system

Abstract

The system according to this embodiment aims to integrate and analyze data from multiple marketing channels and automatically identify customer segments. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an identification unit, and a proposal unit. The collection unit collects data from multiple marketing channels. The analysis unit comprehensively analyzes the data collected by the collection unit. The identification unit automatically identifies customer segments based on the analysis results obtained by the analysis unit. The proposal unit proposes advertising strategies based on the customer segments identified by the identification unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, data from a plurality of marketing channels has not been sufficiently analyzed integrally to automatically identify customer segments, and there is room for improvement.

[0005] The system according to the embodiment aims to integrally analyze data from a plurality of marketing channels and automatically identify customer segments.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, an identification unit, and a proposal unit. The data collection unit collects data from multiple marketing channels. The analysis unit comprehensively analyzes the data collected by the data collection unit. The identification unit automatically identifies customer segments based on the analysis results obtained by the analysis unit. The proposal unit proposes advertising strategies based on the customer segments identified by the identification unit. [Effects of the Invention]

[0007] The system according to this embodiment can integrate and analyze data from multiple marketing channels and automatically identify customer segments. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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. Also, the database 24 and the communication I / F 26 are 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).

[0019] [[ID=二十一]] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​marketing analysis support assistant according to an embodiment of the present invention is a system that comprehensively analyzes data from multiple marketing channels, automatically identifies customer segments, analyzes customer behavior trends, and proposes advertising strategies to maximize ROI. The AI ​​marketing analysis support assistant collects data from multiple marketing channels, and the AI ​​comprehensively analyzes the collected data to automatically identify customer segments. Furthermore, it analyzes customer behavior trends and proposes advertising strategies to maximize ROI. For example, the AI ​​marketing analysis support assistant collects data from sources such as social media, email marketing, and website access logs. This data is input into the AI. Next, the AI ​​comprehensively analyzes the collected data. The AI ​​analyzes the data collected from each channel and automatically identifies customer segments. For example, based on purchase history and browsing history, it classifies customers into segments that prefer expensive products or segments that prefer discounts. Furthermore, the AI ​​analyzes customer behavior trends. For example, it analyzes purchase patterns and browsing patterns over a specific period to understand changes in customer behavior. This makes it possible to predict customer needs and interests. Finally, the AI ​​proposes advertising strategies to maximize ROI. For example, it can suggest optimal advertising messages and promotions for specific customer segments. This maximizes the effectiveness of advertising and improves marketing efficiency. This system allows marketers to centrally manage data from multiple channels and gain a detailed understanding of customer behavior. In addition, the AI ​​automatically identifies customer segments and analyzes trends, streamlining the development of marketing strategies. Furthermore, it can enhance marketing effectiveness by suggesting advertising strategies that maximize ROI. Thus, the AI ​​assistant for marketing analytics can comprehensively analyze data from multiple marketing channels, automatically identify customer segments, analyze customer behavior trends, and suggest advertising strategies that maximize ROI.

[0029] The marketing analysis support AI assistant according to this embodiment comprises a collection unit, an analysis unit, an identification unit, and a proposal unit. The collection unit collects data from multiple marketing channels. The collection unit collects data such as SNS data, email marketing data, and website access logs. For example, the collection unit can obtain SNS data via an API, email marketing data from an email server, and website access logs from a web server. The analysis unit comprehensively analyzes the data collected by the collection unit. For example, the analysis unit performs data preprocessing, such as data cleansing and normalization. For example, the analysis unit can perform data integration methods such as database joining and data warehouse construction. The identification unit automatically identifies customer segments based on the analysis results obtained by the analysis unit. For example, the identification unit classifies customers using a clustering algorithm. For example, the identification unit can classify customers into segments that prefer expensive products or segments that prefer discounts based on purchase history and browsing history. The proposal unit proposes advertising strategies based on the customer segments identified by the identification unit. The proposal unit, for example, proposes optimal advertising messages and promotions for specific customer segments. The proposal unit can, for example, create advertising messages and design promotions. As a result, the marketing analysis support AI assistant according to the embodiment can comprehensively analyze data from multiple marketing channels, automatically identify customer segments, analyze customer behavior trends, and propose advertising strategies to maximize ROI.

[0030] The data collection unit gathers data from multiple marketing channels. Specifically, it collects data such as social media data, email marketing data, and website access logs. Social media data is obtained via APIs and includes detailed data such as post content, likes, shares, and comments. This allows for a detailed understanding of customer reactions and engagement on social media. Email marketing data is obtained from email servers and includes metrics such as open rates, click-through rates, and unsubscribe rates. This allows for evaluation of the effectiveness of email campaigns and identification of areas for improvement. Website access logs are obtained from web servers and include data such as page views, time spent on site, bounce rates, and conversion rates. This allows for detailed analysis of website visitor behavior and helps improve the user experience. The data collection unit collects this data in real time and sends it to a central database. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses tailored to specific marketing campaigns and events. This allows the data collection unit to collect data efficiently and effectively, improving the performance of the AI ​​assistant that supports marketing analysis.

[0031] The analysis department comprehensively analyzes the data collected by the data collection department. Specifically, it performs data preprocessing, including data cleansing and normalization. Data cleansing improves data quality by imputing missing values ​​and removing outliers. Normalization unifies data at different scales to improve the accuracy of analysis. The analysis department can integrate data by joining databases or building data warehouses. Joining databases combines data from different sources into a single dataset, allowing for analysis from a unified perspective. Building data warehouses enables efficient management of large amounts of data and high-speed query processing. Furthermore, the analysis department uses AI to extract data patterns and trends. For example, machine learning algorithms can be used to analyze customer purchasing behavior and website visit patterns to predict future behavior. This allows the analysis department to quickly and accurately analyze collected data and provide the insights necessary for formulating marketing strategies.

[0032] The identification unit automatically identifies customer segments based on the analysis results obtained by the analysis unit. Specifically, it classifies customers using a clustering algorithm. The clustering algorithm groups customers with similar characteristics based on data such as customer purchase history, browsing history, and social media activity. For example, based on purchase history, it can classify customers into segments that prefer expensive products or segments that prefer discounts. It can also identify customers who are interested in specific product categories based on browsing history. The identification unit updates these segments in real time, performing highly accurate segmentation based on the latest customer data. Furthermore, the identification unit can use AI to predict customer lifetime value (LTV) and identify high-value customers. This allows the identification unit to improve the targeting accuracy of marketing strategies and provide a foundation for maximizing ROI.

[0033] The proposal department proposes advertising strategies based on customer segments identified by specific departments. Specifically, it proposes optimal advertising messages and promotions for specific customer segments. The proposal department uses AI to analyze data from past advertising campaigns and extract patterns of effective messages and promotions. For example, it can propose advertising messages emphasizing premium quality to segments that prefer expensive products, and limited-time discount promotions to segments that prefer discounts. Furthermore, the proposal department creates advertising messages and designs promotions. Specifically, it can automatically generate optimal advertising copy for target segments using natural language generation technology. In designing promotions, it selects the optimal timing and channels and develops a plan for implementing effective campaigns. This allows the proposal department to provide personalized advertising strategies tailored to customer segments and maximize marketing effectiveness. In addition, the proposal department can measure the effectiveness of advertising campaigns after their implementation and identify areas for improvement for the next campaign. This allows the proposal department to improve the accuracy and effectiveness of its marketing strategies through continuous improvement.

[0034] The data collection unit can collect SNS data, email marketing data, and website access log data. For example, the data collection unit can obtain SNS data via API, email marketing data from an email server, and website access logs from a web server. This allows for the integrated analysis of a wider range of data by collecting data from multiple marketing channels. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, when the data collection unit obtains SNS data via API, it can use AI to filter and preprocess the data.

[0035] The analysis department can comprehensively analyze collected data and analyze trends in customer behavior. For example, the analysis department can perform data preprocessing, such as data cleansing and normalization. For example, the analysis department can perform data integration methods such as database joining and data warehouse construction. This allows for the prediction of customer needs and interests by analyzing trends in customer behavior. Some or all of the above-mentioned processes in the analysis department may be performed using AI, or not. For example, the analysis department can input collected data into an AI, which can then cleanse and normalize the data and analyze trends in customer behavior.

[0036] The identification unit can classify customers based on their purchase history or browsing history. The identification unit can classify customers using, for example, a clustering algorithm. The identification unit can classify customers into segments such as those that prefer expensive products or those that prefer discounts, based on their purchase history or browsing history. By classifying customers in this way, it is possible to propose the most suitable advertising strategy for specific customer segments. Some or all of the above processing in the identification unit may be performed using, for example, AI, or not using AI. For example, the identification unit can input purchase history or browsing history into an AI, which can then classify customers using a clustering algorithm.

[0037] The proposal unit can propose the most suitable advertising message or promotion for a specific customer segment. For example, the proposal unit can propose the most suitable advertising message or promotion for a specific customer segment. The proposal unit can, for example, create advertising messages and design promotions. This maximizes the effectiveness of advertising by proposing the most suitable advertising strategy for a specific customer segment. Some or all of the above processes in the proposal unit may be performed using AI, or not. For example, the proposal unit can have AI generate the most suitable advertising message for a specific customer segment.

[0038] The data collection unit can dynamically adjust the data collection frequency for each marketing channel and select the optimal data collection strategy. For example, the data collection unit can increase the data collection frequency for social media to grasp real-time trends. It can also adjust the data collection frequency for email marketing to evaluate the effectiveness of campaigns in a timely manner. Furthermore, the data collection unit can increase the data collection frequency for website access logs to analyze user behavior patterns in detail. This allows for the selection of the optimal data collection strategy by dynamically adjusting the data collection frequency for each marketing channel. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can have AI adjust the data collection frequency for each marketing channel.

[0039] The data collection unit can filter data based on the user's current purchasing intent or areas of interest during data collection. For example, if a user is interested in expensive items, the data collection unit will prioritize collecting relevant data. It can also filter and collect data related to discounted items if the user is interested in those items. Furthermore, if a user is interested in products in a specific category, the data collection unit can collect data related to that category. This allows for the collection of highly relevant data by filtering it based on the user's purchasing intent and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI estimate the user's purchasing intent and areas of interest, and the AI ​​can then filter the data.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of marketing data related to that region. Furthermore, if the user is traveling, the data collection unit can also collect data related to their travel destination. Additionally, if the user is at home, the data collection unit can prioritize the collection of local marketing data. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then prioritize the collection of highly relevant data.

[0041] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user mentions a specific brand on social media, the data collection unit can collect data related to that brand. It can also collect data related to an event if a user participates in a specific event on social media. Furthermore, if a user discusses a specific topic on social media, the data collection unit can collect data related to that topic. This allows for the collection of relevant data by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI analyze a user's social media activity, and the AI ​​can collect relevant data.

[0042] The analytics department can adjust the level of detail of its analysis based on the importance of the data. For example, it can perform detailed analysis on important data to provide deeper insights, while performing simplified analysis on less important data. Furthermore, it can focus its analysis on data related to specific marketing campaigns. This allows for detailed analysis of important data by adjusting the level of detail based on its importance. Some or all of the above processes in the analytics department may be performed using AI, for example, or not. For example, the analytics department can have AI assess the importance of the data, and the AI ​​can adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, it can apply a clustering algorithm to purchase history data to identify customer segments. It can also apply a path analysis algorithm to website access logs to analyze user behavior patterns. Furthermore, it can apply a sentiment analysis algorithm to social media data to understand user sentiment. By applying different analysis algorithms depending on the data category, more accurate analysis results can be obtained. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can have AI classify the data categories, and the AI ​​can apply an appropriate analysis algorithm.

[0044] The analysis department can prioritize analyses based on the timing of data collection. For example, it can prioritize analyzing the latest data to grasp real-time trends. It can also analyze historical data to grasp long-term trends. Furthermore, it can focus on analyzing data from a specific period to grasp trends during that period. This allows for real-time trend understanding by prioritizing analyses based on the timing of data collection. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can have AI evaluate the timing of data collection, and the AI ​​can determine the analysis priorities.

[0045] The analysis department can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis department can prioritize the analysis of highly relevant data to gain important insights. It can also postpone the analysis of less relevant data. Furthermore, the analysis department can prioritize the analysis of data related to a specific marketing campaign. This allows for important insights to be gained by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can have AI evaluate the relevance of the data, and the AI ​​can adjust the order of analysis.

[0046] The identification unit can improve the accuracy of customer segmentation based on customer interactions. For example, the identification unit can analyze social networks between customers and identify segments based on their interactions. It can also compare purchase histories between customers and classify customers with common patterns into the same segment. Furthermore, the identification unit can consider customer mutual evaluations to identify highly reliable segments. This improves the accuracy of identification by considering customer interactions. Some or all of the above processing in the identification unit may be performed using AI, for example, or not. For example, the identification unit can input customer interaction data into AI, which can then improve the accuracy of identification.

[0047] The identification unit can identify customer segments by considering customer attribute information. For example, the identification unit can identify segments based on customer attribute information such as age, gender, and income. The identification unit can also identify segments by considering the customer's geographical location information. Furthermore, the identification unit can identify segments based on the customer's purchase history and browsing history. This makes it possible to identify customer segments with higher accuracy by considering customer attribute information. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input customer attribute information into AI, and the AI ​​can identify customer segments.

[0048] The identification unit can identify customer segments while considering the geographical distribution of customers. For example, the identification unit can classify customers concentrated in a specific region into the same segment. It can also classify geographically dispersed customers into different segments. Furthermore, the identification unit can formulate regional marketing strategies based on geographical distribution. This allows for the formulation of regional marketing strategies by considering the geographical distribution of customers. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input customer geographical distribution data into AI, which can then identify customer segments.

[0049] The identification unit can improve the accuracy of customer segment identification based on relevant literature. For example, the identification unit can update the criteria for customer segment identification by referring to the latest marketing research. It can also refer to past success stories and apply effective segment identification methods. Furthermore, the identification unit can improve the accuracy of segment identification by referring to industry best practices. In this way, the accuracy of customer segment identification can be improved by referring to relevant literature. Some or all of the above processes in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input relevant literature into AI, and the AI ​​can update the criteria for customer segment identification.

[0050] The proposal department can adjust the level of detail in its advertising strategy proposals based on the importance of each customer segment. For example, it can propose a detailed advertising strategy to important customer segments, and a simplified strategy to less important customer segments. Furthermore, it can propose a more focused advertising strategy to customer segments relevant to specific marketing campaigns. By adjusting the level of detail based on the importance of each customer segment, it can propose more effective advertising strategies. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can have AI evaluate the importance of customer segments, and the AI ​​can adjust the level of detail in the proposals.

[0051] The proposal department can apply different proposal algorithms depending on the customer segment category when proposing advertising strategies. For example, the proposal department can propose a premium advertising strategy to a customer segment that prefers expensive products. It can also propose a promotion-focused advertising strategy to a customer segment that prefers discounts. Furthermore, the proposal department can propose a personalized advertising strategy to a customer segment with specific interests. By applying different proposal algorithms depending on the customer segment category, it is possible to propose more effective advertising strategies. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can have AI classify customer segment categories, and the AI ​​can apply an appropriate proposal algorithm.

[0052] The proposal department can prioritize advertising strategies based on the timing of customer segment collection when proposing advertising strategies. For example, the proposal department can prioritize proposing advertising strategies to the most recent customer segments. It can also postpone proposing advertising strategies to older customer segments. Furthermore, the proposal department can propose advertising strategies tailored to customer segments within a specific period. By prioritizing proposals based on the timing of customer segment collection, it is possible to propose more effective advertising strategies. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can have AI evaluate the timing of customer segment collection, and the AI ​​can determine the priority of proposals.

[0053] The proposal department can adjust the order of advertising strategies based on the relevance of customer segments when proposing advertising strategies. For example, the proposal department can prioritize proposing advertising strategies to highly relevant customer segments. It can also postpone proposing advertising strategies to less relevant customer segments. Furthermore, the proposal department can focus on proposing advertising strategies to customer segments related to specific marketing campaigns. By adjusting the order of proposals based on the relevance of customer segments, it is possible to propose more effective advertising strategies. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can have AI evaluate the relevance of customer segments, and the AI ​​can adjust the order of proposals.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] AI assistants for marketing analytics can also be equipped with a predictive function. This predictive function can forecast future customer behavior and market trends based on collected data and historical trends. For example, it can analyze past purchase history and seasonal trends to predict what products will sell in the next season. It can also identify products and services that are likely to become popular based on social media trend data. Furthermore, it can predict future purchasing behavior based on customer life events (marriage, childbirth, moving, etc.). This allows marketers to understand future market trends and develop proactive marketing strategies.

[0056] The AI ​​assistant for marketing analytics support can also include a feedback function. This feedback function evaluates the effectiveness of proposed advertising strategies after implementation and feeds the results back into the system. For example, the feedback function monitors click-through rates and conversion rates of advertising campaigns and provides the results to the proposal function. It can also collect customer responses and feedback and incorporate them into future advertising strategies. Furthermore, the feedback function can analyze the effectiveness of competitors' advertising strategies and apply that knowledge to their own. This allows marketing professionals to understand the effectiveness of their advertising strategies in real time and quickly implement improvements.

[0057] Marketing analytics support AI assistants can also be equipped with a personalization function. This personalization function generates optimal advertising messages and promotions for each customer. For example, it can create personalized advertising messages based on a customer's purchase and browsing history. It can also recommend specific products and services based on a customer's interests. Furthermore, it can provide customized promotions tailored to a customer's lifestyle and preferences. This allows marketers to implement more effective advertising strategies for each individual customer.

[0058] The AI ​​assistant for marketing analysis support can also include a competitive analysis department. This department analyzes the marketing strategies and market share of competitors and incorporates those insights into the company's own strategy. For example, it can monitor the effectiveness of competitors' advertising campaigns and identify their success factors. It can also analyze competitors' product lineups and pricing to inform the company's own product strategy. Furthermore, it can collect customer reviews and feedback from competitors to improve the company's own products and services. This allows marketing professionals to understand competitor trends and develop competitive marketing strategies.

[0059] The AI ​​assistant for marketing analytics support can also include a cross-channel analytics function. This function comprehensively analyzes customer behavior across multiple marketing channels to evaluate overall marketing effectiveness. For example, it integrates data from social media, email marketing, and website access logs to identify customer behavior patterns across channels. It can also compare the effectiveness of each channel to identify the most effective one. Furthermore, it can analyze which channels customers achieve the most conversions through and incorporate these results into marketing strategies. This allows marketers to effectively leverage multiple channels and maximize overall marketing effectiveness.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects data from multiple marketing channels. For example, it collects data such as social media data, email marketing data, and website access logs. The data collection unit obtains social media data via API, email marketing data from the mail server, and website access logs from the web server. Step 2: The analysis unit comprehensively analyzes the data collected by the collection unit. For example, it performs data preprocessing, such as data cleansing and normalization. Furthermore, it can perform database merging and data warehouse construction. Step 3: The identification unit automatically identifies customer segments based on the analysis results obtained by the analysis unit. For example, it classifies customers using a clustering algorithm, and then classifies them into segments that prefer expensive products or segments that prefer discounts based on their purchase history and browsing history. Step 4: The proposal department proposes advertising strategies based on the customer segments identified by the specific department. For example, they propose optimal advertising messages and promotions for specific customer segments and create the advertising messages and design the promotions.

[0062] (Example of form 2) The AI ​​marketing analysis support assistant according to an embodiment of the present invention is a system that comprehensively analyzes data from multiple marketing channels, automatically identifies customer segments, analyzes customer behavior trends, and proposes advertising strategies to maximize ROI. The AI ​​marketing analysis support assistant collects data from multiple marketing channels, and the AI ​​comprehensively analyzes the collected data to automatically identify customer segments. Furthermore, it analyzes customer behavior trends and proposes advertising strategies to maximize ROI. For example, the AI ​​marketing analysis support assistant collects data from sources such as social media, email marketing, and website access logs. This data is input into the AI. Next, the AI ​​comprehensively analyzes the collected data. The AI ​​analyzes the data collected from each channel and automatically identifies customer segments. For example, based on purchase history and browsing history, it classifies customers into segments that prefer expensive products or segments that prefer discounts. Furthermore, the AI ​​analyzes customer behavior trends. For example, it analyzes purchase patterns and browsing patterns over a specific period to understand changes in customer behavior. This makes it possible to predict customer needs and interests. Finally, the AI ​​proposes advertising strategies to maximize ROI. For example, it can suggest optimal advertising messages and promotions for specific customer segments. This maximizes the effectiveness of advertising and improves marketing efficiency. This system allows marketers to centrally manage data from multiple channels and gain a detailed understanding of customer behavior. In addition, the AI ​​automatically identifies customer segments and analyzes trends, streamlining the development of marketing strategies. Furthermore, it can enhance marketing effectiveness by suggesting advertising strategies that maximize ROI. Thus, the AI ​​assistant for marketing analytics can comprehensively analyze data from multiple marketing channels, automatically identify customer segments, analyze customer behavior trends, and suggest advertising strategies that maximize ROI.

[0063] The marketing analysis support AI assistant according to this embodiment comprises a collection unit, an analysis unit, an identification unit, and a proposal unit. The collection unit collects data from multiple marketing channels. The collection unit collects data such as SNS data, email marketing data, and website access logs. For example, the collection unit can obtain SNS data via an API, email marketing data from an email server, and website access logs from a web server. The analysis unit comprehensively analyzes the data collected by the collection unit. For example, the analysis unit performs data preprocessing, such as data cleansing and normalization. For example, the analysis unit can perform data integration methods such as database joining and data warehouse construction. The identification unit automatically identifies customer segments based on the analysis results obtained by the analysis unit. For example, the identification unit classifies customers using a clustering algorithm. For example, the identification unit can classify customers into segments that prefer expensive products or segments that prefer discounts based on purchase history and browsing history. The proposal unit proposes advertising strategies based on the customer segments identified by the identification unit. The proposal unit, for example, proposes optimal advertising messages and promotions for specific customer segments. The proposal unit can, for example, create advertising messages and design promotions. As a result, the marketing analysis support AI assistant according to the embodiment can comprehensively analyze data from multiple marketing channels, automatically identify customer segments, analyze customer behavior trends, and propose advertising strategies to maximize ROI.

[0064] The data collection unit gathers data from multiple marketing channels. Specifically, it collects data such as social media data, email marketing data, and website access logs. Social media data is obtained via APIs and includes detailed data such as post content, likes, shares, and comments. This allows for a detailed understanding of customer reactions and engagement on social media. Email marketing data is obtained from email servers and includes metrics such as open rates, click-through rates, and unsubscribe rates. This allows for evaluation of the effectiveness of email campaigns and identification of areas for improvement. Website access logs are obtained from web servers and include data such as page views, time spent on site, bounce rates, and conversion rates. This allows for detailed analysis of website visitor behavior and helps improve the user experience. The data collection unit collects this data in real time and sends it to a central database. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses tailored to specific marketing campaigns and events. This allows the data collection unit to collect data efficiently and effectively, improving the performance of the AI ​​assistant that supports marketing analysis.

[0065] The analysis department comprehensively analyzes the data collected by the data collection department. Specifically, it performs data preprocessing, including data cleansing and normalization. Data cleansing improves data quality by imputing missing values ​​and removing outliers. Normalization unifies data at different scales to improve the accuracy of analysis. The analysis department can integrate data by joining databases or building data warehouses. Joining databases combines data from different sources into a single dataset, allowing for analysis from a unified perspective. Building data warehouses enables efficient management of large amounts of data and high-speed query processing. Furthermore, the analysis department uses AI to extract data patterns and trends. For example, machine learning algorithms can be used to analyze customer purchasing behavior and website visit patterns to predict future behavior. This allows the analysis department to quickly and accurately analyze collected data and provide the insights necessary for formulating marketing strategies.

[0066] The identification unit automatically identifies customer segments based on the analysis results obtained by the analysis unit. Specifically, it classifies customers using a clustering algorithm. The clustering algorithm groups customers with similar characteristics based on data such as customer purchase history, browsing history, and social media activity. For example, based on purchase history, it can classify customers into segments that prefer expensive products or segments that prefer discounts. It can also identify customers who are interested in specific product categories based on browsing history. The identification unit updates these segments in real time, performing highly accurate segmentation based on the latest customer data. Furthermore, the identification unit can use AI to predict customer lifetime value (LTV) and identify high-value customers. This allows the identification unit to improve the targeting accuracy of marketing strategies and provide a foundation for maximizing ROI.

[0067] The proposal department proposes advertising strategies based on customer segments identified by specific departments. Specifically, it proposes optimal advertising messages and promotions for specific customer segments. The proposal department uses AI to analyze data from past advertising campaigns and extract patterns of effective messages and promotions. For example, it can propose advertising messages emphasizing premium quality to segments that prefer expensive products, and limited-time discount promotions to segments that prefer discounts. Furthermore, the proposal department creates advertising messages and designs promotions. Specifically, it can automatically generate optimal advertising copy for target segments using natural language generation technology. In designing promotions, it selects the optimal timing and channels and develops a plan for implementing effective campaigns. This allows the proposal department to provide personalized advertising strategies tailored to customer segments and maximize marketing effectiveness. In addition, the proposal department can measure the effectiveness of advertising campaigns after their implementation and identify areas for improvement for the next campaign. This allows the proposal department to improve the accuracy and effectiveness of its marketing strategies through continuous improvement.

[0068] The data collection unit can collect SNS data, email marketing data, and website access log data. For example, the data collection unit can obtain SNS data via API, email marketing data from an email server, and website access logs from a web server. This allows for the integrated analysis of a wider range of data by collecting data from multiple marketing channels. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, when the data collection unit obtains SNS data via API, it can use AI to filter and preprocess the data.

[0069] The analysis department can comprehensively analyze collected data and analyze trends in customer behavior. For example, the analysis department can perform data preprocessing, such as data cleansing and normalization. For example, the analysis department can perform data integration methods such as database joining and data warehouse construction. This allows for the prediction of customer needs and interests by analyzing trends in customer behavior. Some or all of the above-mentioned processes in the analysis department may be performed using AI, or not. For example, the analysis department can input collected data into an AI, which can then cleanse and normalize the data and analyze trends in customer behavior.

[0070] The identification unit can classify customers based on their purchase history or browsing history. The identification unit can classify customers using, for example, a clustering algorithm. The identification unit can classify customers into segments such as those that prefer expensive products or those that prefer discounts, based on their purchase history or browsing history. By classifying customers in this way, it is possible to propose the most suitable advertising strategy for specific customer segments. Some or all of the above processing in the identification unit may be performed using, for example, AI, or not using AI. For example, the identification unit can input purchase history or browsing history into an AI, which can then classify customers using a clustering algorithm.

[0071] The proposal unit can propose the most suitable advertising message or promotion for a specific customer segment. For example, the proposal unit can propose the most suitable advertising message or promotion for a specific customer segment. The proposal unit can, for example, create advertising messages and design promotions. This maximizes the effectiveness of advertising by proposing the most suitable advertising strategy for a specific customer segment. Some or all of the above processes in the proposal unit may be performed using AI, or not. For example, the proposal unit can have AI generate the most suitable advertising message for a specific customer segment.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the data collection unit can shorten the timing of data collection to collect data quickly. In this way, by adjusting the timing of data collection according to the user's emotions, the user's burden can be reduced and detailed data can be collected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into an AI, which can then adjust the timing of data collection.

[0073] The data collection unit can dynamically adjust the data collection frequency for each marketing channel and select the optimal data collection strategy. For example, the data collection unit can increase the data collection frequency for social media to grasp real-time trends. It can also adjust the data collection frequency for email marketing to evaluate the effectiveness of campaigns in a timely manner. Furthermore, the data collection unit can increase the data collection frequency for website access logs to analyze user behavior patterns in detail. This allows for the selection of the optimal data collection strategy by dynamically adjusting the data collection frequency for each marketing channel. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can have AI adjust the data collection frequency for each marketing channel.

[0074] The data collection unit can filter data based on the user's current purchasing intent or areas of interest during data collection. For example, if a user is interested in expensive items, the data collection unit will prioritize collecting relevant data. It can also filter and collect data related to discounted items if the user is interested in those items. Furthermore, if a user is interested in products in a specific category, the data collection unit can collect data related to that category. This allows for the collection of highly relevant data by filtering it based on the user's purchasing intent and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI estimate the user's purchasing intent and areas of interest, and the AI ​​can then filter the data.

[0075] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is excited, the data collection unit may prioritize collecting the latest trend data. If the user is relaxed, the data collection unit may also prioritize collecting detailed analytical data. Furthermore, if the user is stressed, the data collection unit may prioritize collecting only the most important data. This allows for the collection of more relevant data by prioritizing the data to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI, which can then determine the priority of data to collect.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of marketing data related to that region. Furthermore, if the user is traveling, the data collection unit can also collect data related to their travel destination. Additionally, if the user is at home, the data collection unit can prioritize the collection of local marketing data. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then prioritize the collection of highly relevant data.

[0077] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user mentions a specific brand on social media, the data collection unit can collect data related to that brand. It can also collect data related to an event if a user participates in a specific event on social media. Furthermore, if a user discusses a specific topic on social media, the data collection unit can collect data related to that topic. This allows for the collection of relevant data by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI analyze a user's social media activity, and the AI ​​can collect relevant data.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can display the analysis results using a simple, highly visual graph. If the user is relaxed, the analysis unit can also provide a report with detailed data. Furthermore, if the user is in a hurry, the analysis unit can provide a concise summary. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI, which can then adjust the presentation of the analysis.

[0079] The analytics department can adjust the level of detail of its analysis based on the importance of the data. For example, it can perform detailed analysis on important data to provide deeper insights, while performing simplified analysis on less important data. Furthermore, it can focus its analysis on data related to specific marketing campaigns. This allows for detailed analysis of important data by adjusting the level of detail based on its importance. Some or all of the above processes in the analytics department may be performed using AI, for example, or not. For example, the analytics department can have AI assess the importance of the data, and the AI ​​can adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, it can apply a clustering algorithm to purchase history data to identify customer segments. It can also apply a path analysis algorithm to website access logs to analyze user behavior patterns. Furthermore, it can apply a sentiment analysis algorithm to social media data to understand user sentiment. By applying different analysis algorithms depending on the data category, more accurate analysis results can be obtained. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can have AI classify the data categories, and the AI ​​can apply an appropriate analysis algorithm.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide the analysis using visually stimulating graphs and charts. This allows for more appropriate analysis results by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI, which can then adjust the length of the analysis.

[0082] The analysis department can prioritize analyses based on the timing of data collection. For example, it can prioritize analyzing the latest data to grasp real-time trends. It can also analyze historical data to grasp long-term trends. Furthermore, it can focus on analyzing data from a specific period to grasp trends during that period. This allows for real-time trend understanding by prioritizing analyses based on the timing of data collection. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can have AI evaluate the timing of data collection, and the AI ​​can determine the analysis priorities.

[0083] The analysis department can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis department can prioritize the analysis of highly relevant data to gain important insights. It can also postpone the analysis of less relevant data. Furthermore, the analysis department can prioritize the analysis of data related to a specific marketing campaign. This allows for important insights to be gained by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can have AI evaluate the relevance of the data, and the AI ​​can adjust the order of analysis.

[0084] The identification unit can estimate the user's emotions and adjust the criteria for identifying customer segments based on the estimated emotions. For example, if the user is relaxed, the identification unit can classify the customer using detailed segmentation criteria. If the user is in a hurry, the identification unit can also classify the customer using simplified segmentation criteria. Furthermore, if the user is excited, the identification unit can classify the customer based on specific interests or concerns. This allows for more appropriate customer classification by adjusting the criteria for identifying customer segments according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI or not using AI. For example, the identification unit can input user emotion data into an AI, which can then adjust the criteria for identifying customer segments.

[0085] The identification unit can improve the accuracy of customer segmentation based on customer interactions. For example, the identification unit can analyze social networks between customers and identify segments based on their interactions. It can also compare purchase histories between customers and classify customers with common patterns into the same segment. Furthermore, the identification unit can consider customer mutual evaluations to identify highly reliable segments. This improves the accuracy of identification by considering customer interactions. Some or all of the above processing in the identification unit may be performed using AI, for example, or not. For example, the identification unit can input customer interaction data into AI, which can then improve the accuracy of identification.

[0086] The identification unit can identify customer segments by considering customer attribute information. For example, the identification unit can identify segments based on customer attribute information such as age, gender, and income. The identification unit can also identify segments by considering the customer's geographical location information. Furthermore, the identification unit can identify segments based on the customer's purchase history and browsing history. This makes it possible to identify customer segments with higher accuracy by considering customer attribute information. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input customer attribute information into AI, and the AI ​​can identify customer segments.

[0087] The identification unit can estimate the user's emotions and adjust the display order of identified customer segments based on the estimated user emotions. For example, if the user is in a hurry, the identification unit may prioritize displaying important customer segments. It may also prioritize displaying detailed customer segments if the user is relaxed. Furthermore, if the user is excited, the identification unit may prioritize displaying visually appealing customer segments. This allows for more appropriate information to be provided by adjusting the display order of customer segments according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input user emotion data into an AI, which can then adjust the display order of customer segments.

[0088] The identification unit can identify customer segments while considering the geographical distribution of customers. For example, the identification unit can classify customers concentrated in a specific region into the same segment. It can also classify geographically dispersed customers into different segments. Furthermore, the identification unit can formulate regional marketing strategies based on geographical distribution. This allows for the formulation of regional marketing strategies by considering the geographical distribution of customers. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input customer geographical distribution data into AI, which can then identify customer segments.

[0089] The identification unit can improve the accuracy of customer segment identification based on relevant literature. For example, the identification unit can update the criteria for customer segment identification by referring to the latest marketing research. It can also refer to past success stories and apply effective segment identification methods. Furthermore, the identification unit can improve the accuracy of segment identification by referring to industry best practices. In this way, the accuracy of customer segment identification can be improved by referring to relevant literature. Some or all of the above processes in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input relevant literature into AI, and the AI ​​can update the criteria for customer segment identification.

[0090] The suggestion unit can estimate the user's emotions and adjust the presentation of the advertising strategy based on those emotions. For example, if the user is relaxed, the suggestion unit can suggest a detailed advertising strategy. If the user is in a hurry, it can suggest a concise and to-the-point advertising strategy. Furthermore, if the user is excited, it can suggest a visually appealing advertising strategy. By adjusting the presentation of the advertising strategy according to the user's emotions, a more effective advertising strategy can be proposed. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI, which can then adjust the presentation of the advertising strategy.

[0091] The proposal department can adjust the level of detail in its advertising strategy proposals based on the importance of each customer segment. For example, it can propose a detailed advertising strategy to important customer segments, and a simplified strategy to less important customer segments. Furthermore, it can propose a more focused advertising strategy to customer segments relevant to specific marketing campaigns. By adjusting the level of detail based on the importance of each customer segment, it can propose more effective advertising strategies. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can have AI evaluate the importance of customer segments, and the AI ​​can adjust the level of detail in the proposals.

[0092] The proposal department can apply different proposal algorithms depending on the customer segment category when proposing advertising strategies. For example, the proposal department can propose a premium advertising strategy to a customer segment that prefers expensive products. It can also propose a promotion-focused advertising strategy to a customer segment that prefers discounts. Furthermore, the proposal department can propose a personalized advertising strategy to a customer segment with specific interests. By applying different proposal algorithms depending on the customer segment category, it is possible to propose more effective advertising strategies. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can have AI classify customer segment categories, and the AI ​​can apply an appropriate proposal algorithm.

[0093] The suggestion unit can estimate the user's emotions and adjust the length of the advertising strategy based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can suggest a short, to-the-point advertising strategy. If the user is relaxed, the suggestion unit can suggest a longer advertising strategy that includes detailed explanations. Furthermore, if the user is excited, the suggestion unit can suggest an advertising strategy with visually stimulating effects. This allows for the suggestion of a more effective advertising strategy by adjusting the length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI, which can then adjust the length of the advertising strategy.

[0094] The proposal department can prioritize advertising strategies based on the timing of customer segment collection when proposing advertising strategies. For example, the proposal department can prioritize proposing advertising strategies to the most recent customer segments. It can also postpone proposing advertising strategies to older customer segments. Furthermore, the proposal department can propose advertising strategies tailored to customer segments within a specific period. By prioritizing proposals based on the timing of customer segment collection, it is possible to propose more effective advertising strategies. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can have AI evaluate the timing of customer segment collection, and the AI ​​can determine the priority of proposals.

[0095] The proposal department can adjust the order of advertising strategies based on the relevance of customer segments when proposing advertising strategies. For example, the proposal department can prioritize proposing advertising strategies to highly relevant customer segments. It can also postpone proposing advertising strategies to less relevant customer segments. Furthermore, the proposal department can focus on proposing advertising strategies to customer segments related to specific marketing campaigns. By adjusting the order of proposals based on the relevance of customer segments, it is possible to propose more effective advertising strategies. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can have AI evaluate the relevance of customer segments, and the AI ​​can adjust the order of proposals.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] AI assistants for marketing analytics can also be equipped with a predictive function. This predictive function can forecast future customer behavior and market trends based on collected data and historical trends. For example, it can analyze past purchase history and seasonal trends to predict what products will sell in the next season. It can also identify products and services that are likely to become popular based on social media trend data. Furthermore, it can predict future purchasing behavior based on customer life events (marriage, childbirth, moving, etc.). This allows marketers to understand future market trends and develop proactive marketing strategies.

[0098] The AI ​​assistant for marketing analytics support can also include a feedback function. This feedback function evaluates the effectiveness of proposed advertising strategies after implementation and feeds the results back into the system. For example, the feedback function monitors click-through rates and conversion rates of advertising campaigns and provides the results to the proposal function. It can also collect customer responses and feedback and incorporate them into future advertising strategies. Furthermore, the feedback function can analyze the effectiveness of competitors' advertising strategies and apply that knowledge to their own. This allows marketing professionals to understand the effectiveness of their advertising strategies in real time and quickly implement improvements.

[0099] Marketing analytics support AI assistants can also be equipped with a personalization function. This personalization function generates optimal advertising messages and promotions for each customer. For example, it can create personalized advertising messages based on a customer's purchase and browsing history. It can also recommend specific products and services based on a customer's interests. Furthermore, it can provide customized promotions tailored to a customer's lifestyle and preferences. This allows marketers to implement more effective advertising strategies for each individual customer.

[0100] The AI ​​assistant for marketing analysis support can also include a competitive analysis department. This department analyzes the marketing strategies and market share of competitors and incorporates those insights into the company's own strategy. For example, it can monitor the effectiveness of competitors' advertising campaigns and identify their success factors. It can also analyze competitors' product lineups and pricing to inform the company's own product strategy. Furthermore, it can collect customer reviews and feedback from competitors to improve the company's own products and services. This allows marketing professionals to understand competitor trends and develop competitive marketing strategies.

[0101] The AI ​​assistant for marketing analytics support can also include a cross-channel analytics function. This function comprehensively analyzes customer behavior across multiple marketing channels to evaluate overall marketing effectiveness. For example, it integrates data from social media, email marketing, and website access logs to identify customer behavior patterns across channels. It can also compare the effectiveness of each channel to identify the most effective one. Furthermore, it can analyze which channels customers achieve the most conversions through and incorporate these results into marketing strategies. This allows marketers to effectively leverage multiple channels and maximize overall marketing effectiveness.

[0102] A marketing analytics support AI assistant can have a data collection unit that estimates the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the data collection unit can shorten the timing of data collection to collect data quickly. In this way, by adjusting the timing of data collection according to the user's emotions, the user's burden is reduced and detailed data can be collected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into an AI, which can then adjust the timing of data collection.

[0103] A marketing analytics support AI assistant can have its data collection unit estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is excited, the data collection unit may prioritize collecting the latest trending data. If the user is relaxed, the data collection unit may also prioritize collecting detailed analytical data. Furthermore, if the user is stressed, the data collection unit may prioritize collecting only the most important data. This allows for the collection of more relevant data by prioritizing the data to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI, which can then determine the priority of the data to collect.

[0104] A marketing analytics support AI assistant can have its analytics department estimate the user's emotions and adjust the presentation of the analysis based on those estimated emotions. For example, if the user is stressed, the analytics department can display the analysis results using simple, easy-to-understand graphs. If the user is relaxed, the analytics department can also provide a report with detailed data. Furthermore, if the user is in a hurry, the analytics department can provide a concise summary. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analytics department may be performed using AI or not. For example, the analytics department can input user emotion data into the AI, which can then adjust the presentation of the analysis.

[0105] A marketing analytics support AI assistant can have a specific unit estimate a user's emotions and adjust the criteria for identifying customer segments based on the estimated emotions. For example, if a user is relaxed, the unit can classify the customer using detailed segmentation criteria. If a user is in a hurry, the unit can classify the customer using simplified segmentation criteria. Furthermore, if a user is excited, the unit can classify the customer based on specific interests or concerns. This allows for more appropriate customer classification by adjusting the criteria for identifying customer segments according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the specific unit may be performed using AI or not using AI. For example, the specific unit can input user emotion data into an AI, which can then adjust the criteria for identifying customer segments.

[0106] A marketing analytics support AI assistant can estimate a user's emotions and adjust the presentation of advertising strategies based on those emotions. For example, if a user is relaxed, the suggestion unit can suggest a detailed advertising strategy. If a user is in a hurry, it can suggest a concise and to-the-point advertising strategy. Furthermore, if a user is excited, it can suggest a visually appealing advertising strategy. By adjusting the presentation of advertising strategies according to the user's emotions, more effective advertising strategies can be proposed. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI, which can then adjust the presentation of advertising strategies.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit collects data from multiple marketing channels. For example, it collects data such as social media data, email marketing data, and website access logs. The data collection unit obtains social media data via API, email marketing data from the mail server, and website access logs from the web server. Step 2: The analysis unit comprehensively analyzes the data collected by the collection unit. For example, it performs data preprocessing, such as data cleansing and normalization. Furthermore, it can perform database merging and data warehouse construction. Step 3: The identification unit automatically identifies customer segments based on the analysis results obtained by the analysis unit. For example, it classifies customers using a clustering algorithm, and then classifies them into segments that prefer expensive products or segments that prefer discounts based on their purchase history and browsing history. Step 4: The proposal department proposes advertising strategies based on the customer segments identified by the specific department. For example, they propose optimal advertising messages and promotions for specific customer segments and create the advertising messages and design the promotions.

[0109] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] For example, the collection unit can collect SNS data and email marketing data using the camera 42 and communication I / F 44 of the smart device 14. For example, the analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and performs preprocessing and cleansing of the collected data. For example, the identification unit is implemented by the identification processing unit 290 of the data processing device 12 and automatically identifies customer segments using a clustering algorithm. For example, the proposal unit is implemented by the identification processing unit 290 of the data processing device 12 and proposes the optimal advertising strategy based on the identified customer segments. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] For example, the data collection unit can collect SNS data and email marketing data using the camera 42 and communication I / F 44 of the smart glasses 214. For example, the analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and performs preprocessing and cleansing of the collected data. For example, the identification unit is implemented by the identification processing unit 290 of the data processing device 12 and automatically identifies customer segments using a clustering algorithm. For example, the proposal unit is implemented by the identification processing unit 290 of the data processing device 12 and proposes the optimal advertising strategy based on the identified customer segments. The correspondence between each unit and the device and control unit is not limited to the examples described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] For example, the collection unit can collect SNS data and email marketing data using the camera 42 and communication I / F 44 of the headset terminal 314. For example, the analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and performs preprocessing and cleansing of the collected data. For example, the identification unit is implemented by the identification processing unit 290 of the data processing device 12 and automatically identifies customer segments using a clustering algorithm. For example, the proposal unit is implemented by the identification processing unit 290 of the data processing device 12 and proposes the optimal advertising strategy based on the identified customer segments. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] For example, the collection unit can collect SNS data and email marketing data using the camera 42 and communication I / F 44 of the robot 414. For example, the analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and performs preprocessing and cleansing of the collected data. For example, the identification unit is implemented by the identification processing unit 290 of the data processing device 12 and automatically identifies customer segments using a clustering algorithm. For example, the proposal unit is implemented by the identification processing unit 290 of the data processing device 12 and proposes the optimal advertising strategy based on the identified customer segments. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A data collection unit that collects data from multiple marketing channels, An analysis unit that comprehensively analyzes the data collected by the aforementioned collection unit, A identification unit that automatically identifies customer segments based on the analysis results obtained by the aforementioned analysis unit, A proposal unit that proposes advertising strategies based on customer segments identified by the aforementioned identification unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data from social media, email marketing, and website access logs. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is We analyze the collected data in an integrated manner to analyze trends in customer behavior. The system described in Appendix 1, characterized by the features described herein. (Note 4) The specified part is, Classify customers based on their purchase or browsing history. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We propose the most suitable advertising message or promotion for a specific customer segment. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Estimate user sentiment, and adjust the timing of data collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Dynamically adjust the data collection frequency for each marketing channel and select the optimal data collection strategy. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current purchasing intent or areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system prioritizes collecting data that is highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The specified part is, We estimate user sentiment and adjust the criteria for identifying customer segments based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The specified part is, When identifying customer segments, improve accuracy based on customer relationships. The system described in Appendix 1, characterized by the features described herein. (Note 20) The specified part is, When identifying customer segments, the identification is performed based on customer attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The specified part is, It estimates user sentiment and adjusts the display order of identified customer segments based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The specified part is, When identifying customer segments, consider the geographical distribution of customers. The system described in Appendix 1, characterized by the features described herein. (Note 23) The specified part is, When identifying customer segments, improve the accuracy of the identification based on relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, We estimate user emotions and adjust the advertising strategy's presentation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When proposing advertising strategies, adjust the level of detail in the proposal based on the importance of each customer segment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When proposing advertising strategies, different proposal algorithms are applied depending on the customer segment category. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, It estimates user sentiment and adjusts the length of the ad strategy based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When proposing advertising strategies, prioritize proposals based on when customer segment data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When proposing advertising strategies, adjust the order of suggestions based on the relevance of customer segments. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data from multiple marketing channels, An analysis unit that comprehensively analyzes the data collected by the aforementioned collection unit, A identification unit that automatically identifies customer segments based on the analysis results obtained by the aforementioned analysis unit, A proposal unit that proposes advertising strategies based on customer segments identified by the aforementioned identification unit, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is We collect data from social media, email marketing, and website access logs. The system according to feature 1.

3. The aforementioned analysis unit is We analyze the collected data in an integrated manner to analyze trends in customer behavior. The system according to feature 1.

4. The specified part is, Classify customers based on their purchase or browsing history. The system according to feature 1.

5. The aforementioned proposal section is, We propose the most suitable advertising message or promotion for a specific customer segment. The system according to feature 1.

6. The aforementioned collection unit is Estimate user sentiment, and adjust the timing of data collection based on the estimated user sentiment. The system according to feature 1.

7. The aforementioned collection unit is Dynamically adjust the data collection frequency for each marketing channel and select the optimal data collection strategy. The system according to feature 1.

8. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current purchasing intent or areas of interest. The system according to feature 1.

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