Intelligent sales visit content generation method and system based on large model

Abstract

The invention relates to a sales visit content intelligent generation method and system based on a large model. Comprising an access module used for recording customer access records, a collection module used for collecting customer purchase intentions, a comparison module used for collecting competitor information, an auxiliary module used for assisting sales personnel in communication and a data module used for data analysis. Through the intelligent generation method based on the large model, the information preparation efficiency and accuracy in the sales visit process are improved, and the problem of low efficiency of information collection and content preparation in traditional sales visit is solved; specifically, social public opinion data related to customers are captured in real time, personalized visit materials are automatically generated in combination with a large model, and information is stored in a CRM system through a sales personnel feedback mechanism, so that the response speed of sales work and the accuracy of visit content are improved, and customer relationship management is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of sales data processing, and more specifically, to a method and system for intelligently generating sales visit content based on a large model. Background Art

[0002] In the process of sustainable and stable development of an enterprise, product sales play a direct and vital role in market competition. With the increasingly fierce product market competition and the continuous expansion of enterprise scale, the homogeneous competition of products with the same functions and uses among different enterprises under the information background is obvious, and the competitive pressure in sales of similar products is increasing, which also faces greater challenges in expanding the sales scope in the future.

[0003] A sales data processing method and device are disclosed in a Chinese patent (publication number: CN112835994A), the method comprising: receiving sales visit information uploaded by sales personnel and after-sales service information uploaded by customer service personnel respectively; classifying the sales visit information according to a preset visit information classification format, adding the classified sales visit information to a preset first data table, generating a visit data table, and classifying the after-sales service information according to a preset after-sales information classification format, adding the classified after-sales service information to a preset second data table, generating an after-sales data table; sending the after-sales data table to the sales terminal of the sales personnel, so that the sales terminal can display the after-sales data table; sending the visit data table to the customer service terminal of the customer service personnel. The present invention can generate corresponding data tables for sales visit information and after-sales service information for recording and unified management, which is convenient for users to view and can improve management efficiency.

[0004] However, there are still some problems with the above solution: Data level: Sales data is completely dependent on sales reporting and lacks proactive data insight capabilities.

[0005] Algorithmic level: Relying on standard tables to analyze and process data, lacking the ability to understand and summarize complex knowledge. Summary of the invention

[0006] In view of the problems existing in the prior art, the purpose of the present invention is to provide a method and system for intelligently generating sales visit content based on a large model to solve the technical problems mentioned in the background technology.

[0007] To solve the above problems, the present invention adopts the following technical solutions: A method and system for intelligently generating sales visit content based on a large model, comprising an access module for recording customer visit records, a collection module for collecting customer purchase intentions, a comparison module for collecting competitor information, an auxiliary module for assisting sales staff in communication, and a data module for data analysis, wherein: The access module includes a face recognition camera for identifying customers and a microphone for collecting the sound of customers during their visits; The collection module includes a client , built-in Crawler tool 1 in ; The comparison module includes a crawler tool 2 built into the computer of the enterprise, a crawler tool 3 built into the sales terminal of the sales staff of the enterprise, and a crawler tool 4 for collecting industry platform data; The auxiliary module includes a handheld sales terminal and a headset; The data module includes a Sentiment analysis software based on sentiment analysis algorithm, cloud database, Data processing software for cluster analysis methods, system.

[0008] Preferably, the sentiment analysis software performs sentiment classification on the captured customer information content, identifies the customer's emotional tendencies towards certain topics including positive, negative, and neutral, and records and stores the products corresponding to such emotions.

[0009] Furthermore, based on Data processing software using cluster analysis methods analyzes competitors' behaviors and strategic directions in the market and generates analysis reports including competitors' recent product dynamics and market performance.

[0010] Furthermore, the crawler tool 1, the crawler tool 2, the crawler tool 3, and the crawler tool 4 adopt or technology.

[0011] A method for intelligently generating sales call content based on a large model, which is implemented based on the aforementioned system for intelligently generating sales call content based on a large model, comprising: S1: based on the audio data collected by the microphone, the aforementioned audio data is converted into a text field, and a data module is used to count key words in the text field, and the key word determination basis includes the frequency of occurrence of the word , before and after the emotional words S2: storing the key words and corresponding text fields in the cloud database, determining the customer's intended purchase demand based on the key words analysis, and transmitting the intended purchase demand to the handheld sales terminal through the data module.

[0012] Preferably, the frequency of occurrence of a certain word in the text field , or a certain word is the same as the key word preset in the data module system, that is, the word is determined to be a key word, and the customer's purchase intention is profiled by the key word, and product information that meets the customer's purchase intention is pushed to the handheld sales terminal. The salesperson shows the instructions to the customer and collects the customer's audio information again through the microphone, repeats S1, and re-checks the products pushed to the handheld sales terminal. If the review is the same, the salesperson is reminded to focus on the introduction. If the review is different, the new product is pushed to the handheld sales terminal, and the salesperson introduces the products on the handheld sales terminal to the customer in turn.

[0013] Furthermore, the text field contains a certain sentiment word before and after each The words in the range are defined as keywords, and the above keywords are compared with the characteristics of the product to determine the characteristic points that customers need to buy the product , the customer's purchase intention is profiled by the feature points, and product information that meets the customer's purchase intention is pushed to the handheld sales terminal. The salesperson shows the instructions to the customer and collects the customer's audio information again through the microphone. S1 is repeated to re-check the products pushed to the handheld sales terminal. If the review is the same, the salesperson is reminded to focus on the introduction. If the review is different, a new product is pushed to the handheld sales terminal, and the salesperson introduces the products on the handheld sales terminal to the customer in turn.

[0014] Compared with the prior art, the advantages of the present invention are: Through the intelligent generation method based on the big model, the efficiency and accuracy of information preparation in the sales visit process are improved, and the inefficiency of information collection and content preparation in traditional sales visits is solved; specifically, the present invention captures social public opinion data related to customers in real time, combines the big model to automatically generate personalized visit materials, and stores the information in the CRM system through the sales staff feedback mechanism, thereby improving the response speed of sales work, the accuracy of visit content, and optimizing customer relationship management. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The figure is a schematic diagram of the overall structure of a method and system for intelligently generating sales visit content based on a large model according to the present invention. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention; it is obvious that the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments, and all other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making creative work are within the scope of protection of the present invention.

[0017] Example: See also Figure 1 A sales visit content intelligent generation method and system based on a large model, including an access module for recording customer visit records, a collection module for collecting customer purchase intentions, a comparison module for collecting competitor information, an auxiliary module for assisting sales staff communication, and a data module for data analysis, wherein The access module includes a face recognition camera for identifying customers and a microphone for collecting the sound of customers during their visits; The collection module includes a client , built-in Crawler tool 1 in ; The comparison module includes a crawler tool 2 built into the computer of the enterprise, a crawler tool 3 built into the sales terminal of the sales staff of the enterprise, and a crawler tool 4 for collecting industry platform data; The auxiliary module includes a handheld sales terminal and a headset; The data module includes a Sentiment analysis software based on sentiment analysis algorithm, cloud database, Data processing software for cluster analysis methods, system; Specific: S1: Based on the audio data collected by the microphone, the audio data is converted into text fields, and the data module counts the key words in the text fields. The key words are determined based on the frequency of occurrence of the words. , before and after the emotional words Vocabulary within a range, key words preset by the data module system; The frequency of a word in the text field , or a certain word is the same as a key word preset in the data module system, that is, the word is determined to be a key word; The text field contains a certain sentiment word before and after each The words in the range are defined as keywords, and the above keywords are compared with the characteristics of the product to determine the characteristic points that customers need to buy the product ,Profile the customer's purchasing intention by feature points; S2: storing the key words and the corresponding text fields in the cloud database, determining the customer's intended purchase demand based on the key words analysis, and transmitting the intended purchase demand to the handheld sales terminal through the data module; The salesperson shows the instructions to the customer and collects the customer's audio information again through the microphone, repeats S1, and re-checks the products pushed to the handheld sales terminal. If the review is the same, the salesperson is reminded to focus on the introduction. If the review is different, the new product is pushed to the handheld sales terminal, and the salesperson introduces the products on the handheld sales terminal to the customer in turn.

[0018] In this embodiment, the sentiment analysis software performs sentiment classification on the captured customer information content, identifies the customer's emotional tendencies on certain topics, including positive, negative, and neutral, and records and stores the products corresponding to such emotions.

[0019] In this embodiment, based on Data processing software using cluster analysis methods analyzes competitors' behaviors and strategic directions in the market and generates analysis reports including competitors' recent product dynamics and market performance.

[0020] In this embodiment, the crawler tool 1, the crawler tool 2, the crawler tool 3, and the crawler tool 4 adopt or technology.

[0021] The working process of the present invention is as follows: Customer information analysis implementation steps: Step 1: Configure the crawler tool, select the social platform to be crawled, and set the crawling frequency (such as crawling once every hour or once a day).

[0022] Step 2: Use sentiment analysis models to classify the captured social media content and identify customers’ emotional tendencies towards certain topics (such as positive, negative, and neutral).

[0023] Step 3: The system automatically generates an analysis report, which includes the customer's interest changes, sentiment trends and hot topics.

[0024] Step 4: The report is pushed to sales staff in an automated manner to ensure real-time information.

[0025] Customer competitor data monitoring (implementation example): Equipment and system configuration: Configure a dedicated data capture module related to competitors, and use the API interface to capture competitors' news, blog posts, and product release information.

[0026] Through machine learning algorithms (such as Cluster analysis) to analyze competitors’ behavior and strategic direction in the market.

[0027] Implementation steps: Step 1: Configure customized crawling rules for each competitor to regularly obtain competitor marketing activities, published news or product information.

[0028] Step 2: Evaluate the content of competitor news through sentiment analysis to extract market impact points and trend changes.

[0029] Step 3: Generate an analysis report, which includes competitors’ recent product dynamics, market performance, etc., for sales staff’s reference.

[0030] Data monitoring of the customer's industry (example of implementation): Equipment and system configuration: Use industry data service platforms (such as Statista, IBISWorld) combined with customized crawler tools to obtain industry reports, news and market trend data.

[0031] Use natural language processing algorithms (such as TF-IDF) to extract keywords from the captured industry content to ensure that the latest trends in the industry can be identified.

[0032] Implementation steps: Step 1: Update industry-related information in real time by regularly capturing industry reports, market research data, etc.

[0033] Step 2: Use data mining technology to conduct in-depth analysis of industry data to identify the industry's growth potential and risk points.

[0034] Step 3: Generate industry analysis reports and push them to sales staff to help them understand industry development trends and potential opportunities.

[0035] CRM System Customer visit records (implementation example): Equipment and system configuration: Integrate the user interface (UI) in the CRM system to provide a simple visit record interface, including customer name, visit time, visit goal, and customer feedback.

[0036] It supports voice input and speech-to-text functions, using speech recognition technologies such as Google Speech-to-Text or IBM Watson to help sales staff quickly record visit information.

[0037] Implementation steps: Step 1: During a visit, sales staff can use a mobile device (such as a tablet or mobile phone) to record visit information in real time, or quickly input customer feedback through a voice recognition system.

[0038] Step 2: The information is automatically stored in the CRM system and a customer visit log is generated.

[0039] Step 3: The system analyzes customer visit data, generates follow-up visit suggestions, and pushes them to sales staff.

[0040] Customer cooperation records (examples of implementation methods): Equipment and system configuration: In the CRM system, a cooperation record module is provided to record the cooperation process between customers and the company, including contract signing, product sales, project progress and other information.

[0041] Configure the data integration interface to connect with the financial and contract management systems to achieve automatic synchronization of contract changes, payment records, etc.

[0042] Implementation steps: Step 1: During the customer cooperation process, the salesperson enters the cooperation information into the CRM system and the system automatically generates a cooperation history record.

[0043] Step 2: The system automatically evaluates the customer's cooperation potential based on their cooperation status and provides sales staff with customer classification and follow-up suggestions.

[0044] Step 3: Combine cooperation records to generate a long-term value assessment report for the customer to provide data support for sales decisions.

[0045] The above is only a preferred specific implementation of the present invention; however, the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solution and its improved conception within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A sales visit content intelligent generation system based on a large model, characterized by: It includes an access module for recording customer access records, a collection module for collecting customer purchase intentions, a comparison module for collecting competitor information, an auxiliary module for assisting sales staff in communication, and a data module for data analysis, among which: The access module includes a face recognition camera for identifying customers and a microphone for collecting the sound of customers during their visits; The collection module includes a client , built-in Crawler tool 1 in ; The comparison module includes a crawler tool 2 built into the computer of the enterprise, a crawler tool 3 built into the sales terminal of the sales staff of the enterprise, and a crawler tool 4 for collecting industry platform data; The auxiliary module includes a handheld sales terminal and a headset; The data module includes a Sentiment analysis software based on sentiment analysis algorithm, cloud database, Data processing software for cluster analysis methods, system.

2. According to claim 1, a sales call content intelligent generation system based on a large model is characterized by: The sentiment analysis software performs sentiment classification on the captured customer information content, identifies the customer's emotional tendencies towards certain topics, including positive, negative, and neutral, and records and stores the products corresponding to such emotions.

3. According to claim 1, a sales call content intelligent generation system based on a large model is characterized by: Based on the Data processing software using cluster analysis methods analyzes competitors' behaviors and strategic directions in the market and generates analysis reports including competitors' recent product dynamics and market performance.

4. The sales call content intelligent generation system based on a large model according to claim 1 is characterized by: The crawler tool 1, the crawler tool 2, the crawler tool 3, and the crawler tool 4 adopt or technology.

5. A method for intelligently generating sales call content based on a large model, which is implemented based on a system for intelligently generating sales call content based on a large model as described in any one of claims 1 to 4, characterized in that: include: S1: Based on the audio data collected by the microphone, the audio data is converted into text fields, and the data module counts the key words in the text fields. The key words are determined based on the frequency of occurrence of the words. , before and after the emotional words S2: storing the key words and corresponding text fields in the cloud database, determining the customer's intended purchase demand based on the key words analysis, and transmitting the intended purchase demand to the handheld sales terminal through the data module.

6. The method for intelligently generating sales visit content based on a large model according to claim 5, characterized in that: The frequency of a word in the text field , or a certain word is the same as the key word preset in the data module system, that is, the word is determined to be a key word, and the customer's purchase intention is profiled by the key word, and product information that meets the customer's purchase intention is pushed to the handheld sales terminal. The salesperson shows the instructions to the customer and collects the customer's audio information again through the microphone, repeats S1, and re-checks the products pushed to the handheld sales terminal. If the review is the same, the salesperson is reminded to focus on the introduction. If the review is different, the new product is pushed to the handheld sales terminal, and the salesperson introduces the products on the handheld sales terminal to the customer in turn.

7. The method for intelligently generating sales visit content based on a large model according to claim 5, characterized in that: The text field contains a certain sentiment word before and after each The words in the range are defined as keywords, and the above keywords are compared with the characteristics of the product to determine the characteristic points that customers need to buy the product , the customer's purchase intention is profiled by the feature points, and product information that meets the customer's purchase intention is pushed to the handheld sales terminal. The salesperson shows the instructions to the customer and collects the customer's audio information again through the microphone. S1 is repeated to re-check the products pushed to the handheld sales terminal. If the review is the same, the salesperson is reminded to focus on the introduction. If the review is different, a new product is pushed to the handheld sales terminal, and the salesperson introduces the products on the handheld sales terminal to the customer in turn.

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