AI is used to create a health score based on the information stored in CRM. A group of companies that meet certain conditions is extracted, such as companies with a health score below a certain threshold or companies that have been in the business for a certain period of time since their most recent contract renewal.

[1099] Furthermore, the server generates a "list of customers to contact today, this week, and this month" for the CSM and automatically creates draft emails and content. The CSM checks these, distributes them, and responds. After entering the results, the timing of the next contact is also automatically set.

[1100] Specifically, the server predicts signs of churn based on customer data and generates a list of customers to contact. It automatically creates email drafts and sends them using an SMTP server. This makes it possible to accurately predict signs of customer churn and upselling opportunities, and contact customers at the appropriate time.

[1101] The hardware used is a server or PC for data processing and email sending. The software used is Python, pandas, scikit-learn, and smtplib. These software are used to read data, calculate health scores, generate customer lists, draft emails, and send emails.

[1102] For example, customer data may include information such as:

[1103] Customer name

[1104] Purchase frequency (purchase_frequency)

[1105] Average Order Value (average_order_value)

[1106] Last purchase date (last_purchase_date)

[1107] Last contact date (last_contact_date)

[1108] Email address

[1109] Example prompts to input to a generative AI model:

[1110] Write a Python program that uses customer data to predict which customers are likely to churn and generate a list of customers to contact. The program should include the following steps:

[1111] 1. Loading customer data

[1112] 2. Calculating your Health Score

[1113] 3. Identifying customers who are at risk of churn

[1114] 4. Create a contact list

[1115] 5. Drafting an email

[1116] 6. Sending emails

[1117] In this way, a system is provided that enables the operator of a mail-order site to predict signs of customer cancellation and contact the customer at the appropriate time.

[1118] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1119] Step 1:

[1120] The server stores data such as sales activities, customer success and customer support response history, and contract status in CRM. The input is historical data on various sales activities and customer support, and the output is data stored in CRM. Specifically, the data entered by sales representatives and customer support representatives is automatically saved in CRM.

[1121] Step 2:

[1122] The server uses generative AI to create health scores based on data stored in the CRM. The input is customer data stored in the CRM, and the output is each customer's health score. Specifically, it reads the data using pandas and calculates the health score using a random forest model using scikit-learn.

[1123] Step 3:

[1124] The server extracts companies whose health scores are below a certain threshold or whose most recent contract renewal date has been a certain amount of time. The input is the health score and contract renewal date, and the output is a list of companies that are likely to cancel. Specifically, it filters out companies whose health scores are below a threshold and lists companies whose contract renewal date has been longer than a certain amount of time.

[1125] Step 4:

[1126] The server generates a "list of customers to contact today, this week, and this month" for the CSM, and automatically creates emails and content drafts. The input is a list of companies that are at risk of churn, and the output is a list of customers to contact and email drafts. Specifically, it sets the priority of contact based on the list of companies that are at risk of churn, and creates drafts using email templates.

[1127] Step 5:

[1128] The CSM checks the customer list and email draft generated by the server, makes any necessary corrections, and delivers them. The input is the customer list and email draft generated by the server, and the output is the corrected email draft and delivery results. Specifically, the CSM checks the email draft, corrects the content as necessary, and sends the email using the SMTP server.

[1129] Step 6:

[1130] The server automatically sets the timing of the next contact based on the results entered by the CSM. The input is the distribution result entered by the CSM, and the output is the timing of the next contact. Specifically, the server analyzes the distribution result and calculates and sets the timing of the next required contact.

[1131] In this way, the server can accurately predict customer churn and upsell opportunities, and contact customers at the right time.

[1132] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1133] "Example 1"

[1134] One embodiment of the present invention is a system that incorporates an emotion engine. This system recognizes a user's emotions and reflects the results in the calculation of a health score. Specifically, the system extracts the user's emotions from communication with the user (e.g., phone calls, emails, chats, etc.) and quantifies those emotions. This quantified emotion data is used to calculate the health score.

[1135] "Example 2"

[1136] Furthermore, the emotion recognition results from the emotion engine are also used to generate a "customer list to contact today, this week, this month" for the CSM. Specifically, if a user is judged to have a high probability of canceling or upselling based on their emotion data, that user will be included in the list with priority. This allows the CSM to respond efficiently while taking into account the user's emotions. "Example 3"

[1137] Furthermore, the emotion engine also has the ability to automatically set the next timing of contact based on the user's emotional data. For example, if it determines that the user's emotions are unstable, it will set the next timing of contact to allow for an earlier follow-up. This increases user satisfaction and reduces the risk of cancellation.

[1138] The processing flow of each embodiment will be described below.

[1139] "Example 1"

[1140] Step 1: Extract the user's emotions from communication with the user (e.g., phone calls, emails, chats, etc.).

[1141] Step 2: Quantify the extracted emotions.

[1142] Step 3: Use the quantified emotional data to calculate a health score.

[1143] "Example 2"

[1144] Step 1: Based on user sentiment data, determine whether the user is likely to churn or upsell.

[1145] Step 2: Based on the results of the judgment, the user is prioritized for inclusion in the "Customer Contact List for Today, This Week, and This Month."

[1146] "Example 3"

[1147] Step 1: Automatically set the next contact timing based on the user's emotional data.

[1148] Step 2: For example, if the user's emotions are determined to be unstable, the next contact timing is set to allow for early follow-up.

[1149] Example 1

[1150] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1151] With conventional customer relationship management systems, it was difficult to centrally manage information such as sales activities, customer support response history, and contract status, making it difficult to accurately evaluate customer satisfaction and risk. Furthermore, because it was not possible to grasp user sentiment in real time and calculate a health score based on that, it was often the case that signs of cancellation and upselling opportunities were overlooked. This created challenges for improving customer response efficiency and customer satisfaction.

[1152] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1153] In this invention, the server includes: means for storing all sales activities, customer success and customer support response history, contract status, etc. in a customer relationship management system; means for creating a health score using generative artificial intelligence; means for extracting a group of companies that meet conditions such as signs of cancellation, changes in circumstances, and upselling; means for generating a "customer list to contact today, this week, and this month" for the customer success manager and drafting emails and content; means for the customer success manager to check, distribute, and respond; means for setting the timing of the next contact after entering the results; means for extracting and quantifying emotions from communication data with the user; and means for calculating a health score based on the quantified emotion data. This enables centralized management of customer information and calculation of a health score based on the user's emotions.

[1154] "Sales activities" are a series of actions that a company takes to promote sales and conduct business with customers.

[1155] "Customer success" is the process of helping customers get the most out of products and services and achieve success.

[1156] "Customer support response history" is a record of responses to customer inquiries and problems.

[1157] "Contract status" is information indicating the content and progress of the contract with the customer.

[1158] A "customer relationship management system" is an information system for managing relationships with customers and streamlining sales activities and support responses.

[1159] "Generative AI" is AI that has the ability to generate new information and predictions based on data.

[1160] A "health score" is an indicator used to evaluate customer satisfaction and risk.

[1161] "Cancellation indicators" are signs that indicate the possibility that a customer may cancel a contract.

[1162] "Change in contract" refers to a change made by the customer to the contents of the contract.

[1163] "Upselling" is a sales technique that offers customers more expensive products or services.

[1164] A "Customer Success Manager" is a person responsible for supporting customer success and improving customer satisfaction.

[1165] "Extracting emotions" means identifying and extracting emotions from user communication data.

[1166] "Quantifying" means expressing the extracted emotions numerically.

[1167] This invention is a system that centrally manages customer information such as sales activities, customer success and customer support response history, and contract status, and calculates a health score based on the user's emotions. This system is realized by combining a customer relationship management (CRM) system and generative artificial intelligence (AI).

[1168] System configuration

[1169] The system consists of the following major components:

[1170] 1. Terminal: A device used by sales or customer support representatives that provides an interface for entering customer information.

[1171] 2. Server: Collects, converts, and stores customer information, extracts and quantifies emotional data, and calculates health scores.

[1172] 3. CRM system: Provides a database and interface for centrally managing customer information.

[1173] 4. Generative AI model: An artificial intelligence model that analyzes user emotions and calculates a health score.

[1174] Hardware and software used

[1175] Device: A device such as a computer, tablet, or smartphone.

[1176] Server: A server with high-performance computing power (e.g., a cloud server).

[1177] CRM system: A customer relationship management system such as Salesforce or HubSpot.

[1178] Generative AI models: Natural language processing models such as BERT and GPT-3.

[1179] Speech recognition software: such as Google Speech-to-Text.

[1180] Data processing and calculation

[1181] 1. Collection of Customer Information:

[1182] The terminal collects data on sales activities, customer support response history, contract status, etc. For example, a sales representative inputs visit records and business negotiation details, while a customer support representative inputs response history.

[1183] 2. Data conversion and storage:

[1184] The server receives the JSON formatted data sent from the terminal, converts it into an SQL query, and stores it in the CRM system database.

[1185] 3. Extracting emotion data:

[1186] The server collects communication data such as phone calls, emails, and chats with users and converts it into text data using voice recognition software.

[1187] 4. Quantifying Emotional Data:

[1188] The server inputs the extracted text data into a sentiment analysis model and quantifies the type and intensity of emotions.

[1189] 5. Calculating your Health Score:

[1190] The server calculates the user's health score based on the quantified emotional data and stores it in the CRM system.

[1191] Specific examples

[1192] For example, when a user calls customer support, the following specific actions are taken:

[1193] 1. The user calls customer support.

[1194] 2. The server converts the call into text data using speech recognition software.

[1195] 3. The server inputs the converted text data into a sentiment analysis model, recognizes the emotion as "anger," and quantifies its intensity as "75 / 100."

[1196] 4. The server stores the quantified emotion data in the CRM system.

[1197] 5. The server calculates the user's health score based on the stored emotional data and stores it in the CRM system.

[1198] Prompt Sentence Examples

[1199] Examples of prompts to be input to a generative AI model include:

[1200] The following is a transcript of a conversation with a user. Please extract the user's emotions from this transcript and quantify them.

[1201] Caller response: "I'm very unhappy with your recent service. It's been too slow."

[1202] Expected Output: Emotion: Anger, Intensity: 75 / 100

[1203] In this way, the system recognizes the user's emotions and calculates a health score based on the data accumulated in the CRM.

[1204] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1205] Step 1:

[1206] The terminal collects customer information such as sales activities, customer support response history, and contract status. Sales representatives and customer support representatives enter data using a CRM application as input. Specifically, the terminal collects information such as the date and time of visit, the location of the visit, the details of the business negotiation, the date and time of support response, the response details, the response results, the contract start date, the contract end date, and the contract details. As output, this data is sent to the server in JSON format.

[1207] Step 2:

[1208] The server receives customer information data in JSON format sent from the terminal. As input, it receives the JSON data sent from the terminal. Specifically, the server converts the received data into an SQL query and inserts it into the CRM system database. As output, the converted data is saved in the CRM system.

[1209] Step 3:

[1210] Users communicate with customer support via phone, email, chat, etc. The user's call content and text data are sent to the server as input. Specifically, the server uses voice recognition software to convert the call content into text data and directly collects email and chat text data. Text data is generated as output.

[1211] Step 4:

[1212] The server inputs the extracted text data into a sentiment analysis model to quantify the emotion. The text data generated in step 3 is input into the sentiment analysis model. Specifically, the server uses a natural language processing model such as BERT or GPT-3 to obtain the type of emotion (joy, anger, sadness, etc.) and its intensity as a numerical value. The output is quantified emotion data.

[1213] Step 5:

[1214] The server calculates the user's health score based on the quantified emotion data. As input, it uses the quantified emotion data generated in step 4. Specifically, the server stores the quantified emotion data in the CRM system and runs an algorithm to calculate a health score based on the stored data. As output, the calculated health score is stored in the CRM system.

[1215] Step 6:

[1216] The server extracts a group of companies that meet certain conditions, such as signs of churn, changes in behavior, and upselling. As input, it uses customer information and health scores stored in the CRM system. Specifically, the server filters the group of companies based on specific conditions and extracts companies that are likely to be signs of churn or upselling. As output, a list of the extracted group of companies is generated.

[1217] Step 7:

[1218] The server generates a "list of customers to contact today, this week, and this month" for the customer success manager and creates email and content drafts. As input, it uses the list of companies generated in step 6 and the customer information stored in the CRM system. Specifically, the server uses a generative AI model to create a list of customers to contact and draft emails and content for them. As output, a customer list and drafts are generated.

[1219] Step 8:

[1220] The customer success manager reviews the generated customer list and draft, makes any necessary corrections, and distributes them to the customer. The customer list and draft generated in step 7 are used as input. Specifically, the customer success manager reviews the list and draft, and distributes them to the customer after making any necessary corrections. The output is the email or content that is distributed.

[1221] Step 9:

[1222] The customer success manager inputs the customer's response and results and sets the timing of the next contact. As input, the customer's response and results are entered into the CRM system. As a specific operation, the customer success manager inputs the results and sets the timing of the next contact. As output, the timing of the next contact is saved in the CRM system.

[1223] (Application example 1)

[1224] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1225] Conventional CRM systems accumulate data such as customer communication history and contract status, but are unable to recognize customer emotions and reflect them in health scores. This makes it difficult to grasp customer satisfaction and dissatisfaction in real time, making it difficult to follow up at the appropriate time. Furthermore, because analysis based on emotion data is not performed, there is a lack of specific feedback to improve the quality of customer support.

[1226] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes: means for storing all data, such as sales activities, customer success and customer support response history, and contract status, in a CRM; means for creating a health score using generative AI; means for extracting a group of companies that meet conditions such as signs of cancellation, changes in customer behavior, and upselling; means for generating a "customer list to contact today, this week, and this month" for the CSM and drafting emails and content; means for the CSM to check, distribute, and respond; means for setting the timing of the next contact after entering the results; means for recognizing emotions from communication with the user and storing the emotion data in the CRM; means for calculating a health score based on the emotion data; means for quantifying the emotion data and sending it to the CRM; and means for calculating a health score based on customer data obtained from the CRM. This enables real-time understanding of customer emotions and appropriate follow-up.

[1227] "Sales activities" are a series of activities a company undertakes to sell its products and services.

[1228] "Customer Success" is the support provided to help customers get the most out of products and services and achieve success.

[1229] "Customer support response history" is a record of responses to customer inquiries and problems.

[1230] "Contract status" is information indicating the progress and content of a contract with a customer.

[1231] "CRM" stands for Customer Relationship Management System, a system for managing relationships with customers.

[1232] "Generative AI" is artificial intelligence that generates new information and predictions based on data.

[1233] A "health score" is a numerical indicator of customer satisfaction and health status.

[1234] "Cancellation warning signs" are signs that indicate the possibility that a customer may cancel a service or contract.

[1235] "Kihen" is an abbreviation for "functional change" and refers to a change in the functionality of a product or service.

[1236] "Upselling" is a sales technique that offers customers more expensive products or services.

[1237] A "group of companies" is a collection of multiple companies that meet certain conditions.

[1238] "CSM" stands for Customer Success Manager, a person whose role is to support customer success.

[1239] "Emotion recognition" is a technology that analyzes and recognizes a user's emotions.

[1240] "Emotion data" is data that quantifies the user's emotions.

[1241] "Follow-up" refers to any additional actions or contact you take to maintain and strengthen your relationship with a customer.

[1242] The system for implementing this invention has the following configuration. The server has a means for storing all sales activities, customer success and customer support response history, contract status, etc. in CRM. This allows for centralized management of various communications and transaction histories with customers.

[1243] Next, the server has a means to create a health score using generative AI. The generative AI calculates the health score based on the information and emotional data accumulated in the CRM. The emotional data is acquired by recognizing emotions from communication with the user, converting the emotional data into numerical values, and sending them to the CRM.

[1244] Furthermore, the server has a means to extract a group of companies that meet certain conditions, such as signs of cancellation, feature changes, upselling, etc. This makes it possible to classify customers based on specific conditions and provide appropriate responses.

[1245] For CSMs, a means is provided to generate a "list of customers to contact today, this week, and this month" and draft emails and content. CSMs can then review these and distribute or respond to them. After entering the results, a means is also provided to set the timing of the next contact.

[1246] A concrete example of this system is an "emotion-aware customer support app" that is installed on smartphones. This app recognizes users' emotions through chat, phone, and email interactions with the user, and stores that data in CRM. Furthermore, it calculates the user's health score based on the emotional data and provides feedback to improve the quality of customer support.

[1247] The hardware used includes a smartphone. The software used is Python, an emotion recognition library (EmotionRecognizer), and a CRM API library (CRM_API). The emotion recognition engine receives the user's text data as input, recognizes their emotions, and quantifies the results. The quantified emotion data is sent to the CRM system via the CRM API. The CRM system calculates a health score based on the accumulated data and provides information for appropriate follow-up.

[1248] For example, if a user sends a chat message saying, "There was a problem with my recent order," this message is input into an emotion recognition engine, which recognizes the user's emotion as "dissatisfied." This emotion is quantified and stored in the CRM. Based on the accumulated data, the user's health score is calculated and feedback is provided to improve the quality of customer support.

[1249] An example of a prompt is as follows:

[1250] "Please input the user's chat message into the emotion recognition engine and recognize the emotion. Then, convert the recognized emotion into a numerical value and send it to the CRM. Then, calculate a health score based on the customer data obtained from the CRM."

[1251] The above is an embodiment of the present invention.

[1252] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1253] Step 1:

[1254] A user accesses a customer support app on their smartphone and initiates a chat, email, or phone call. Input includes the user's text message and voice data, which is sent to an emotion recognition engine.

[1255] Step 2:

[1256] The server uses an emotion recognition engine (EmotionRecognizer) to recognize emotions from the user's text messages and voice data. The input includes the user's text messages and voice data. The emotion recognition engine uses natural language processing technology to analyze emotions and quantify them. The output is the recognized emotions and their quantified data.

[1257] Step 3:

[1258] The server sends the quantified emotion data to the CRM system through the CRM API. The input contains the quantified emotion data. The CRM system stores the received emotion data in the customer profile. The output is an updated customer profile.

[1259] Step 4:

[1260] The server uses generative AI to calculate a health score based on customer data and emotional data stored in the CRM. The input includes customer data and emotional data stored in the CRM. The generative AI analyzes this data and calculates the customer's health score. The output is the calculated health score.

[1261] Step 5:

[1262] The server extracts a group of companies that meet certain conditions, such as signs of cancellation, feature changes, and upselling. The input includes customer data and health scores stored in the CRM. The server uses this data to classify and extract companies based on specific conditions. The output is a list of the extracted companies.

[1263] Step 6:

[1264] The server generates a "list of customers to contact today, this week, and this month" for the CSM and drafts emails and content. The input includes customer data and health scores stored in the CRM. The server generates a list of customers to contact based on this data and drafts emails and content. The output is the generated customer list and drafts.

[1265] Step 7:

[1266] The CSM checks the generated customer list and draft, makes any necessary corrections, and distributes and responds to them. The input includes the generated customer list and draft. The CSM contacts the customer based on these and responds to them. The output is the results of contacting and responding to the customer.

[1267] Step 8:

[1268] The CSM inputs the response results into the CRM system and sets the timing of the next contact. The inputs include the response results and the timing of the next contact. The CRM system updates the customer profile based on this data and sets the timing of the next contact. The output is the updated customer profile and the timing of the next contact.

[1269] Example 2

[1270] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1271] With traditional customer management systems, it is difficult to quantitatively evaluate a customer's contract renewal probability or the possibility of upselling, leaving customer success managers with a lack of information to respond efficiently. Furthermore, because they are unable to take user sentiment data into account, appropriate responses to customers who are likely to cancel or upsell can be delayed. This can lead to lower customer satisfaction and lost business opportunities.

[1272] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1273] In this invention, the server includes a means for accumulating all sales activity, customer success and customer support response history, contract status, etc. in a customer management system; a means for creating a health score using generative artificial intelligence; a means for extracting groups of companies that meet conditions such as signs of churn, changes in behavior, and upselling; a means for generating a "customer list to contact today, this week, and this month" for the customer success manager and drafting emails and content; a means for the customer success manager to review, distribute, and respond; a means for setting the timing of the next contact after entering the results; and a means for analyzing user emotion data using an emotion engine to prioritize users who are likely to churn or upsell. This allows for quantitative evaluation of customer contract renewal probability and upsell potential, enabling the customer success manager to respond efficiently. Furthermore, by taking user emotion data into consideration, appropriate responses can be made quickly to customers who are likely to churn or upsell.

[1274] "Sales activities" are a series of activities that a company undertakes to sell products and services to customers.

[1275] "Customer Success" is the activity of helping customers get the most out of products and services and achieve success.

[1276] "Customer support response history" is a record of responses to customer inquiries and problems.

[1277] "Contract status" is information indicating the status and content of the contract concluded between the customer and the company.

[1278] A "customer management system" is a system that centrally manages information about customers and supports sales activities and customer support.

[1279] "Generative AI" is an artificial intelligence technology that generates new information and predictions based on data.

[1280] A "health score" is an indicator used to quantitatively evaluate a customer's contract renewal probability and upselling potential.

[1281] "Cancellation indicators" are signs that indicate the possibility that a customer may cancel a contract.

[1282] "Changes" refer to changes in the customer's situation or behavior.

[1283] "Upselling" is a sales technique that offers customers more expensive products or additional services.

[1284] A "Customer Success Manager" is a person who works to support customer success and improve customer satisfaction.

[1285] An "emotion engine" is a technology for analyzing a user's emotional data and evaluating their emotional state.

[1286] "User emotion data" refers to information about emotions obtained from the content of a user's emails and chats, social media posts, etc.

[1287] A "customer list" is a list of customers selected based on specific conditions.

[1288] This invention is a system that uses generative artificial intelligence to calculate customer health scores based on data accumulated in a customer management system, enabling customer success managers to respond efficiently. It also uses an emotion engine to analyze user emotion data and prioritize users who are likely to cancel or upsell.

[1289] Hardware and software used

[1290] The server uses the following hardware and software:

[1291] Hardware: A server with a powerful processor, sufficient memory, and ample storage.

[1292] Software: Customer Relationship Management (CRM), generative AI models (e.g., TensorFlow, PyTorch), sentiment analysis APIs (e.g., IBM Watson, Microsoft Azure)

[1293] Data processing and calculation

[1294] The server processes and calculates data in the following procedure.

[1295] 1. Data collection: The server collects customer data (purchase history, inquiry history, website visit history, etc.) stored in the customer management system.

[1296] 2. Data preprocessing: The server preprocesses the collected data, including imputing missing values, normalizing data, and encoding categorical data.

[1297] 3. Applying the model: The server inputs the preprocessed data into a generative artificial intelligence model, which is built using TensorFlow or PyTorch.

[1298] 4. Calculation of health score: The server calculates each customer's health score based on the output of the AI ​​model. The health score is an index used to quantitatively evaluate the customer's contract renewal probability and upsell potential.

[1299] 5. Emotional Data Collection: The server collects users' emotional data (emails, chat content, social media posts, etc.).

[1300] 6. Emotion data analysis: The server inputs the collected emotion data into an emotion engine, which is built using IBM Watson and Microsoft Azure emotion analysis APIs.

[1301] 7. Customer list generation: Based on the analysis results of the emotion engine, the server generates a list of customers to contact today, this week, and this month for the customer success manager. If a user is determined to have a high probability of canceling or upselling, that user will be included in the list with priority.

[1302] Specific examples

[1303] Customer A: Extensive purchase history and many positive recent inquiries. Health score: 80.

[1304] Customer B: Has expressed dissatisfaction in recent emails and is likely to churn. Based on the sentiment engine analysis, this customer is included in the "Customers to Contact Today" list.

[1305] Prompt Sentence Examples

[1306] "Calculate a customer health score based on the data stored in your CRM system. Then use a sentiment engine to analyze user sentiment data and generate a list of customers for your customer success managers to contact."

[1307] In this way, the server is a system that uses CRM data and sentiment data to calculate a customer health score and enable customer success managers to respond efficiently.

[1308] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1309] Step 1:

[1310] The server collects customer data stored in the customer management system. The input includes customer purchase history, inquiry history, website visit history, etc. Specifically, the server obtains this data through API and stores it in an internal database. The output is the collected raw data.

[1311] Step 2:

[1312] The server preprocesses the collected data. The input includes the raw data collected in step 1. Specific operations performed by the server include imputing missing values, normalizing data, and encoding categorical data. The output is the preprocessed, clean data.

[1313] Step 3:

[1314] The server inputs the preprocessed data into a generative AI model. The input includes the data preprocessed in step 2. Specifically, the server inputs the data into an AI model built using TensorFlow or PyTorch and executes inference on the model. The output is a health score for each customer.

[1315] Step 4:

[1316] The server calculates a health score for each customer based on the output of the AI ​​model. The input includes the model output obtained in step 3. Specifically, the server calculates the health score and assigns a score to each customer. The output is a health score for each customer.

[1317] Step 5:

[1318] The server collects user emotional data. Inputs include users' emails, chat messages, and social media posts. Specifically, the server retrieves emotional data from these data sources and stores it in an internal database. The output is the collected emotional data.

[1319] Step 6:

[1320] The server inputs the collected emotion data into the emotion engine. The input includes the emotion data collected in step 5. Specifically, the server analyzes the emotion data using IBM Watson or Microsoft Azure emotion analysis APIs. The output is the analyzed emotion data.

[1321] Step 7:

[1322] Based on the analysis results of the emotion engine, the server generates a "list of customers to contact today, this week, and this month" for the customer success manager. The input includes the health score obtained in step 4 and the emotion data obtained in step 6. Specifically, the server integrates this data and prioritizes users who are likely to churn or upsell in the list. The output is a customer list for the customer success manager.

[1323] In this way, the server is a system that collects, pre-processes, and analyzes data at each step, ultimately enabling customer success managers to respond efficiently.

[1324] (Application example 2)

[1325] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1326] Traditional CRM systems lacked the means to quantitatively evaluate customer contract renewal probability and upselling potential, making it difficult to implement efficient marketing measures that took into account customer sentiment data. Additionally, there was a lack of tools to check customer health scores in real time and take appropriate action.

[1327] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1328] In this invention, the server includes: means for accumulating all sales activities, customer success and customer support response history, contract status, etc. in CRM; means for creating a health score using generative AI; means for extracting groups of companies that meet conditions such as signs of cancellation, changes in circumstances, and upselling; means for generating a "customer list to contact today, this week, and this month" for the CSM and drafting emails and content; means for the CSM to check, distribute, and respond; means for setting the timing of the next contact after entering the results; means for analyzing customer emotion data using an emotion engine and generating a list of customers to contact; and means for checking health scores in real time through an application installed on a smartphone and implementing marketing measures. This enables efficient marketing measures that take customer emotions and health scores into account.

[1329] "Sales activities" are a series of activities a company undertakes to sell its products and services.

[1330] "Customer Success" is the activity of helping customers get the most out of products and services and achieve success.

[1331] "Customer support response history" is a record of responses to customer inquiries and problems.

[1332] "Contract status" is information indicating the progress and content of a contract with a customer.

[1333] "CRM" stands for Customer Relationship Management System, a system for managing relationships with customers.

[1334] "Generative AI" is artificial intelligence that generates new information and predictions based on data.

[1335] A "health score" is an indicator that quantitatively evaluates a customer's probability of contract renewal and the possibility of upselling.

[1336] "Cancellation indicators" are signs that indicate the possibility that a customer may cancel a contract.

[1337] "Kihen" is an abbreviation for "functional change" and refers to a change in the functionality of a product or service.

[1338] "Upselling" is a sales technique that offers customers more expensive products or services.

[1339] A "group of companies" is a collection of multiple companies that meet certain conditions.

[1340] "CSM" stands for Customer Success Manager, a person whose role is to support customer success.

[1341] The "emotion engine" is an artificial intelligence for analyzing customer emotional data.

[1342] A "smartphone" is a mobile phone that can connect to the Internet and use applications.

[1343] An "application" is software that provides specific functions or services.

[1344] A "marketing initiative" is a series of activities aimed at promoting a product or service.

[1345] The system for implementing this invention is configured as follows: The server has a means for storing all sales activities, customer success and customer support response history, contract status, etc. in CRM, which allows detailed customer data to be managed in a unified manner.

[1346] Next, the server has a means to create a health score using generative AI. This generative AI quantitatively evaluates the customer's contract renewal probability and upselling potential based on the information accumulated in the CRM. Specifically, it uses Python's Scikit-learn library to build a model using RandomForestClassifier and calculates the health score.

[1347] Furthermore, the server has the means to extract groups of companies that meet certain conditions, such as signs of cancellation, changes in schedule, and upselling. This makes it possible to classify customers based on specific conditions and implement appropriate marketing measures.

[1348] The server also generates a list of customers to be contacted today, this week, and this month for the CSM, and has a means for drafting emails and content. This allows the CSM to efficiently respond to customers. It also includes a means for the CSM to check, distribute, and respond to emails, and a means for setting the timing of the next contact after entering the results.

[1349] The server also includes means for analyzing customer sentiment data using an emotion engine to generate a list of customers to contact, the emotion engine using a sentiment analysis model from the Transformers library to analyze customer feedback.

[1350] Finally, a smartphone application will allow marketers to check their customers' health scores in real time and implement marketing measures, enabling them to understand their customers' situations anytime, anywhere and respond quickly.

[1351] For example, if Customer A sends feedback saying, "I'm dissatisfied with the service I received recently," the sentiment engine will analyze this as a negative sentiment, and if the health score is low, the customer will be added to the priority contact list. This allows the CSM to respond quickly and resolve the customer's dissatisfaction.

[1352] An example prompt is, "Perform sentiment analysis based on customer feedback and generate a list of customers with low health scores to prioritize contact."

[1353] In this way, efficient marketing measures that take into account customer emotions and health scores become possible.

[1354] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1355] Step 1:

[1356] The server accumulates data such as sales activities, customer success and customer support response history, and contract status in the CRM. Various customer data is provided as input and saved in the CRM system. The accumulated customer data is obtained as output.

[1357] Step 2:

[1358] The server uses generative AI to create a health score based on data stored in the CRM. Customer data obtained from the CRM is provided as input, and a generative AI model (e.g., RandomForestClassifier) ​​is used to calculate a health score based on this data. The output is a health score for each customer.

[1359] Step 3:

[1360] The server extracts companies that meet certain criteria, such as signs of churn, changes in behavior, and upselling. Health scores and other customer data are provided as input, and companies that meet certain criteria are extracted based on this. The output is a list of companies that meet the criteria.

[1361] Step 4:

[1362] The server generates a "customer list to contact today, this week, and this month" for the CSM and creates email and content drafts. The extracted list of companies and customer data are provided as input, and the server generates the contact list and drafts based on this. The output is the contact list and email drafts.

[1363] Step 5:

[1364] The CSM checks the contact list and email draft generated by the server, makes any necessary corrections, and delivers them to the customer. The contact list and email draft are provided as input, and the CSM contacts the customer based on these. The output is the result of contacting the customer.

[1365] Step 6:

[1366] The server sets the timing of the next contact based on the results entered by the CSM. The input is the result of contacting the customer, and the server sets the timing of the next contact based on this. The output is the schedule for the next contact.

[1367] Step 7:

[1368] The server uses an emotion engine to analyze customer emotion data and generate a list of customers to contact. Customer feedback is provided as input, and the emotion engine (e.g., a sentiment analysis model from the Transformers library) is used to analyze the emotion data. The output is a contact list based on the emotion data.

[1369] Step 8:

[1370] Users check their health score in real time through an application installed on their smartphone and execute marketing measures. The input is the health score and contact list provided by the server, and marketing measures are planned and executed based on this. The output is the results of the executed marketing measures.

[1371] Example 3

[1372] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1373] Conventional customer relationship management systems make it difficult to centrally manage data such as sales activities, customer support history, and contract status, making it difficult to extract groups of companies based on specific criteria. Customer success managers also had to manually create contact lists and draft content, which hindered efficient customer support. Furthermore, they lacked the functionality to automatically set the next contact timing based on user sentiment data, which hindered efforts to improve customer satisfaction and reduce the risk of cancellation.

[1374] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1375] In this invention, the server includes means for storing all data such as sales activities, customer success and customer support response history, and contract status in a customer relationship management system, means for creating a health score using generative artificial intelligence, means for extracting groups of companies that meet conditions such as signs of cancellation, equipment changes, and additional sales, means for generating a "customer list to contact today, this week, and this month" for the customer success manager and drafting emails and content, means for the customer success manager to check, distribute, and respond, means for setting the timing of the next contact after entering the results, and means for automatically setting the timing of the next contact based on emotion data. This makes it possible to efficiently extract groups of companies based on the data stored in the customer relationship management system and automate the work of the customer success manager, thereby improving customer satisfaction and reducing the risk of cancellation.

[1376] "Sales activities" are a series of activities that a company undertakes to sell products and services to customers.

[1377] Customer success is the activity of helping customers get the most out of products and services and achieve their desired outcomes.

[1378] "Customer support response history" is a record of responses to customer inquiries and support requests.

[1379] "Contract status" is information indicating the content and progress of a contract concluded between a company and a customer.

[1380] A "customer relationship management system" is an information system for managing relationships with customers and efficiently conducting sales activities and customer support.

[1381] "Generative AI" is an AI technology that generates specific results based on data.

[1382] A "health score" is an index used to evaluate the status of a company or customer, and is a score calculated based on specific criteria.

[1383] A "cancellation warning sign" is a sign that indicates a high probability that a customer will cancel a contract.

[1384] "Equipment Change" refers to changing the equipment used by the customer to a new one.

[1385] "Add-on sales" refers to selling additional products or services to existing customers.

[1386] A "customer success manager" is someone whose role is to help customers get the most out of a product or service.

[1387] A "contact list" is a list of customers to be contacted within a specific time period.

[1388] "Email" means a digital message sent or received over the Internet.

[1389] A "content draft" is an early version of a message or material that you will send to a client.

[1390] "Emotion data" is data that indicates the user's emotional state and is collected from feedback, chat logs, etc.

[1391] "Next contact timing" refers to the next time you should contact the customer.

[1392] MODE FOR CARRYING OUT THE INVENTION

[1393] This invention is a system for improving the efficiency of sales activities and customer success management based on data accumulated in a customer relationship management system (CRM). A specific embodiment of this system will be described below.

[1394] Data collection and storage

[1395] The server stores data such as sales activities, customer success stories, customer support response history, and contract status in a customer relationship management system (CRM). For this purpose, a database management system (e.g., MySQL) is used. The server executes SQL queries to collect the necessary data and stores it in the CRM.

[1396] Creating a Health Score

[1397] The server uses generative artificial intelligence (AI) to create a health score based on the data stored in the CRM. This AI uses Python's Pandas library to analyze the data and calculate the health score based on a specific algorithm.

[1398] Extraction of company groups

[1399] Based on the health score and other analysis results, the server automatically extracts companies that meet certain conditions, such as signs of cancellation, equipment changes, or additional sales. For this, a data analysis tool (e.g., Python's Pandas library) is used. The server extracts companies that meet the conditions from the data frame and generates a list.

[1400] Generate contact lists and content drafts

[1401] For the selected companies, the server generates a "customer list to contact today, this week, and this month" for the customer success manager (CSM). This list is added to the schedule using the Google Calendar API. The server also uses an email template generation tool such as Mailchimp to create a draft email to send to each company.

[1402] Setting the next contact timing

[1403] After the CSM checks, distributes, and responds, the server automatically sets the timing of the next contact after entering the results. This is done using the Google Calendar API. It also has a function to automatically set the timing of the next contact based on emotional data. It uses an emotional analysis tool (e.g., IBM Watson Tone Analyzer) to analyze the user's emotions and set the timing of the next contact.

[1404] Specific examples

[1405] For example, the server extracts companies using the following prompt:

[1406] "List companies with a health score of 50 or less."

[1407] Also, when creating a contact list, use the following prompt:

[1408] "Generate a list of companies I need to contact this week."

[1409] To set the next contact timing based on emotion data, use the following prompt:

[1410] "If the user's emotions are unstable, schedule your next contact earlier."

[1411] The above is a specific embodiment for carrying out the present invention. This system makes it possible to improve customer relationship management efficiency, improve customer satisfaction, and reduce the risk of cancellation. The flow of the identification process in the third embodiment will be described with reference to FIG. 21.

[1412] System program processing flow

[1413] Step 1: Data collection and storage

[1414] The server stores data such as sales activities, customer success and support history, and contract status in a customer relationship management system (CRM).

[1415] Input: Sales activity data, customer support history, contract information

[1416] Output: Data stored in CRM

[1417] Specific operation: The server executes SQL queries to gather the necessary data and save it to the CRM. For example, it retrieves sales activity data from a MySQL database and saves it to the CRM.

[1418] Step 2: Create a Health Score

[1419] The server uses generative artificial intelligence (AI) to create a health score based on the data stored in the CRM.

[1420] Input: Data stored in CRM

[1421] Output: Health score

[1422] How it works: The server analyzes the data using Python's Pandas library and calculates a health score based on a specific algorithm, such as each company's sales activities and customer support history.

[1423] Step 3: Extracting companies

[1424] Based on the health score and other analytical results, the server automatically extracts a group of companies that meet the conditions for cancellation, equipment changes, additional sales, etc.

[1425] Input: Health score, other analysis results

[1426] Output: A list of companies that match the criteria

[1427] Specific operation: The server uses a data analysis tool (e.g., Python's Pandas library) to extract companies that match the criteria from the data frame and generate a list. For example, it extracts companies with a health score of 50 or less.

[1428] Step 4: Generate a contact list

[1429] The server generates a "list of customers to contact today, this week, and this month" for the customer success manager (CSM) for the extracted group of companies.

[1430] Input: A list of companies that match the criteria

[1431] Output: Contact list

[1432] Specific operation: The server uses the Google Calendar API to add a contact list to a calendar. For example, it generates a list of companies that need to be contacted this week and adds it to Google Calendar.

[1433] Step 5: Create a content draft

[1434] The server generates a draft email to send to each company based on the contact list.

[1435] Input: Contact list

[1436] Output: Email draft

[1437] Specific operation: The server uses an email template generation tool such as Mailchimp to create a draft of the email to be sent to each company. For example, it uses the Mailchimp API to create a draft of the email to be sent to each company.

[1438] Step 6: Set the next contact timing

[1439] After the CSM checks, distributes, and responds, the server automatically sets the timing for the next contact after the results are entered.

[1440] Input: CSM confirmation results, distribution results

[1441] Output: Next contact timing

[1442] Specific operation: The server uses the Google Calendar API to add the next contact timing to the calendar. For example, after the CSM confirms, the next contact timing is automatically added to Google Calendar.

[1443] Step 7: Determine next contact timing based on emotional data

[1444] The server automatically sets the next contact timing based on the emotion data.

[1445] Input: Emotion data

[1446] Output: Next contact timing

[1447] Specific operation: The server analyzes the user's emotions using a sentiment analysis tool (e.g., IBM Watson Tone Analyzer) and sets the timing of the next contact. For example, if the user's emotions are judged to be unstable, it sets a follow-up contact three days later.

[1448] The above are the specific processing steps of the program of this system.

[1449] (Application example 3)

[1450] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1451] In traditional customer management systems, data analysis to predict signs of churn and upselling opportunities is often done manually, resulting in inefficiency. It is also difficult to set the timing of follow-ups that take into account customer sentiment data, which means that improvements in customer satisfaction and reductions in the risk of churn are not being fully implemented. Furthermore, automation of customer management using smartphones has not progressed, requiring rapid on-site responses.

[1452] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1453] In this invention, the server includes: means for accumulating all sales activities, customer success and customer support response history, contract status, etc. in CRM; means for creating a health score using generative AI; means for extracting groups of companies that meet conditions such as signs of cancellation, changes in schedule, and upselling; means for generating a "customer list to contact today, this week, and this month" for the CSM and drafting emails and content; means for the CSM to check, distribute, and respond; means for setting the timing of the next contact after entering the results; means for automatically setting the timing of the next contact based on customer emotion data; and means for executing a customer filtering function, a contact list generation function, and an emotion engine using an application installed on a smartphone. This enables automated and efficient customer management, improving customer satisfaction and reducing the risk of cancellation.

[1454] "Sales activities" are a series of activities that a company undertakes to sell its products and services.

[1455] "Customer Success and Customer Support Response History" means a record of past responses to assist Customer success and provide support.

[1456] "Contract status" is information that indicates the current state of the contract concluded between the company and the customer.

[1457] "CRM" stands for Customer Relationship Management System, a software for managing relationships with customers.

[1458] "Generative AI" is an artificial intelligence technology that generates new information and predictions based on data.

[1459] A "health score" is a numerical indicator of a customer's health status and satisfaction.

[1460] "Cancellation indicators" are signs that indicate the possibility that a customer may cancel a contract.

[1461] "Kihen" is an abbreviation for "function change" and refers to a change in the functions of a system or service.

[1462] "Upselling" is a sales technique that offers customers more expensive products or additional services.

[1463] A "group of companies" is a collection of multiple companies that meet certain conditions.

[1464] "CSM" stands for Customer Success Manager, a person whose role is to support customer success.

[1465] A "customer list" is a list of customers who need to be contacted or dealt with.

[1466] "Email and Content Draft" means a draft of an email or content to be sent to a customer.

[1467] "Emotion data" is data that indicates the emotional state of the customer.

[1468] A "smartphone" is a mobile phone that can connect to the Internet and use applications.

[1469] An "application" is a software program designed to perform a particular function.

[1470] The "customer filtering function" is a function for selecting customers based on specific conditions.

[1471] The "contact list generation function" is a function that automatically creates a list of customers who need to be contacted.

[1472] The "emotion engine" is a system that analyzes customer emotional data and determines the appropriate response.

[1473] The system for implementing this invention has the following configuration: The server has a means for storing all sales activities, customer success and customer support response history, contract status, etc. in CRM. This allows all customer-related information to be managed centrally.

[1474] Next, there is a method to create a health score using generative AI. This generative AI quantifies the customer's health status and satisfaction based on the information accumulated in CRM, making it possible to quantitatively evaluate the customer's condition.

[1475] Additionally, there is a method to extract a group of companies that meet certain conditions, such as signs of cancellation, changes in contract status, and upselling. This method automatically lists specific companies based on conditions such as the generated health score and contract renewal period.

[1476] For CSMs, it provides a way to generate a list of customers to contact today, this week, and this month, and to draft emails and content, allowing CSMs to respond to customers efficiently.

[1477] It also includes a means for the CSM to check, distribute, and respond, as well as a means for setting the timing of the next contact after entering the results. This automates the entire customer response process.

[1478] Additionally, there is a way to automatically schedule the next contact based on customer sentiment data. This sentiment engine analyzes the customer's emotional state and determines the appropriate timing for follow-up.

[1479] Finally, there is a way to use an application installed on a smartphone to perform customer filtering, contact list generation, and sentiment engines, which allows for quick response on the spot.

[1480] For example, the following prompts could be fed into a generative AI model:

[1481] Customer ID: 1

[1482] Health score: 40

[1483] Last contact date: 60 days ago

[1484] Emotion score: 30

[1485] Set a date for your next contact with this customer and generate an email draft.

[1486] By inputting this prompt into a generative AI model, the appropriate timing for contact and email draft are automatically generated.

[1487] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1488] Step 1:

[1489] The server stores all data such as sales activities, customer success and customer support response history, contract status, etc. in CRM. The input is sales activity and customer support history data, and by saving this in CRM, all customer information is managed centrally. The output is the data stored in CRM.

[1490] Step 2:

[1491] The server uses generative AI to create a health score based on the information stored in the CRM. The input is customer data stored in the CRM, which the generative AI analyzes to calculate a health score. The output is a health score for each customer.

[1492] Step 3:

[1493] The server extracts a group of companies that meet certain conditions, such as signs of cancellation, changes in contract status, and upselling. The inputs include the generated health score and contract renewal period, and specific companies are listed based on these. The output is a list of companies that meet the conditions.

[1494] Step 4:

[1495] The server generates a "customer list to contact today, this week, and this month" for the CSM and creates email and content drafts. The input is the extracted company list and contact timing conditions, and based on this, the server generates the contact list and email draft. The output is the contact list and email draft.

[1496] Step 5:

[1497] The CSM checks the contact list and email draft generated by the server and delivers them to the customer. As input, it receives the contact list and email draft provided by the server, and contacts the customer based on this. As output, it completes the contact with the customer.

[1498] Step 6:

[1499] The CSM inputs the results of customer interactions into the server and sets the timing of the next contact. The input is the result data of the customer interaction, and the timing of the next contact is automatically set based on this. The output is the timing of the next contact.

[1500] Step 7:

[1501] The server automatically sets the timing of the next contact based on the customer's emotional data. The input is the customer's emotional data, which the emotion engine analyzes to determine the appropriate timing for follow-up. The output is the timing of the next contact based on the emotional data.

[1502] Step 8:

[1503] The device (smartphone) executes the customer filtering function, contact list generation function, and emotion engine provided by the server. Data from the server is input, and each function is executed on the smartphone based on this data. The output is a filtered customer list, a generated contact list, and the next contact timing determined by the emotion engine.

[1504] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1505] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1506] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1507] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1508] [Third embodiment]

[1509] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1510] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1511] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1512] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1513] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1514] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1515] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1516] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1517] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1518] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1519] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1520] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1521] "Example 1"

[1522] One embodiment of the present invention provides a means for storing all sales activities, customer success and customer support response history, contract status, etc. in CRM. Specifically, a system is built with an interface and API for storing various customer information in CRM. This allows various communications and transaction histories with customers to be managed in a unified manner, and a database for analysis is created.

[1523] "Example 2"

[1524] Furthermore, a means of creating health scores using generative AI will be provided. Specifically, an AI engine will be built that analyzes customer situations and behaviors based on various data accumulated in CRM and calculates a health score for each customer. This health score will be used to quantitatively evaluate important business indicators such as the probability of customer contract renewal and the possibility of upselling.

[1525] "Example 3"

[1526] It also provides a means to extract groups of companies that meet certain conditions, such as signs of cancellation, changes in schedule, or upselling. Specifically, a filtering function will be built that automatically extracts groups of companies that meet specific conditions based on health scores and other analysis results. For example, it is possible to automatically list companies that meet certain conditions, such as companies with health scores below a certain threshold, or companies where a certain amount of time has passed since their most recent contract renewal.

[1527] "Example 4"

[1528] Furthermore, a means will be provided for CSMs to generate "customer lists to contact today, this week, and this month" and draft emails and content. Specifically, a function will be built that plans contact with the extracted group of companies at the appropriate timing and content, and automatically generates action lists and content drafts based on that. This means that CSMs only need to check, distribute, and respond, and after entering the results, the timing of the next contact will be set automatically.

[1529] The processing flow of each embodiment will be described below.

[1530] "Example 1"

[1531] Step 1: Using an interface or API to store customer information in the CRM, store all sales activities, customer success and customer support response history, contract status, etc. in the CRM.

[1532] "Example 2"

[1533] Step 1: Obtain various data stored in CRM.

[1534] Step 2: Based on the acquired data, generative AI is used to analyze the customer's situation and behavior. Step 3: Based on the analysis results, a health score is calculated for each customer.

[1535] "Example 3"

[1536] Step 1: Get your health score and other analytics.

[1537] Step 2: Based on the results obtained, automatically extract a group of companies that meet specific conditions. "Example 4"

[1538] Step 1: Plan appropriate timing and content for contacting the selected group of companies.

[1539] Step 2: Automatically generate action lists and content drafts based on your plan.

[1540] Step 3: The CSM reviews the generated action list and content drafts and distributes or responds to them.

[1541] Step 4: After entering the results, automatically set the next contact timing.

[1542] Example 1

[1543] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1544] With traditional customer relationship management systems, it is difficult to centrally manage customer information such as sales activities, customer success and customer support response history, and contract status, which makes it easy for data to be scattered and duplicated. Furthermore, analyzing customer information, creating health scores, detecting signs of churn, and identifying upselling opportunities are all done manually, which is inefficient and prone to human error. Furthermore, creating contact lists and drafting emails for customer success management is time-consuming, making it difficult to respond quickly.

[1545] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1546] In this invention, the server includes means for accumulating all sales activity, customer success and customer support response history, contract status, etc. in a customer relationship management system, means for creating a health score using generative artificial intelligence, means for extracting groups of companies that meet conditions such as signs of cancellation, changes in circumstances, and upselling, means for generating a "customer list to contact today, this week, and this month" for customer success management and drafting emails and content, means for customer success management to check, distribute, and respond, means for setting the timing of the next contact after inputting the results, means for a user to input customer information from a terminal, means for the terminal to send the input data to the server via an application program interface, means for the server to save the received data in the customer relationship management system database, means for the server to process the saved data for analysis, and means for the server to provide the analysis results to the user. This enables centralized management of customer information, efficient analysis, and rapid response.

[1547] "Sales activities" are a series of actions taken to propose products and services to customers and promote sales.

[1548] "Customer success" refers to support activities that help customers make the most of the products and services they purchase and increase their satisfaction.

[1549] "Customer support response history" is a record of responses to customer inquiries and problems.

[1550] "Contract status" is information indicating the content and progress of the contract concluded with the customer.

[1551] A "customer relationship management system" is a software system that centrally manages customer information and efficiently conducts sales activities and customer support.

[1552] "Generative AI" is an AI technology that automatically analyzes and predicts data.

[1553] A "health score" is a numerical indicator of a customer's health status and satisfaction.

[1554] "Cancellation warning signs" are signs that indicate the possibility that a customer may cancel a service or contract.

[1555] "Change in equipment" refers to a change in the service or product that a customer is currently using.

[1556] "Upselling" is a sales technique that encourages customers to purchase more expensive products or services.

[1557] "Customer success management" is a management activity aimed at supporting customer success and building long-term relationships.

[1558] An "application program interface" is an interface for exchanging data between different software systems.

[1559] A "terminal" is a device such as a computer or smartphone operated by a user.

[1560] A "server" is a computer system for storing and processing data.

[1561] A "database" is a system for efficiently storing, searching, and managing data.

[1562] "Analysis" is the process of extracting information and finding meaning from data.

[1563] "Email" is a digital message sent and received over the Internet.

[1564] "Content" is a general term for information and materials provided to users.

[1565] "Drafting" refers to creating a draft of a document or email.

[1566] This invention is a system that uses a customer relationship management system (CRM) to centrally manage customer information such as sales activities, customer success and customer support response history, and contract status, thereby achieving efficient analysis and rapid response. A specific embodiment of this system is described below.

[1567] Hardware and software used

[1568] Hardware: A powerful server (e.g., Dell PowerEdge R740)

[1569] Software: CRM systems (e.g., Salesforce), database management systems (e.g., MySQL), API management tools (e.g., Postman)

[1570] Program processing

[1571] The server receives the customer information entered by the user from the terminal and stores it in the CRM system. Specifically, it performs the following processes:

[1572] 1. The user enters customer information from the terminal.

[1573] The user opens a browser on the device and accesses the input form of the CRM system. For example, the user enters "Customer name: Company A, Business content: New product proposal, Date: October 1, 2023."

[1574] 2. The device sends the entered data to the server via API.

[1575] The terminal converts the input data into JSON format and sends it to an API endpoint using an HTTP POST request, for example, " / api / v1 / customers".

[1576] 3. The server saves the received data in the CRM system database

[1577] The server parses the received JSON data and generates an SQL query to insert it into the database. For example, it executes the query "INSERT INTO customers (name, deal_content, date) VALUES ('Company A', 'New product proposal', '2023-10-01')".

[1578] 4. The server processes the stored data for analysis.

[1579] The server aggregates the stored data and filters it based on certain conditions, for example, by running the query "SELECT COUNT() FROM deals WHERE status = 'success' AND date BETWEEN '2023-01-01' AND '2023-12-31'".

[1580] 5. The server provides the analysis results to the user.

[1581] The server converts the analysis results into graphs and charts and displays them on a dashboard, for example, a pie chart showing success rates, which is displayed in the user's browser.

[1582] Examples of concrete examples and prompts

[1583] Examples:

[1584] When a user wants to enter information about a business deal with a new customer, he or she follows the steps below.

[1585] 1. The user enters business negotiation information (customer name, business details, date, etc.) from the terminal.

[1586] 2. The device sends the entered data to the server via the API.

[1587] 3. The server stores the received data in the CRM system database.

[1588] 4. The stored data is later used to analyze sales activities and generate reports.

[1589] Example prompt sentence:

[1590] "Please enter the details of your new customer opportunity. Include the customer name, details of the opportunity, and the date."

[1591] In this way, a system is realized in which the server, terminals, and users work together to centrally manage customer information and form a database for analysis.

[1592] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1593] Step 1:

[1594] The user enters customer information from the terminal.

[1595] The user opens a browser on their device and accesses the input form of the CRM system. For example, they enter "Customer name: Company A, Negotiation content: New product proposal, Date: October 1, 2023." The entered data is divided into fields such as customer name, negotiation content, and date.

[1596] Step 2:

[1597] The terminal sends the entered data to the server via the API.

[1598] The terminal converts the input data into JSON format and sends it to the API endpoint using an HTTP POST request, for example, to " / api / v1 / customers." The input data is structured as a JSON object and sent to the API endpoint.

[1599] Step 3:

[1600] The server stores the received data in the CRM system database.

[1601] The server parses the received JSON data and generates a SQL query to insert into the database, for example "INSERT INTO customers (name, deal_content, date) VALUES ('Company A', 'New Product Proposal', '2023-10-01')". The input data is saved in the appropriate table in the database.

[1602] Step 4:

[1603] The server processes the stored data for analysis.

[1604] The server aggregates the stored data and filters it based on certain conditions, for example, by running the query "SELECT COUNT() FROM deals WHERE status = 'success' AND date BETWEEN '2023-01-01' AND '2023-12-31'". The input data is processed for aggregation and filtering, and analytical results are generated.

[1605] Step 5:

[1606] The server provides the analysis results to the user.

[1607] The server converts the analysis results into graphs and charts and displays them on a dashboard. For example, a pie chart showing success rates may be generated and displayed in the user's browser. The input data is processed for visualization and provided to the user.

[1608] In this way, a system is realized in which the server, terminals, and users work together to centrally manage customer information and form a database for analysis.

[1609] (Application example 1)

[1610] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1611] Conventional CRM systems do not centrally manage customer information and interaction information, making it difficult for store staff to check the information they need in real time when responding to customers. Furthermore, there was a lack of a way to effectively utilize customer visit history, purchase history, and customer support response history, making it difficult to improve the quality of customer support.

[1612] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1613] In this invention, the server includes: means for accumulating all sales activity, customer success and customer support response history, contract status, etc. in CRM; means for creating a health score using generative AI; means for extracting groups of companies that meet conditions such as signs of cancellation, time changes, and upselling; means for generating a "customer list to contact today, this week, and this month" for the CSM and drafting emails and content; means for the CSM to check, distribute, and respond; means for setting the timing of the next contact after entering the results; means for checking customer visit history, purchase history, and customer support response history in real time for use by store staff when responding to customers; and means for checking customer information and interaction information in real time to provide better customer service. This allows store staff to check the information they need when responding to customers in real time, improving the quality of customer service.

[1614] "Sales activities" are a series of activities a company undertakes to sell its products and services.

[1615] "Customer Success" is the activity of helping customers get the most out of products and services and achieve success.

[1616] "Customer support response history" is a record of responses to customer inquiries and problems.

[1617] "Contract status" is information indicating the progress and content of a contract with a customer.

[1618] "CRM" stands for Customer Relationship Management System, a system for centrally managing customer information.

[1619] "Generative AI" is a system that uses artificial intelligence to analyze data and generate information for specific purposes.

[1620] A "health score" is a numerical indicator of a customer's health status and satisfaction.

[1621] "Cancelers" are signs that indicate the possibility that a customer will cancel a service.

[1622] "Kihen" is an abbreviation for "functional change" and refers to a change in the functionality of a product or service.

[1623] "Upselling" is a sales technique that offers customers more expensive products or services.

[1624] A "group of companies" is a collection of multiple companies that meet certain conditions.

[1625] "CSM" stands for Customer Success Manager, a person whose role is to support customer success.

[1626] A "customer list" is a list of customers selected based on specific conditions.

[1627] "Drafting" refers to creating a draft of a document or content.

[1628] A "brick and mortar store" is a retail outlet that exists in a physical location.

[1629] "Store visit history" is a record of a customer's visits to a store.

[1630] "Purchase history" is a record of products and services purchased by a customer.

[1631] "Interaction information" is information about interactions and exchanges with customers.

[1632] The system for implementing this invention consists of three main components: a server, a terminal, and a user. The server centrally manages customer information and analyzes the data using a generative AI model. The terminal is a device used by store staff when dealing with customers, such as a smartphone or tablet. The user is a store staff member or a customer success manager (CSM).

[1633] The server has a means of storing all sales activity, customer success and customer support response history, contract status, etc. in CRM. This allows for centralized management of various communication and transaction histories with customers. In addition, it has a means of creating a health score using generative AI and extracting companies that meet conditions such as signs of cancellation, feature changes, and upselling. This makes it possible to quantify customer health and satisfaction and classify customers based on specific conditions.

[1634] The terminals are used by store staff when dealing with customers and provide a means to check customer visit history, purchase history, and customer support response history in real time, allowing store staff to check customer information and interaction information in real time and provide better customer service.

[1635] Users contact customers at the appropriate time based on the "Customer list to contact today, this week, this month" provided by the server. In addition, users can create drafts of emails and content, which are then checked, distributed, and handled by the CSM. After entering the results, there is also a means to set the timing of the next contact.

[1636] As a concrete example, the following prompt sentence can be input to a generative AI model:

[1637] Example prompt sentence:

[1638] "Get the information for the customer whose customer ID is 1."

[1639] Using this prompt, the server retrieves customer information and displays it on the terminal, allowing store staff to check the information they need in real time when assisting customers and improve the quality of their customer service.

[1640] Also, the prompt statement to obtain interaction information for a customer with customer ID 1 is as follows:

[1641] Example prompt sentence:

[1642] "Get interaction information for customer with customer ID 1."

[1643] Using this prompt, the server retrieves interaction information and displays it on the device, allowing store staff to review past interactions with customers and provide more appropriate responses.

[1644] In this way, by linking the server, terminal, and user, it is possible to achieve centralized management of customer information and provide information in real time, thereby improving the quality of customer service.

[1645] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1646] Step 1:

[1647] The server stores customer information such as sales activities, customer success and customer support response history, and contract status in CRM. As input, it receives data on various communications and transactions with customers and stores this in a database. As output, the stored customer information is managed centrally.

[1648] Step 2:

[1649] The server uses a generative AI model to create a health score based on the information stored in the CRM. Customer history data is received as input, and the generative AI model analyzes it. The output is a health score that quantifies the customer's health status and satisfaction.

[1650] Step 3:

[1651] The server extracts companies that meet certain criteria, such as those predicting churn, changing features, or upselling. As input, it receives health scores and other customer data, filters the data based on specific criteria, and generates a list of companies that meet the criteria as output.

[1652] Step 4:

[1653] The server generates a "customer list to contact today, this week, and this month" for the CSM and creates email and content drafts. The server receives the extracted company list and health score as input, and the generative AI model uses this to create the list and draft. The output is a customer list to contact and email content drafts.

[1654] Step 5:

[1655] The user (CSM) checks the customer list and draft provided by the server and makes corrections as necessary. As input, the list and draft provided by the server are received, and the user checks and corrects them. As output, the final customer list and email content are confirmed.

[1656] Step 6:

[1657] The user (CSM) contacts customers based on the confirmed customer list. As input, the confirmed customer list and email content are received, and the user sends this to the customer. As output, contact with the customer is completed.

[1658] Step 7:

[1659] The user (CSM) inputs the contact results into the server and sets the timing of the next contact. The server receives the contact results and the timing of the next contact as input, and stores them in the database. The timing of the next contact is set as output.

[1660] Step 8:

[1661] When a store staff member assists a customer, the terminal checks the customer's visit history, purchase history, and customer support response history in real time. It receives a customer ID as input and retrieves related customer information from the server. As output, the customer information is displayed on the terminal.

[1662] Step 9: 【166...

Claims

1. A means for accumulating information including sales activities to customers, efforts to realize the success of customers who use the company's products and services, customer support response history, progress of contracts with customers, and the contents of contracts with customers in a customer relationship management (CRM) system; A means for generating a health score, which is an index that quantifies a customer's health condition and satisfaction, using a generative AI model; An extraction means for extracting companies whose health scores are below a certain threshold and companies whose most recent contract renewal has been a certain period of time ago as a group of companies that meet certain conditions by inputting a specific prompt sentence into a generating AI model; A way to create a list of selected customers based on specific criteria for a Customer Success Manager (CSM), who is responsible for helping customers succeed; means for delivering the list to the customer success manager (CSM); a setting unit that sets the timing of the next contact with the customer when the customer success manager (CSM) who has checked the distributed list has completed sending the materials to the customer; An emotion engine that recognizes emotions of a customer who is a user communicating with the customer success manager (CSM); A means for quantifying the emotion; Including, The creation means uses a generative AI model to create the health score based on the information stored in the customer relationship management (CRM) system and the quantified emotions.

2. The system of claim 1 , further comprising means for including in said list customers who are more likely to churn and upsell based on said sentiment.

3. The system of claim 1 , further comprising means for displaying the health score on a smartphone.

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