A multi-al tool linkage sales training empowerment and customer management system and method

Abstract

The application discloses a kind of sales training empowerment and customer management system and method of multi-AI tool linkage, it is related to AI technology and enterprise service cross technical field.The system includes first AI tool module, second AI tool module and customer data module, first AI tool module gathers the interactive data of sales personnel and target customer, generates the first output data of the first output data and the second output data of the target customer demand label / portrait label after analysis, second AI tool module receives first output data and automatically generates special simulation sales training task for skill short board item, customer data module receives second output data and updates the customer portrait of target customer, simultaneously based on the marketing action of the target customer of updated customer portrait, by which, through multi-AI tool linkage, data barrier between sales training and customer management is broken through, the pertinence of staff training and the efficiency of customer operation are significantly improved.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary field of AI (Artificial Intelligence) technology and enterprise services. Specifically, it involves sales staff training empowerment and customer information lifecycle management technology based on AI tools such as LLM (Large Language Model), TTS (Text-to-Speech), ASR (Automatic Speech Recognition, including speech-to-text), and NLP (Natural Language Processing). It also provides a sales training empowerment and customer information management system and method that integrates multiple AI tools, which is particularly suitable for scenarios such as improving sales capabilities, optimizing customer service, and accurately operating customer information in gas service companies. Background Technology

[0002] Currently, with the rapid development of artificial intelligence technology, more and more companies are applying AI tools to sales training and customer management. Common AI tools include: AI training tools (such as AI coaching systems, used to simulate sales scenarios to train employees in sales techniques), AI outbound calling tools (for automated marketing outreach), AI / human customer service systems (for customer service and response to requests), smart employee badges (for collecting real-world recordings of sales staff), and CRM (Customer Relationship Management) systems (for customer information management), etc.

[0003] However, in practical applications, all of the aforementioned AI tools are independent systems, lacking effective data interoperability and business collaboration mechanisms, specifically manifested in the following aspects: (1) Data fragmentation, forming "data silos", that is, AI coaching, AI outbound calling, AI / human customer service, smart badges and CRM system operate independently and data cannot flow; for example, sales practice recordings collected by smart badges cannot be fed back to AI coaching system to optimize training content; the demand information generated by AI outbound calling or customer service system interaction with customers cannot be automatically synchronized to CRM system to form a complete customer profile; the customer historical data in CRM system cannot provide effective support for AI outbound calling precision marketing; this "data silo" phenomenon causes the value of each tool to fail to form a synergy, and the company's data assets are severely fragmented; (2) The improvement of employee skills lacks specificity. The existing sales training tools are mostly in a "one-way output" mode, that is, standardized training courses or scripts are pushed to employees, but they cannot be accurately analyzed based on the actual sales performance of employees. For example, although smart badges can record audio, they lack the ability to deeply analyze the semantic content of the recordings and cannot identify the specific problems that employees have in communicating with customers, such as "not clearly explaining the energy-saving highlights of the product", "a harsh service attitude" or "not following the standard sales process". Therefore, the training improvement lacks a precise direction, and employees have difficulty obtaining targeted ability improvement, which greatly reduces the training effect. (3) Insufficient customer demand mining and low customer data utilization rate. In the existing system, although tools such as smart ID cards, AI outbound calls and AI customer service generate a lot of interactions with customers, the customer needs implied in the interaction process (such as "planning to replace stove", "concern about product safety" and "price sensitivity") cannot be effectively extracted and structured and recorded. Even if some information is recorded, it cannot be integrated with the historical customer information in the CRM system due to inconsistent data standards, resulting in fragmented and incomplete customer profiles. The CRM system plays more of a "static record book" role and is difficult to automatically trigger precise marketing based on real-time updated customer profiles. A large number of high-value customer data are idle or lost.

[0004] In summary, the existing technology lacks a new solution that can effectively link multiple AI tools to achieve data interoperability and business collaboration. This results in the separation of the two major business links of sales training and customer data management. It is impossible to improve employee capabilities through practical data feedback training, nor can it drive marketing conversion through accurate customer profiles, resulting in low overall operational efficiency. Summary of the Invention

[0005] The purpose of this invention is to provide a sales training empowerment and customer data management system and method that integrates multiple AI tools, in order to solve the problems of data silos and fragmentation, lack of targeted employee skill improvement and / or low customer data utilization caused by the lack of effective data interoperability and business collaboration mechanisms between existing AI systems.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a sales training empowerment and customer data management system that integrates multiple AI tools, including a first AI tool module, a second AI tool module and a customer data module; The first AI tool module is used to first collect sales and service interaction data generated by the communication between sales personnel and target customers, then analyze the sales and service interaction data and generate first output data and second output data based on the analysis results. The first output data is used to characterize the skill gaps of the sales personnel, and the second output data is used to characterize the first demand tag and / or first profile tag of the target customer. The second AI tool module is communicatively connected to the first AI tool module, and is used to receive the first output data and, in response to the first output data, automatically generate a special simulated sales training task for the skill deficiency item. The customer data module is communicatively connected to the first AI tool module, and is used to receive the second output data and update the customer profile of the target customer in response to the second output data; The customer data module is also used to automatically trigger marketing actions for the target customer based on the updated customer profile.

[0007] Based on the above-mentioned invention, a new solution is provided that can effectively link multiple AI tools to achieve data interoperability and business collaboration. This solution includes a first AI tool module, a second AI tool module, and a customer data module. The first AI tool module collects interaction data between sales personnel and target customers, analyzes it, and generates first output data representing the sales personnel's skill gaps and second output data representing the target customer's need tags / profile tags. The second AI tool module receives the first output data and automatically generates specific simulated sales training tasks targeting the skill gaps. The customer data module receives the second output data and updates the target customer's profile. Simultaneously, based on the updated customer profile, it automatically triggers marketing actions targeting the target customer. Thus, through the linkage of multiple AI tools, the data barriers between sales training and customer data management are broken down, achieving a closed-loop process of "real-world data collection → skill gap identification → specific training generation → customer need extraction → profile update → precise marketing triggering." This significantly improves the relevance of employee training and the efficiency of customer data operations, facilitating practical application and promotion.

[0008] In one possible design, a third AI tool module is also included, which is communicatively connected to the customer data module, wherein the third AI tool module is used to perform the marketing action; Based on the updated customer profile, marketing actions targeting the target customer are automatically triggered, including: Based on the updated customer profile, the system calls upon a pre-defined enterprise product inventory and marketing policy knowledge base, uses retrieval-enhanced generation technology to constrain the large language model to generate a personalized marketing plan suitable for the target customer, and triggers the third AI tool module to initiate the first AI robot to call the target customer for automated marketing according to the personalized marketing plan.

[0009] In one possible design, an excellent case selection module is also included, which is respectively connected to the customer data module, the first AI tool module, and the second AI tool module in communication. The excellent case screening module is used to respond to the signal that the customer status of the target customer in the customer data module has changed to a completed transaction. It automatically retrieves the historical sales and service interaction data corresponding to the target customer, calls the preset scoring model to score the historical sales and service interaction data, and then marks the historical sales and service interaction data with a score higher than a preset threshold as excellent interaction records and stores them in the experience library of the second AI tool module to optimize the simulated sales training task. The historical sales and service interaction data includes at least the audio data collected by the first AI tool module.

[0010] In one possible design, the second AI tool module is further configured to respond to the update signal of the experience base, acquire the excellent interaction records, and generate fine-tuning samples based on the excellent interaction records to supervise and fine-tune the preset simulated sales role model and / or simulated customer role model in the second AI tool module.

[0011] In one possible design, the sales and service interaction data is analyzed and first output data is generated based on the analysis results, including: By using a unified language model and combining it with multiple preset detection dimension prompts, semantic analysis is performed on the sales and service interaction data to identify sales process nodes that the sales personnel did not execute or product selling points that were not mentioned, which are then used as the skill deficiency items in the first output data.

[0012] In one possible design, a third AI tool module and a fourth AI tool module are also included, which are respectively communicatively connected to the customer data module; The third AI tool module is used to first collect marketing outbound call interaction data generated by the first AI robot / first natural person communicating with the target customer, then analyze the marketing outbound call interaction data and generate third output data based on the analysis results, wherein the third output data is used to characterize the target customer's second demand tag and / or second profile tag; The fourth AI tool module is used to first collect after-sales customer service interaction data generated by the second AI robot / second natural person communicating with the target customer, then analyze the after-sales customer service interaction data and generate fourth output data based on the analysis results, wherein the fourth output data is used to characterize the third demand tag and / or third profile tag of the target customer; Updating the customer profile of the target customer includes: based on a unified data standard, integrating the demand tags and / or profile tags received from the first AI tool module, the third AI tool module, and the fourth AI tool module with the basic customer information of the target customer pre-stored in the customer data module to generate a customer profile containing multi-dimensional tags.

[0013] In one possible design, based on the updated customer profile, marketing actions targeting the target customer are automatically triggered, including: Determine whether the confidence level of the updated demand tag in the customer profile exceeds a preset confidence threshold. If so, automatically trigger a marketing action for the target customer; otherwise, stop triggering a marketing action for the target customer. And / or, identify the urgency of the updated demand tags in the customer profile and determine whether the urgency exceeds a preset urgency threshold. If so, automatically trigger marketing actions for the target customer in real time; otherwise, automatically trigger marketing actions for the target customer at regular intervals or in sequence according to the queuing status of marketing tasks. And / or, verify whether the updated customer profile contains the preset minimum necessary fields. If so, automatically trigger marketing actions for the target customer; otherwise, stop triggering marketing actions for the target customer and generate an information completion task for the sales personnel.

[0014] In one possible design, marketing actions targeting the stated customer are automatically triggered, including: Determine whether the time from the most recent marketing timestamp to the current timestamp exceeds a preset time threshold. If so, automatically trigger a marketing action for the target customer. Otherwise, stop triggering the marketing action for the target customer until the time exceeds the preset time threshold. The most recent marketing timestamp refers to the execution timestamp of the most recent historical marketing action for the target customer. And / or, determine whether there is a marketing circuit breaker tag for the target customer in the customer data module. If so, stop triggering marketing actions for the target customer until the tag is canceled. Otherwise, automatically trigger marketing actions for the target customer. The marketing circuit breaker tag is marked when the target customer's marketing feedback intent is a clear rejection and is canceled when the target customer actively initiates a service request. The marketing feedback intent is extracted based on the results of historical marketing actions corresponding to the target customer.

[0015] In one possible design, the second AI tool module is also communicatively connected to the customer data module and is further configured to, when the special simulated sales training task is generated, obtain a customer profile of the target customer associated with the skill gap item from the customer data module, and convert the customer profile into role-building prompts to construct a simulated customer role with the customer characteristics of the target customer in the special simulated sales training task.

[0016] In one possible design, the second AI tool module is also used to, in performing the specialized simulated sales training task, first acquire a preset gold-medal sales interaction record, then construct a simulated gold-medal sales role based on the gold-medal sales interaction record, and start the simulated gold-medal sales role to conduct simulated sales training with the salesperson playing the role of the customer, so as to demonstrate to the salesperson the standard coping strategies for the skill gap item.

[0017] In one possible design, the second AI tool module is also used to dynamically switch between the simulated customer role and the simulated top sales role in response to the salesperson's mode switching instruction or based on a preset switching strategy during the execution of the specialized simulated sales training task, so that the salesperson can alternate between practical exercises and observation and learning.

[0018] In one possible design, the second AI tool module is further configured to extract personal characteristic information of the sales personnel in training and construct a personalized memory model of the sales personnel in training based on historical sales and service interaction data collected by the first AI tool module and / or historical training interaction data generated by the second AI tool module, and to call the personalized memory model when generating response content simulating a top salesperson role, so that the generated response content matches the personal style of the sales personnel in training.

[0019] In one possible design, during the execution of the specialized simulated sales training task, the second AI tool module uses enhanced generation technology to call a preset enterprise private knowledge base to generate the response content of the simulated sales role.

[0020] Secondly, the present invention provides a sales training empowerment and customer management method that integrates multiple AI tools, applied to the sales training empowerment and customer management system as described in the first aspect or any possible design within the first aspect, and includes the following steps: The first AI tool module first collects sales and service interaction data generated by the communication between sales personnel and target customers. Then, it analyzes the sales and service interaction data and generates first output data and second output data based on the analysis results. The first output data is used to characterize the skill gaps of the sales personnel, and the second output data is used to characterize the first demand tag and / or first profile tag of the target customer. The second AI tool module receives the first output data and, in response to the first output data, automatically generates a specific simulated sales training task targeting the skill gap item. The customer data module first receives the second output data and, in response to the second output data, updates the customer profile of the target customer. Then, based on the updated customer profile, it automatically triggers marketing actions targeting the target customer.

[0021] In one possible design, based on the updated customer profile, marketing actions targeting the target customer are automatically triggered, including: Based on the updated customer profile, a preset enterprise product inventory and marketing policy knowledge base is invoked. A personalized marketing plan suitable for the target customer is generated by using retrieval-enhanced generation technology to constrain the large language model. The third AI tool module, which is used to execute the marketing action, is then triggered to start the first AI robot to call the target customer in accordance with the personalized marketing plan for automated marketing.

[0022] In one possible design, the excellent case screening module responds to the signal that the customer status of the target customer in the customer data module has changed to a completed transaction. It automatically retrieves the historical sales and service interaction data corresponding to the target customer, calls a preset scoring model to score the historical sales and service interaction data, and then marks the historical sales and service interaction data with scores higher than a preset threshold as excellent interaction records and stores them in the experience library of the second AI tool module to optimize the simulated sales training task. The historical sales and service interaction data includes at least the audio recording data collected by the first AI tool module.

[0023] In one possible design, the second AI tool module responds to the update signal of the experience base, acquires the excellent interaction records, and generates fine-tuning samples based on the excellent interaction records to supervise and fine-tune the preset simulated sales role model and / or simulated customer role model in the second AI tool module.

[0024] In one possible design, the sales and service interaction data is analyzed and first output data is generated based on the analysis results, including: By using a unified language model and combining it with multiple preset detection dimension prompts, semantic analysis is performed on the sales and service interaction data to identify sales process nodes that the sales personnel did not execute or product selling points that were not mentioned, which are then used as the skill deficiency items in the first output data.

[0025] In one possible design, the method further includes: The third AI tool module first collects marketing outbound call interaction data generated by the first AI robot / first natural person communicating with the target customer, then analyzes the marketing outbound call interaction data and generates third output data based on the analysis results, wherein the third output data is used to characterize the second demand tag and / or second profile tag of the target customer; The fourth AI tool module first collects after-sales customer service interaction data generated by the second AI robot / second natural person communicating with the target customer, then analyzes the after-sales customer service interaction data and generates fourth output data based on the analysis results, wherein the fourth output data is used to characterize the third demand tag and / or third profile tag of the target customer; Updating the customer profile of the target customer includes: based on a unified data standard, integrating the demand tags and / or profile tags received from the first AI tool module, the third AI tool module, and the fourth AI tool module with the basic customer information of the target customer pre-stored in the customer data module to generate a customer profile containing multi-dimensional tags.

[0026] In one possible design, based on the updated customer profile, marketing actions targeting the target customer are automatically triggered, including: Determine whether the confidence level of the updated demand tag in the customer profile exceeds a preset confidence threshold. If so, automatically trigger a marketing action for the target customer; otherwise, stop triggering a marketing action for the target customer. And / or, identify the urgency of the updated demand tags in the customer profile and determine whether the urgency exceeds a preset urgency threshold. If so, automatically trigger marketing actions for the target customer in real time; otherwise, automatically trigger marketing actions for the target customer at regular intervals or in sequence according to the queuing status of marketing tasks. And / or, verify whether the updated customer profile contains the preset minimum necessary fields. If so, automatically trigger marketing actions for the target customer; otherwise, stop triggering marketing actions for the target customer and generate an information completion task for the sales personnel.

[0027] In one possible design, marketing actions targeting the stated customer are automatically triggered, including: Determine whether the time from the most recent marketing timestamp to the current timestamp exceeds a preset time threshold. If so, automatically trigger a marketing action for the target customer. Otherwise, stop triggering the marketing action for the target customer until the time exceeds the preset time threshold. The most recent marketing timestamp refers to the execution timestamp of the most recent historical marketing action for the target customer. And / or, determine whether there is a marketing circuit breaker tag for the target customer in the customer data module. If so, stop triggering marketing actions for the target customer until the tag is canceled. Otherwise, automatically trigger marketing actions for the target customer. The marketing circuit breaker tag is marked when the target customer's marketing feedback intent is a clear rejection and is canceled when the target customer actively initiates a service request. The marketing feedback intent is extracted based on the results of historical marketing actions corresponding to the target customer.

[0028] In one possible design, when the specialized simulated sales training task is generated, the second AI tool module obtains the customer profile of the target customer associated with the skill gap item from the customer data module, and converts the customer profile into role-building prompts to construct a simulated customer role with the customer characteristics of the target customer in the specialized simulated sales training task.

[0029] In one possible design, the second AI tool module, while performing the specialized simulated sales training task, first acquires preset top sales interaction records, then constructs a simulated top sales role based on the top sales interaction records, and initiates simulated sales training between the simulated top sales role and the salesperson playing the role of a customer, in order to demonstrate to the salesperson standard coping strategies for the skill gaps.

[0030] In one possible design, during the execution of the specialized simulated sales training task, the second AI tool module responds to the salesperson's mode switching command or, based on a preset switching strategy, dynamically switches between the simulated customer role and the simulated top salesperson role, so that the salesperson can alternate between practical exercises and observational learning.

[0031] In one possible design, the second AI tool module extracts the personal characteristic information of the sales personnel in training and constructs a personalized memory model of the sales personnel in training based on the historical sales and service interaction data collected by the first AI tool module and / or the historical training interaction data generated by the second AI tool module. The personalized memory model is then invoked when generating the response content of a simulated top sales role so that the generated response content matches the personal style of the sales personnel in training.

[0032] In one possible design, during the execution of the specialized simulated sales training task, the second AI tool module uses a retrieval enhancement generation technology to call a preset enterprise private knowledge base to generate the simulated sales role's response content.

[0033] The beneficial effects of the above scheme are: (1) This invention provides a new solution that can effectively link multiple AI tools to achieve data interoperability and business collaboration. It includes a first AI tool module, a second AI tool module and a customer data module. The first AI tool module collects the interaction data between sales personnel and target customers, analyzes it and generates first output data representing the sales personnel's skill shortcomings and second output data representing the target customer's demand tags / profile tags. The second AI tool module receives the first output data and automatically generates a special simulated sales training task for the skill shortcomings. The customer data module receives the second output data and updates the customer profile of the target customer. At the same time, it automatically triggers marketing actions for the target customer based on the updated customer profile. Thus, by linking multiple AI tools, the data barrier between sales training and customer management is broken down, and a full-link closed loop of "real-world data collection → skill shortcomings identification → special training generation → customer demand extraction → profile update → precise marketing trigger" is realized, which significantly improves the pertinence of employee training and the efficiency of customer operations. (2) In terms of sales training empowerment, this solution realizes the automated extraction and continuous feedback of top sales experience. That is, the first AI tool module collects sales practice interaction data and identifies the skill gaps of sales personnel, and then the second AI tool module generates special simulated sales training tasks in a targeted manner, so that the training content is transformed from "general standardized courses" to "personalized training based on practical gaps". In particular, through the excellent case screening module, the system can respond to the signal that the customer status changes to a transaction, automatically retrieve the corresponding historical interaction data and call the preset scoring model for intelligent evaluation, mark the high-quality interaction records with scores higher than the threshold as excellent interaction records and store them in the experience library, and then continuously optimize the simulated sales role model and / or simulated customer role model in the second AI tool module through supervised fine-tuning technology. This mechanism forms a spiral upward closed loop of "experience extraction - model fine-tuning - special training - practical application - re-extraction", which enables the practical experience of top sales to be replicated and inherited on a large scale, significantly improving the pertinence and effectiveness of sales training. (3) In terms of in-depth mining of customer value, this solution breaks down the data barriers of multiple customer touchpoints and realizes the accurate construction and dynamic updating of customer profiles. Specifically, the first AI tool module extracts the first demand tag and / or first profile tag of the target customer from sales practice, the third AI tool module extracts the second demand tag and / or second profile tag from marketing outbound call interaction, and the fourth AI tool module extracts the third demand tag and / or third profile tag from after-sales customer service interaction. The customer data module integrates the multi-source tags with the pre-stored basic customer information based on a unified data standard to generate a complete customer profile containing multi-dimensional tags. This mechanism greatly improves the completeness of customer profiles. Experimental data shows that the completeness of customer profiles can be increased from 30% in the traditional CRM system to 110% (including newly added industry-specific tags), realizing the holographic integration of customer information and completely avoiding the problem of data fragmentation. At the same time, the system can capture the potential needs of customers in real time, such as the replacement of old stoves and the upgrading of heating systems, so that the efficiency of customer data (i.e. the proportion of customer data containing clear needs) increases from 40% to 100%, effectively reducing the loss of high-value customer data. (4) Regarding the two-way collaboration between training and customer data, this solution constructs a full-link collaborative mechanism of "training to improve capabilities - capabilities to optimize services - services to accumulate customer data - customer data to drive marketing". That is, the first AI tool module generates first output data pointing to the sales staff's skill shortcomings and second output data pointing to the needs of target customers. The former drives the second AI tool module to generate targeted training tasks, and the latter drives the customer data module to update customer profiles. At the same time, when the second AI tool module generates special simulated sales training tasks, it can also obtain the target customer profiles related to the skill shortcomings from the customer data module and convert them into role-building prompts. In the simulated training, AI roles with real customer characteristics are built, so that sales staff can rehearse for real customers they are about to face in a safe environment. This two-way linkage realizes the mutual empowerment of training and customer data, avoiding the drawbacks of the separation between the two in the traditional system. (5) In terms of precision marketing and operational efficiency, this solution realizes intelligent marketing triggering based on real-time updated customer profiles. That is, the customer data module generates personalized marketing plans suitable for target customers based on the updated customer profiles through a large language model, and triggers the third AI tool module to start the first AI robot to conduct automated outbound marketing according to the plan. During the triggering process, the system intelligently integrates multiple control mechanisms: ensuring that only high-credibility demand tags can trigger marketing by judging the confidence threshold; realizing real-time reach of high-priority demands and timed batch processing of low-priority demands by recognizing the urgency level; ensuring that the minimum necessary fields required for the generation of marketing plans are complete by verifying the profile completeness; and avoiding excessive harassment of the same customer by judging the marketing duration threshold. Thanks to these mechanisms, the AI ​​tool and the customer data module achieve full-link data interconnection, the employee ID recording feeds back to training optimization, and the customer data supports marketing decisions, reducing the number of operational process links by 40% and improving overall efficiency by 50%. More importantly, the customer data module automatically generates customized plans based on complete profiles, and combined with AI outbound calls to reach customers, the gas product marketing conversion rate has increased significantly from 8% to 38% (an increase of 30 percentage points), significantly reducing the cost of ineffective marketing. (6) This solution effectively solves the problems of data fragmentation, weak training relevance and low customer resource utilization in the existing technology through the collaborative linkage of multiple AI tool modules. It achieves deep integration and mutual empowerment of sales training and customer resource management, generating significant technical effects and commercial value, and is easy to apply and promote in practice. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a schematic diagram of the sales training empowerment and customer management system with multiple AI tools linked together, as provided in this embodiment of the invention.

[0036] Figure 2 This is a flowchart illustrating the sales training empowerment and customer management method that integrates multiple AI tools, as provided in this embodiment of the invention. Detailed Implementation

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying drawings is only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these embodiments without creative effort. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0038] It should be understood that although the terms "first" and "second", etc., may be used herein to describe various objects, these objects should not be limited by these terms. These terms are only used to distinguish one object from another. For example, the first object may be referred to as the second object, and similarly, the second object may be referred to as the first object, without departing from the scope of the exemplary embodiments of the invention.

[0039] It should be understood that the term "and / or" that may appear in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, or A and B exist simultaneously. Another example is A, B and / or C, which can mean that any one of A, B, and C or any combination thereof exists. The term " / and" that may appear in this document describes another relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone or A and B exist simultaneously. In addition, the character " / " that may appear in this document generally indicates that the related objects before and after it are in an "or" relationship.

[0040] Example 1 like Figure 1As shown, the sales training empowerment and customer data management system with multiple AI tools linked together provided in this embodiment includes, but is not limited to, a first AI tool module, a second AI tool module, and a customer data module.

[0041] The first AI tool module is used to first collect sales and service interaction data generated by the communication between sales personnel and target customers, then analyze the sales and service interaction data and generate first output data and second output data based on the analysis results. The first output data is used to characterize the skill gaps of the sales personnel, and the second output data is used to characterize the first demand tag and / or first profile tag of the target customer.

[0042] Specifically, the first AI tool module can be manifested as a smart badge or a recording application integrated into a mobile terminal, used to collect voice data in real time between sales personnel and target customers in scenarios such as face-to-face communication, telephone communication, or online meetings (legally collected with the knowledge and consent of both the sales personnel and the target customer). The aforementioned collected voice data can be used as the sales and service interaction data, and converted into text data through ASR technology to form analyzable sales and service interaction text; subsequently, NLP natural language processing technology and large language models can be used to perform deep semantic analysis on the text (before performing semantic analysis, it is also necessary to perform privacy data desensitization processing on the text to be analyzed through a preset named entity recognition algorithm, so as to automatically block or replace sensitive personal information in the speech-to-text, such as real name, ID number and / or detailed house number, etc., and then extract demand tags while ensuring customer privacy compliance; the aforementioned privacy data desensitization processing must be performed before subsequent semantic analysis, which will not be elaborated on then). During semantic analysis, the performance of sales personnel can be evaluated based on multiple preset detection dimensions (such as product highlight explanation, demand mining, objection handling, and standard sales process execution). This involves analyzing the sales and service interaction data and generating first output data based on the analysis results. This includes, but is not limited to, using a unified language model combined with preset detection dimension prompts to perform semantic analysis on the sales and service interaction data, identifying sales process nodes not executed or product selling points not mentioned by the sales personnel as skill deficiencies in the first output data. For example, in the gas stove sales scenario, using a unified language model combined with preset detection dimension prompts, it can detect whether the sales personnel mention product selling points such as "Level 1 energy efficiency" and "automatic flameout protection," whether they proactively inquire about customer information such as the gas usage period and family size, and whether they provide reasonable explanations for customers' "price concerns." If it is detected that the sales personnel have not executed a node in the standard sales process or have not mentioned a key product selling point, this missing item can be identified as a skill deficiency and transmitted as the first output data to the second AI tool module. Additionally, a unified language model, combined with pre-defined prompts across multiple detection dimensions, can be used to extract the potential needs and attribute characteristics of the target customer from the sales and service interaction text, generating the first need tag and / or the first profile tag. For example, when a customer mentions, "My stove is over ten years old, and recently the flame feels unstable," "Need to replace an old stove" can be extracted as the first need tag; when a customer states, "I cook a lot and value gas efficiency the most," "Energy saving preference" can be extracted as the first profile tag. These tags will be transmitted as the second output data to the customer data module for subsequent customer profile updates.

[0043] The second AI tool module is communicatively connected to the first AI tool module, and is used to receive the first output data and, in response to the first output data, automatically generate a special simulated sales training task targeting the skill gap item.

[0044] Specifically, the second AI tool module can be embodied as an AI training system, whose core function is to provide sales personnel with highly realistic simulated sales scenarios for sales training. After receiving the first output data (i.e., skill gap items) from the first AI tool module, the second AI tool module does not simply push general training courses, but dynamically generates a specific simulated sales training task targeting that skill gap item. Specifically, the specific simulated sales training task can include the following two complementary simulation modes (A) and (B).

[0045] (A) AI Role-Playing as a Customer – Targeted Practical Exercises: Taking the weakness of "not clearly explaining the product's energy-saving highlights" as an example, the second AI tool module automatically constructs a sales scenario where the AI ​​plays the role of an elderly customer who is "very sensitive to gas costs and repeatedly asks how to save gas." Salespeople are required to clearly explain the product's energy-saving technology, first-level energy efficiency standard, and long-term energy-saving effects to the customer in this scenario. During the simulation training, the AI ​​will engage in multiple rounds of dialogue with the salesperson based on preset role characteristics (such as "repeating when not hearing clearly" and "repeatedly questioning due to price sensitivity"), forcing the salesperson to repeatedly practice the weak points in the practical exercises until they reach the preset qualification standard. The difficulty and complexity of the specialized simulated sales training tasks can be dynamically adjusted based on the salesperson's historical performance. For example, for salespeople who repeatedly exhibit the same weakness, the second AI tool module can generate a more challenging customer role (such as a picky customer who "questions both price and safety") to enhance the training effect. Furthermore, the interaction data generated during training can be recorded for subsequent training effect evaluation and model optimization.

[0046] (B) AI plays the role of a top salesperson - demonstrative observation and learning: In addition to the practical exercise mode of AI playing the role of a customer, the second AI tool module can also automatically construct another sales scenario: set the AI ​​to play the role of a top salesperson, and require the salesperson to play the role of a customer in this scenario. By observing the top salesperson's demonstration of the salesperson's speech and response strategies, the salesperson can perceive and learn excellent sales skills. Taking "explaining product energy-saving highlights" as an example, the following scenario can be constructed: AI plays the role of an experienced top salesperson, while the salesperson plays the role of a customer who "needs energy saving but is price-sensitive." In the simulated dialogue, the top salesperson demonstrates how to naturally integrate energy-saving highlights into the conversation, for example: "You mentioned that you cook a lot, so this energy-efficient stove is especially suitable for you. Although the price is slightly higher than the ordinary model, based on your family's gas consumption, the gas bill savings in a year will almost make up for the price difference, making it more cost-effective in the long run. Moreover, it also has automatic flameout protection, which is more reassuring for families with elderly people and children." Through this "role-playing" observation, the salesperson can intuitively perceive the excellent salesperson's expression logic, sales techniques, and customer psychology understanding skills, thereby internalizing them into their own abilities.

[0047] The two modes described above can be used in combination: sales personnel can first observe and learn through mode (B), and then practice through mode (A); alternatively, if they encounter difficulties in mode (A), they can temporarily switch to mode (B) to view a demonstration, and then return to continue practicing. The second AI tool module can also intelligently recommend suitable high-performing sales cases (i.e., excellent interaction records selected from real transaction recordings) based on the sales personnel's weaknesses. Thus, through this closed-loop mechanism of "learning-practice-feedback-relearning," the sales personnel's capabilities can be continuously improved.

[0048] The customer data module is communicatively connected to the first AI tool module, and is used to receive the second output data and update the customer profile of the target customer in response to the second output data.

[0049] Specifically, the customer data module can be embodied in a CRM system or a customer data platform, used for unified storage and management of customer information. Upon receiving the second output data (i.e., the first demand tag and / or the first profile tag) from the first AI tool module, the customer data module does not simply append records. Instead, it integrates the tags with the customer's historical data based on unified data standards (such as customer ID and tag coding specifications) to generate a more complete customer profile. For example, assuming the CRM system already stores the target customer's basic information: name, contact information, community name (older community), and gas meter type, etc.; when the first AI tool module extracts two new tags, "old stove replacement demand" and "energy-saving preference," from the sales interaction, the customer data module will integrate these new tags with the basic information to form an updated customer profile: Customer Profile = Basic Information (Older Community Resident) + Dynamic Demand (Stove Replacement) + Attribute Preference (Energy-Saving Preference).

[0050] Preferably, the customer data module can also receive tag data from other AI tool modules, that is, the sales training empowerment and customer data management system may include, but is not limited to, a third AI tool module and a fourth AI tool module that are respectively connected to the customer data module; the third AI tool module is used to first collect marketing outbound call interaction data generated by the first AI robot / first natural person communicating with the target customer, then analyze the marketing outbound call interaction data and generate third output data based on the analysis results, wherein the third output data is used to characterize the second demand tag and / or second profile tag of the target customer; the fourth AI tool module is used to first collect the marketing outbound call interaction data generated by the first AI robot / first natural person communicating with the target customer, then analyze the marketing outbound call interaction data and generate third output data based on the analysis results, wherein the third output data is used to characterize the second demand tag and / or second profile tag of the target customer; The system generates after-sales customer service interaction data through communication between two AI robots / second natural persons and the target customer. This data is then analyzed, and a fourth output data is generated based on the analysis results. This fourth output data is used to characterize the target customer's third need tag and / or third profile tag. The system updates the target customer's customer profile, including but not limited to: based on a unified data standard, fusing the need tags and / or profile tags received from the first AI tool module, the third AI tool module, and the fourth AI tool module with the target customer's pre-stored basic customer information in the customer data module to generate a customer profile containing multi-dimensional tags.

[0051] Specifically, the third AI tool module can be embodied as an AI outbound calling system, the core function of which is to automatically reach out to the target customers. During the outbound marketing call process, the third AI tool can use ASR technology to transcribe the dialogue between the target customer and the first AI robot (which is legally collected with the knowledge and consent of the target customer) into text in real time, and then call NLP technology and a large language model to perform semantic analysis on the text to extract new needs or attributes shown by the customer during the marketing outreach process (the specific technical principles of semantic analysis and extraction can be found in the aforementioned first need tag and / or first profile tag). For example, in a marketing call targeting "energy-saving stoves," a customer might mention "My child is young, so safety is my top priority" or "My kitchen is small, so I want a built-in stove." The third AI tool module can extract tags such as "safety preference" and "built-in stove requirement" from these conversations as the second demand tag. The third AI tool module can also identify characteristics such as the customer's tone and attitude, such as "price sensitive" or "high brand recognition," as the second profile tag. These tags complement the first batch of tags extracted from sales practice by the first AI tool module, making the customer profile more comprehensive. Furthermore, the third AI tool module also supports human outbound calling scenarios: when a human customer service representative (i.e., the first natural person) makes outbound marketing calls, the conversation recordings can also be collected (legally collected with the knowledge and consent of both the first natural person and the target customer) and analyzed. After extracting tags, the data is synchronized to the customer data module (regardless of whether the outbound caller is an AI robot or a natural person, the logic and standards for tag extraction remain consistent).

[0052] Specifically, the fourth AI tool module can be embodied as an AI customer service system, used to collect interaction data of the target customer in after-sales service scenarios. After-sales scenarios are a crucial window into customer needs: customers often reveal a wealth of valuable information during repair requests, inquiries, and complaints. For example, if a customer calls customer service to report that their gas stove won't ignite, the fourth AI tool module can extract tags such as "equipment malfunction" and "urgent repair need" from the recorded conversation (legally collected with the target customer's knowledge and consent) using ASR technology, NLP technology, and large language models while processing the repair request. If the customer mentions in the conversation, "This stove is five years old, shouldn't it be replaced?", the fourth AI tool module can further extract the "replacement need" tag. If the customer expresses satisfaction with the repair service, the fourth AI tool module can also extract profile tags such as "high service satisfaction." Furthermore, the fourth AI tool module also supports both AI robot customer service and human customer service (i.e., through the second natural person), ensuring that customer data generated at all after-sales service touchpoints can be effectively collected and structured, preventing the loss of customer information during the after-sales process.

[0053] Specifically, the customer data module maintains a unified data standard, including customer ID (which serves as a unique identifier), predefined tag coding specifications, and tag confidence scoring mechanisms. When tag data from different AI tool modules converges into the customer data module, the module first associates new tags with historical data based on the customer ID, and then integrates them according to preset fusion rules. The specific process of the fusion includes, but is not limited to, the following methods: (1) Deduplication and merging, that is, for the same or similar tags (such as “energy saving preference” from work badges and “energy efficiency concern” from outbound calls), they can be merged according to the confidence level or time sequence; (2) Tag classification and structuring, that is, the tags are divided into basic information categories (such as community type or family members), demand categories (such as replacement demand or maintenance demand), preference categories (such as brand preference or function preference) and behavior categories (such as interaction frequency or response willingness); (3) Time decay processing, that is, for time-sensitive tags (such as “recent purchase intention”), a time decay factor is introduced to ensure that the profile reflects the latest status of the customer; (4) Confidence weighting, that is, for the case where the same tag has multiple sources, it is weighted according to confidence level to generate a more accurate final tag. Through the aforementioned integration mechanism, the completeness of customer profiles can be increased from 30% in traditional CRM systems to 110% (including industry-specific tags), laying a data foundation for subsequent precision marketing. Taking the gas industry as an example, a typical complete customer profile can include: basic information (such as old residential areas, number of family members, and gas meter type), demand tags (such as stove replacement, heating upgrades, and safety inspections), preference tags (such as energy-saving preferences, brand preferences, and price sensitivity), and behavioral tags (such as interaction frequency, complaint records, and satisfaction ratings), providing a solid data foundation for subsequent precision marketing and service optimization.

[0054] The customer data module is also used to automatically trigger marketing actions for the target customer based on the updated customer profile.

[0055] Specifically, after the customer data module completes the update of the customer profile, it can also automatically determine whether to start a marketing action according to the preset triggering method. The aforementioned triggering method may include, but is not limited to, the following dimensions (1) to (3).

[0056] (1) Real-time triggering: For high-intent tags (such as "explicitly inquire about price" or "indicates a recent purchase plan"), marketing actions are triggered in real time. Based on the updated customer profile, the preset enterprise product inventory and marketing policy knowledge base is invoked, and the large language model is constrained by retrieval-Augmented Generation (RAG) technology to generate personalized marketing plans suitable for the target customers. For example, a plan of "Level 1 energy efficiency stove package + energy-saving subsidy explanation" is generated for customers with "energy-saving preference", and a plan of "trade-in special activity" is generated for "residents of old communities", etc. Considering that when generating the personalized marketing plan, it is necessary to avoid the large language model from generating "illusions" (i.e., fabricating marketing promises that do not exist or do not conform to the current policy), the retrieval-Augmented Generation (RAG) technology is introduced to constrain it so as to combine the generation capability of the large language model with the accurate retrieval capability of the enterprise product inventory and marketing policy knowledge base. Specifically, a knowledge base for the enterprise's product inventory and marketing policies needs to be pre-built. This knowledge base contains verifiable real-time information such as the actual inventory quantity of each product model, currently effective promotional activities (e.g., discounts, promotional offers, or free gifts), installation service policies in various regions, product delivery time limits, and pricing systems. When a marketing plan needs to be generated for the target customer, the system first retrieves matching product models (e.g., energy-saving preferences + residents of older communities + previous inquiries about stoves) and applicable policies (e.g., "trade-in subsidy of 200 yuan") from the knowledge base based on the updated customer profile (e.g., "energy-saving preference + residents of older communities + previous inquiries about stoves"). Then, the retrieved real information fragments are used as context input into the large language model, which is constrained to only organize the language and generate the plan within the scope of the retrieved information. The final personalized marketing plan mentions products, prices, and preferential promises that are real and executable, fundamentally avoiding problems such as false promises and exaggerated advertising, and ensuring the credibility and fulfillment of the marketing content.

[0057] (2) Triggering other AI tool modules to execute outbound calls: When the sales training empowerment and customer data management system also includes a third AI tool module (i.e., an AI outbound call system) that is connected to the customer data module and used to execute the marketing action, based on the updated customer profile, it calls the preset enterprise product inventory and marketing policy knowledge base, generates a personalized marketing plan suitable for the target customer through retrieval enhancement generation technology constraining the large language model, and triggers the third AI tool module to start the first AI robot to call the target customer for automated marketing according to the personalized marketing plan. In addition, during the outbound call process, the first AI robot can also dynamically adjust the script according to the customer's real-time feedback, such as further explaining the installment payment policy for the customer's "price concerns" and explaining the free on-site installation service in detail for the customer's "installation concerns".

[0058] (3) Intelligent anti-harassment mechanism, which can also be built with the following intelligent trigger control logic: (3.1) Determine whether the confidence level of the updated demand tag in the customer profile (which is bound to the output of the analyzed tag) exceeds the preset confidence threshold. If so, automatically trigger the marketing action for the target customer. Otherwise, stop triggering the marketing action for the target customer. (3.2) Identify the urgency level of the updated demand tag in the customer profile and determine whether the urgency level exceeds the preset urgency level threshold. If so, automatically trigger the marketing action for the target customer in real time. Otherwise, automatically trigger the marketing action for the target customer according to the queuing status of the marketing task (which is applicable to the case of batch triggering multiple marketing actions) or in sequence. (3.3) Verify whether the updated customer profile contains the preset minimum necessary fields (such as contact information field and intended product type field). If so, automatically trigger the marketing action for the target customer. Otherwise, stop triggering the marketing action for the target customer and target the target customer. The sales personnel generate information completion tasks; (3.4) determine whether the time from the most recent marketing timestamp to the current timestamp exceeds the preset time threshold (e.g., 7 days, to avoid excessive harassment of the same customer). If so, automatically trigger the marketing action for the target customer; otherwise, stop triggering the marketing action for the target customer until the time exceeds the preset time threshold. The most recent marketing timestamp refers to the execution timestamp of the most recent historical marketing action for the target customer; (3.5) determine whether there is a marketing circuit breaker tag for the target customer in the customer data module. If so, stop triggering the marketing action for the target customer until the tag is canceled; otherwise, automatically trigger the marketing action for the target customer. The marketing circuit breaker tag is marked when the target customer's marketing feedback intention is a clear rejection and is canceled when the target customer actively initiates a service request. The marketing feedback intention is extracted based on the results of historical marketing actions corresponding to the target customer.

[0059] Once the third AI tool module (i.e., the AI ​​outbound call module) has completed the marketing outbound call to the target customer, it can automatically perform in-depth analysis on the result of the marketing interaction (i.e., as the result of a historical marketing action) to extract the customer's marketing feedback intent. The specific extraction process is as follows: First, the conversation between the customer and the AI ​​robot during the outbound call is transcribed into text in real time using ASR technology to form a complete interaction record; then, NLP technology and a large language model are called to perform semantic analysis and intent recognition on the text (the model used for intent recognition is pre-trained and can accurately identify the various feedback tendencies expressed by the customer in the marketing dialogue). For example, at the end of an outbound call, special attention can be paid to the customer's final response to the marketing invitation: when the customer explicitly states "I don't need it," "Don't call again," or "I'm busy, don't bother me," the marketing feedback intent is identified as a clear rejection; when the customer says "I'll think about it" or "I'm busy now, let's talk later," the marketing feedback intent is identified as no immediate need or a postponement of the decision; when the customer proactively asks "How much is this?" or "When can you come to install it?", the marketing feedback intent is identified as interest; when the customer says "Call me back in a couple of days" or "I'll discuss it with my family tonight," the marketing feedback intent is identified as a request for a return call; and so on. When the marketing feedback intent of the target customer is identified as "clear rejection," a marketing circuit breaker tag is marked for the target customer in the customer data module. This tag is a mandatory marketing interception identifier, which includes, but is not limited to, the following metadata: Circuit breaker type—clear rejection; Circuit breaker time—time stamp of the tag generation; Circuit breaker source—task ID of this outbound call; Circuit breaker product line—product type involved in this marketing (such as stoves or heating equipment, etc.). After the aforementioned circuit breaker label is set, the existence of the label will be checked first before any subsequent marketing action is triggered. If it exists, the marketing action will be unconditionally suspended regardless of whether the freeze period (i.e., the duration from the most recent marketing timestamp to the current timestamp) has expired, until the label is canceled.

[0060] The mechanism for canceling the marketing circuit breaker label is also based on a change in customer willingness: when the target customer proactively initiates a service request (e.g., calling the customer service hotline, initiating an online inquiry, visiting a store, or contacting the company through other channels), the system identifies this proactive contact behavior and determines that the customer's attitude towards the company has changed from "rejection" to "communication." At this point, customer service personnel can remove the customer's circuit breaker label from the system, or the system can automatically remove the label based on the customer's proactive contact behavior. After the label is removed, the customer will be reintegrated into the normal marketing trigger process.

[0061] Through the aforementioned mechanism, the customer data module can transform from "static recording" to "dynamic driving," converting real-time updated customer profiles into precise marketing actions, significantly improving marketing conversion rates and customer data utilization. Furthermore, the marketing circuit breaker mechanism ensures deep respect for customer intentions and precise responses: it avoids secondary harassment of customers who have clearly refused due to a simple "cooling-off period," while preserving the possibility of re-establishing communication after a change in customer attitude, demonstrating the intelligence and humanization of the marketing system.

[0062] Based on the detailed description of the sales training empowerment and customer data management system described above, a new solution is provided that can effectively link multiple AI tools to achieve data interoperability and business collaboration. This solution includes a first AI tool module, a second AI tool module, and a customer data module. The first AI tool module collects interaction data between sales personnel and target customers, analyzes it, and generates first output data representing the sales personnel's skill gaps and second output data representing the target customer's need tags / profile tags. The second AI tool module receives the first output data and automatically generates specific simulated sales training tasks targeting the skill gaps. The customer data module receives the second output data and updates the target customer's profile, while automatically triggering marketing actions for that target customer based on the updated profile. Thus, by linking multiple AI tools, the data barriers between sales training and customer data management are broken down, achieving a closed-loop process of "real-world data collection → skill gap identification → specific training generation → customer need extraction → profile update → precise marketing triggering." This significantly improves the relevance of employee training and the efficiency of customer data operations, facilitating practical application and promotion.

[0063] Preferably, the sales training empowerment and customer management system further includes, but is not limited to, an excellent case selection module that is communicatively connected to the customer data module, the first AI tool module, and the second AI tool module. The excellent case selection module is used to respond to the signal in the customer data module that the customer status of the target customer has changed to a transaction, automatically retrieve the historical sales and service interaction data corresponding to the target customer, and call a preset scoring model to score the historical sales and service interaction data. Then, the historical sales and service interaction data with scores higher than a preset threshold are marked as excellent interaction records and stored in the experience library in the second AI tool module to optimize the simulated sales training task. The historical sales and service interaction data includes at least the audio recording data collected by the first AI tool module.

[0064] The excellent case screening module is used to automatically filter high-quality sales interaction records from real transaction cases and transform them into reusable training resources, forming a closed loop of "practical experience → training resources". It can be built into the first AI tool module as a sub-module or as an independent AI tool module (i.e., the fifth AI tool module). Specifically, the excellent case screening module communicates with the customer data module in real time to monitor changes in customer status. When the customer status of the target customer in the customer data module changes to "transaction" (such as successful signing or order completion), the following screening process is automatically triggered: The first step is data retrieval. The excellent case screening module automatically retrieves all historical sales and service interaction data related to the target customer based on the unique identifier of the target customer (such as customer ID). This data includes at least the sales practice recording data collected by the first AI tool module (such as smart badge), and may also include AI outbound call recordings and customer service dialogue records related to the customer. Then, this recording data is transcribed into text using ASR technology to form a complete interactive text record. The second step is intelligent scoring, which involves calling a preset scoring model to score the retrieved interaction data in multiple dimensions. The scoring model can be built based on a large language model and combined with expert-level evaluation dimensions for scoring. The evaluation dimensions include, but are not limited to: (1) Depth of demand mining, i.e., whether the sales personnel have deeply mined the explicit and implicit needs of the customers; (2) Accuracy of pain point response, i.e. whether the sales personnel have accurately grasped and responded to the core pain points of the customers; (3) Standard sales process execution rate, i.e. whether the sales personnel have executed the sales process stipulated by the company, such as product introduction, objection handling and facilitation attempts; (4) Communication skills, i.e. the sales personnel's expression logic, affinity and adaptability; Each dimension can be set with an independent scoring weight, and a comprehensive score is generated by weighted summation; for example, the full score of the comprehensive score can be set to 100 points, and only interaction records with a score higher than 80 points will be considered as excellent. The third step is automatic tagging and storage. Only interaction records that simultaneously meet the two conditions of "transaction completed" and "intelligent score higher than the preset threshold" will be automatically tagged as excellent interaction records by the excellent case screening module.

[0065] This dual screening mechanism of "business results-driven + intelligent quality assessment" effectively filters out "lucky sales" (i.e., salespeople with mediocre skills but customers automatically close the deal), ensuring that only high-quality sales experience is retained. Thus, the excellent case selection module automates the transformation from "sales results" to "reusable training resources," enabling the continuous extraction, accumulation, and inheritance of a company's top sales experience. This forms a spiral-like upward closed loop of "practice → screening → storage → fine-tuning → training → re-practice," continuously improving the overall capabilities of the sales team. Furthermore, considering that the third AI tool may also generate sales cases during outbound calls, and the fourth AI tool may also generate new sales cases during after-sales customer service, the collected outbound call interaction data and customer service interaction data may also serve as excellent case sources for the second AI tool. Specifically, the historical sales and service interaction data further includes, but is limited to, recorded data collected by the third AI tool module and / or recorded data collected by the fourth AI tool module.

[0066] In detail, the excellent interaction records will then be stored in the experience base of the second AI tool module. This experience base is a structured case resource pool, and each excellent interaction record contains information such as the original recording, transcribed text, multi-dimensional scoring, and key speech fragments. These excellent interaction records can be used in several ways: (1) as fine-tuning samples, that is, the second AI tool module is also used to respond to the update signal of the experience base, obtain the excellent interaction records, and generate fine-tuning samples based on the excellent interaction records to perform supervised fine-tuning on the preset simulated sales role model (which corresponds to the scenario of "AI playing the role of a top salesperson" and / or simulated customer role model (which corresponds to the scenario of "AI playing the role of a customer") and / or simulated customer role model (which corresponds to the scenario of "AI playing the role of a customer") in the second AI tool module. SFT is a machine learning model optimization technique. Its basic process is: on the basis of an existing pre-trained model, high-quality labeled data—that is, "fine-tuning samples"—is used to train the model in a targeted manner, so that the model's performance in a specific task or specific field is improved; compared with training the model from scratch, supervised fine-tuning requires less data, is faster, and has more stable results); (2) as observation and learning material, that is, sales personnel can directly view the recordings and texts of excellent cases through the system to learn the practical sales techniques of top salespersons; (3) used for training task generation, that is, based on the key sales techniques and coping strategies in excellent cases, the difficulty and content of the special simulated sales training tasks can be optimized. Through the aforementioned mechanism, the second AI tool module is no longer a static "sales technique player", but an intelligent training system with self-evolution capabilities, which can continuously absorb the company's top sales experience and transform it into scalable training resources to further improve the overall capabilities of the sales team.

[0067] Preferably, the second AI tool module is also communicatively connected to the customer data module. It is further configured to, when the specialized simulated sales training task is generated, obtain a customer profile of the target customer associated with the skill deficiency from the customer data module, and convert the customer profile into role-building prompts to construct a simulated customer role with the customer characteristics of the target customer in the specialized simulated sales training task. Traditional AI training systems often use general and fixed customer role templates (such as "price-sensitive customer" and "indecisive customer"). While these templates provide some training value, they differ from the real customers that sales personnel will face. This system, by connecting the customer data module and the second AI tool module, achieves "dynamic role construction based on real customer profiles," making the simulated training closer to real-world practice, thereby further enhancing the "realism" and "relevance" of the specialized simulated sales training task.

[0068] In detail, the technical details of obtaining customer profiles associated with skill gaps include, but are not limited to: when the second AI tool module generates a specialized simulated sales training task in response to the first output data (i.e., skill gaps), it does not blindly and randomly select a customer profile, but intelligently associates it with target customers related to that skill gap. For example, the first AI tool module analyzes and finds that salesperson Zhang San, in his communication with "Ms. Li, a resident of an old residential area," has a skill gap of "not clearly explaining the energy-saving highlights of the product"; when the second AI tool module generates a specialized training task for this skill gap, it will request the customer data module to obtain a customer profile with similar characteristics to Ms. Li, or directly obtain Ms. Li's anonymized profile (for debriefing exercises); then, by analyzing Ms. Li's customer profile, it is found that her tags include: resident of an old residential area, high monthly household gas consumption, having inquired about energy-saving products, and moderate price sensitivity, etc., and this profile information can be used to construct an AI customer persona that is highly similar to a real customer.

[0069] In detail, the technical aspects of converting customer profiles into persona-building prompts include, but are not limited to: after acquiring the customer profile, the second AI tool module does not directly input the structured data into the AI ​​model, but instead converts it into "persona-building prompts" that the large language model can understand and execute. This conversion process is a key technical step, and examples are as follows: Original customer profile (structured data): - Customer type: Residents of older residential communities - Family characteristics: Elderly people living together - Gas usage characteristics: High-frequency cooking - Pain points: Concerns about gas safety, focus on energy efficiency - Behavioral tag: Inquired about the product but did not make a purchase. Convert to character creation prompts (natural language commands): [Character Setting] You are now playing Ms. Li, a customer living in an older neighborhood. You live with elderly relatives, cook frequently, and have high monthly gas bills. You've recently been following news about gas safety and are concerned about the safety of your old stove. You also want to replace it with a more energy-efficient one to save money. You've consulted with salespeople before but haven't made a final decision, mainly because you have some doubts about the product price and installation service.

[0070] [Personality Traits] You are cautious and will carefully inquire about the salesperson's presentation, paying particular attention to safety features and energy-saving data. You are not easily persuaded and need salespeople to use concrete data and case studies to dispel your concerns.

[0071] [Dialogue Objective] In your conversation with the salesperson, you should naturally express your concern for safety and energy conservation, and raise reasonable questions about price and installation to assess whether the salesperson can accurately grasp your needs and provide targeted responses.

[0072] [Example Question] - "Does this stove really save gas? How much money can it save in a year?" - "My elderly relative has a bad memory. Is the engine shutdown protection automatic?" - Is installation free? Do you handle the disposal of old stoves? Therefore, the character construction prompts include: identity background (to help AI understand "who I am"), personality traits (to help AI understand "how I speak"), dialogue goals (to help AI understand "what role I should play in training"), and example questions (to guide AI to generate typical questions that match the character settings).

[0073] In detail, the technical aspects of constructing simulated customer roles with realistic customer characteristics in training tasks include, but are not limited to: injecting the generated role-building prompts into the simulated customer role model as system prompts. When a salesperson initiates a specialized training task focusing on "energy-saving highlights," they will no longer be facing a generic "price-sensitive customer," but a living, breathing AI role with real customer characteristics. Salesperson: "Hello Ms. Li, you inquired about our energy-saving stove last time. Today, I'll give you a detailed introduction." AI character (simulating Ms. Li): "Great, I was just about to ask you about that. You said this stove is energy-saving, but how much gas does it actually save? My family cooks a lot, and our gas bill is over a hundred yuan a month. It would be great if we could save some, but you can't just make it sound good; you need to provide actual data." Salesperson: "Don't worry, this is a Level 1 energy efficiency cooktop with a thermal efficiency of 63%, which is about 15% more energy-efficient than a regular Level 3 cooktop..." AI character (simulating Ms. Li): "15% sounds like a lot, but that's lab data, right? Can it actually be achieved in real-world use? Also, I've heard that energy-saving stoves have low heat output, but my family stir-fries over high heat. Will that be enough?" In this way, salespeople can repeatedly practice how to handle various questions and concerns that real customers might raise in a completely safe environment. When they finally face the real Ms. Li, having already "rehearsed" it many times in simulation training, they will naturally be more composed in their response.

[0074] Through the above mechanism, the second AI tool module can also transform static customer profiles into dynamic and interactive simulated customer roles, enabling sales training to move from "standardized production" to "personalized customization," and significantly improving the practical effectiveness of training.

[0075] Preferably, the second AI tool module is further configured to, in performing the special simulated sales training task, first obtain a preset gold-medal sales interaction record, then construct a simulated gold-medal sales role based on the gold-medal sales interaction record, and start the simulated gold-medal sales role to conduct simulated sales training with the salesperson playing the role of the customer, so as to demonstrate to the salesperson the standard coping strategies for the skill gap item. Specifically, when the second AI tool module generates a special simulated sales training task targeting specific skill gaps (such as "not clearly explaining the product's energy-saving highlights"), the Gold Medal Sales Demonstration Mode can be activated. In this mode, preset Gold Medal Sales interaction records are first retrieved from the experience base (these records are high-quality interaction data selected from real transaction cases through the aforementioned excellent case selection module, containing the brilliant rhetoric and coping strategies of Gold Medal Salespeople when dealing with similar scenarios). Then, a simulated Gold Medal Salesperson role is constructed based on these records. This role is trained using the aforementioned supervised fine-tuning technology to accurately replicate the expression logic, rhetoric style, and objection handling skills of Gold Medal Salespeople. Then, the simulated Gold Medal Salesperson role (which is actually an AI robot playing the role of a simulated Gold Medal Salesperson) is activated, requiring the salesperson to play the role of a customer and engage in dialogue rehearsal with the simulated Gold Medal Salesperson role. During the drills, simulated top salespersons demonstrate standard strategies for naturally introducing product highlights, responding to typical customer inquiries, and seizing opportunities to close the deal. For example, addressing a weakness in explaining energy-saving features, a simulated top salesperson might demonstrate: "You mentioned cooking a lot. This energy-efficient stove can save about 15% on your gas bill annually. Although the price is slightly higher, based on your household gas consumption, the savings over two years will offset the price difference, making it more cost-effective in the long run." By role-playing as customers, salespeople can directly perceive the logical expression and sales techniques of top salespeople, thus internalizing these skills into their own abilities.

[0076] Preferably, the second AI tool module is further configured to dynamically switch between the simulated customer role and the simulated top sales role in response to the salesperson's mode switching command or based on a preset switching strategy during the execution of the specialized simulated sales training task, so that the salesperson can alternate between practical exercises and observational learning. Specifically, the following flexible switching modes can be supported during the training process: the salesperson can actively click the "Switch Mode" button to trigger the command, switching from practical exercises with the AI ​​customer to observing a top salesperson's demonstration; or conversely, the system can automatically switch based on a preset strategy, for example, if the salesperson fails to meet the requirements in three consecutive practical exercises, it can automatically switch to the demonstration mode to display standard scripts, and then switch back to the practical mode to continue training after the demonstration. Thus, through the aforementioned dynamic switching mechanism, the salesperson can seamlessly connect "learning" and "practice," significantly improving the training effect.

[0077] Preferably, the second AI tool module is further configured to extract personal characteristic information of the sales personnel in training and construct a personalized memory model of the sales personnel in training based on historical sales and service interaction data collected by the first AI tool module and / or historical training interaction data generated by the second AI tool module. This personalized memory model is then invoked when generating response content simulating a top salesperson role, so that the generated response content matches the personal style of the sales personnel in training. Specifically, personal characteristic information can be extracted from the historical real-world recordings (collected by the first AI tool module) and historical training performance (generated by the second AI tool module) of the sales personnel in training (i.e., the sales personnel being trained). This includes language habits (such as a preference for using metaphors), communication style (such as starting with casual conversation), and commonly used phrases (such as "We ordinary people live our lives"). This characteristic information is encoded into a personalized memory model and stored in the second AI tool module in vector form. When the second AI tool module generates the response content of the simulated top salesperson (i.e., in the observation and learning mode), it will simultaneously call the personalized memory model to constrain the output, so that the speech delivered by the simulated top salesperson retains the professional experience of the original top salesperson and fits the language style of the trainee salesperson, thereby enhancing the trainee salesperson's sense of immersion and acceptance, and improving the training effect.

[0078] Preferably, during the execution of the specialized simulated sales training task, the second AI tool module uses retrieval-augmented generation (RAG) technology to call upon a preset enterprise private knowledge base to generate responses for the simulated sales role. To avoid the large language model generating "illusions" (i.e., fabricating non-existent or incorrect product information) when generating sales scripts, the second AI tool module introduces retrieval-augmented generation (RAG) technology, which combines the generation capabilities of the large language model with the precise retrieval capabilities of the enterprise private knowledge base. During the execution of the simulated sales training task, when the AI ​​needs to respond to a salesperson's question, it first retrieves the most relevant information fragments from the preset enterprise private knowledge base based on the dialogue context. This private knowledge base contains non-public data exclusive to the enterprise, such as: gas product manuals (including technical parameters and energy efficiency ratings), product highlight databases (refined core selling points), service specifications (standard operating procedures), common customer objections and standard response scripts, and promotional activity policies, etc. After retrieving relevant information, the original dialogue content and the retrieved knowledge fragments are input into the large language model, prompting the model to "generate a response based on the following reference materials." The model then generates an accurate response that conforms to language expression habits and is strictly based on the company's actual product and service information. For example, when a salesperson asks, "What is the thermal efficiency of this stove?", the model first retrieves the accurate data "63% thermal efficiency, Level 1 energy efficiency" from the product manual. The generated response would be: "This stove has a thermal efficiency of 63%, meeting the national Level 1 energy efficiency standard, saving approximately 15% energy compared to ordinary Level 3 energy efficiency stoves." Through RAG technology, the second AI tool module ensures that the generated simulated sales responses are accurate, data-driven, and conform to company standards, avoiding information errors that may arise from general large models and ensuring that training content is highly consistent with the company's actual business. Furthermore, the third AI tool module, during outbound marketing calls, or the fourth AI tool module, during after-sales customer service, can also use retrieval enhancement generation technology to call upon a preset private enterprise knowledge base to generate AI robot responses.

[0079] In summary, the sales training empowerment and customer management system provided in this embodiment has the following technical effects: (1) This embodiment provides a new solution that can effectively link multiple AI tools to achieve data interoperability and business collaboration. It includes a first AI tool module, a second AI tool module and a customer data module. The first AI tool module collects the interaction data between sales personnel and target customers, analyzes it and generates first output data representing the sales personnel's skill shortcomings and second output data representing the target customer's demand tags / profile tags. The second AI tool module receives the first output data and automatically generates a special simulated sales training task for the skill shortcomings. The customer data module receives the second output data and updates the customer profile of the target customer. At the same time, it automatically triggers marketing actions for the target customer based on the updated customer profile. Thus, by linking multiple AI tools, the data barrier between sales training and customer management is broken down, and a full-link closed loop of "real-world data collection → skill shortcomings identification → special training generation → customer demand extraction → profile update → precise marketing trigger" is realized, which significantly improves the pertinence of employee training and the efficiency of customer operations. (2) In terms of sales training empowerment, this solution realizes the automated extraction and continuous feedback of top sales experience. That is, the first AI tool module collects sales practice interaction data and identifies the skill gaps of sales personnel, and then the second AI tool module generates special simulated sales training tasks in a targeted manner, so that the training content is transformed from "general standardized courses" to "personalized training based on practical gaps". In particular, through the excellent case screening module, the system can respond to the signal that the customer status changes to a transaction, automatically retrieve the corresponding historical interaction data and call the preset scoring model for intelligent evaluation, mark the high-quality interaction records with scores higher than the threshold as excellent interaction records and store them in the experience library, and then continuously optimize the simulated sales role model and / or simulated customer role model in the second AI tool module through supervised fine-tuning technology. This mechanism forms a spiral upward closed loop of "experience extraction - model fine-tuning - special training - practical application - re-extraction", which enables the practical experience of top sales to be replicated and inherited on a large scale, significantly improving the pertinence and effectiveness of sales training. (3) In terms of in-depth mining of customer value, this solution breaks down the data barriers of multiple customer touchpoints and realizes the accurate construction and dynamic updating of customer profiles. Specifically, the first AI tool module extracts the first demand tag and / or first profile tag of the target customer from sales practice, the third AI tool module extracts the second demand tag and / or second profile tag from marketing outbound call interaction, and the fourth AI tool module extracts the third demand tag and / or third profile tag from after-sales customer service interaction. The customer data module integrates the multi-source tags with the pre-stored basic customer information based on a unified data standard to generate a complete customer profile containing multi-dimensional tags. This mechanism greatly improves the completeness of customer profiles. Experimental data shows that the completeness of customer profiles can be increased from 30% in the traditional CRM system to 110% (including newly added industry-specific tags), realizing the holographic integration of customer information and completely avoiding the problem of data fragmentation. At the same time, the system can capture the potential needs of customers in real time, such as the replacement of old stoves and the upgrading of heating systems, so that the efficiency of customer data (i.e. the proportion of customer data containing clear needs) increases from 40% to 100%, effectively reducing the loss of high-value customer data. (4) Regarding the two-way collaboration between training and customer data, this solution constructs a full-link collaborative mechanism of "training to improve capabilities - capabilities to optimize services - services to accumulate customer data - customer data to drive marketing". That is, the first AI tool module generates first output data pointing to the sales staff's skill shortcomings and second output data pointing to the needs of target customers. The former drives the second AI tool module to generate targeted training tasks, and the latter drives the customer data module to update customer profiles. At the same time, when the second AI tool module generates special simulated sales training tasks, it can also obtain the target customer profiles related to the skill shortcomings from the customer data module and convert them into role-building prompts. In the simulated training, AI roles with real customer characteristics are built, so that sales staff can rehearse for real customers they are about to face in a safe environment. This two-way linkage realizes the mutual empowerment of training and customer data, avoiding the drawbacks of the separation between the two in the traditional system. (5) In terms of precision marketing and operational efficiency, this solution realizes intelligent marketing triggering based on real-time updated customer profiles. That is, the customer data module generates personalized marketing plans suitable for target customers based on the updated customer profiles through a large language model, and triggers the third AI tool module to start the first AI robot to conduct automated outbound marketing according to the plan. During the triggering process, the system intelligently integrates multiple control mechanisms: ensuring that only high-credibility demand tags can trigger marketing by judging the confidence threshold; realizing real-time reach of high-priority demands and timed batch processing of low-priority demands by recognizing the urgency level; ensuring that the minimum necessary fields required for the generation of marketing plans are complete by verifying the profile completeness; and avoiding excessive harassment of the same customer by judging the marketing duration threshold. Thanks to these mechanisms, the AI ​​tool and the customer data module achieve full-link data interconnection, the employee ID recording feeds back to training optimization, and the customer data supports marketing decisions, reducing the number of operational process links by 40% and improving overall efficiency by 50%. More importantly, the customer data module automatically generates customized plans based on complete profiles, and combined with AI outbound calls to reach customers, the gas product marketing conversion rate has increased significantly from 8% to 38% (an increase of 30 percentage points), significantly reducing the cost of ineffective marketing. (6) This solution effectively solves the problems of data fragmentation, weak training relevance and low customer resource utilization in the existing technology through the collaborative linkage of multiple AI tool modules. It achieves deep integration and mutual empowerment of sales training and customer resource management, generating significant technical effects and commercial value, and is easy to apply and promote in practice.

[0080] Example 2 like Figure 2 As shown, this embodiment provides a sales training empowerment and customer management method that integrates multiple AI tools. It is applied to the sales training empowerment and customer management system described in Embodiment 1, and includes, but is not limited to, the following steps S100 to S300.

[0081] S100. The first AI tool module first collects sales and service interaction data generated by the communication between sales personnel and target customers, then analyzes the sales and service interaction data and generates first output data and second output data based on the analysis results. The first output data is used to characterize the skill gaps of the sales personnel, and the second output data is used to characterize the first demand tag and / or first profile tag of the target customer.

[0082] S200. The second AI tool module receives the first output data and, in response to the first output data, automatically generates a specific simulated sales training task targeting the skill gap item.

[0083] S300. The customer data module first receives the second output data and, in response to the second output data, updates the customer profile of the target customer. Then, based on the updated customer profile, it automatically triggers marketing actions for the target customer.

[0084] Preferably, based on the updated customer profile, marketing actions targeting the target customer are automatically triggered, including: Based on the updated customer profile, a preset enterprise product inventory and marketing policy knowledge base is invoked. A personalized marketing plan suitable for the target customer is generated by using retrieval-enhanced generation technology to constrain the large language model. The third AI tool module, which is used to execute the marketing action, is then triggered to start the first AI robot to call the target customer in accordance with the personalized marketing plan for automated marketing.

[0085] Preferably, in response to a signal from the customer data module indicating a change in the target customer's status to a completed transaction, the excellent case screening module automatically retrieves historical sales and service interaction data corresponding to the target customer, calls a preset scoring model to score the historical sales and service interaction data, and then marks the historical sales and service interaction data with scores higher than a preset threshold as excellent interaction records and stores them in the experience library of the second AI tool module to optimize simulated sales training tasks. The historical sales and service interaction data includes at least audio recording data collected by the first AI tool module.

[0086] More preferably, the second AI tool module responds to the update signal of the experience base, acquires the excellent interaction records, and generates fine-tuning samples based on the excellent interaction records to supervise and fine-tune the preset simulated sales role model and / or simulated customer role model in the second AI tool module.

[0087] Preferably, analyzing the sales and service interaction data and generating first output data based on the analysis results includes: By using a unified language model and combining it with multiple preset detection dimension prompts, semantic analysis is performed on the sales and service interaction data to identify sales process nodes that the sales personnel did not execute or product selling points that were not mentioned, which are then used as the skill deficiency items in the first output data.

[0088] Preferably, the method further includes: The third AI tool module first collects marketing outbound call interaction data generated by the first AI robot / first natural person communicating with the target customer, then analyzes the marketing outbound call interaction data and generates third output data based on the analysis results, wherein the third output data is used to characterize the second demand tag and / or second profile tag of the target customer; The fourth AI tool module first collects after-sales customer service interaction data generated by the second AI robot / second natural person communicating with the target customer, then analyzes the after-sales customer service interaction data and generates fourth output data based on the analysis results, wherein the fourth output data is used to characterize the third demand tag and / or third profile tag of the target customer; Updating the customer profile of the target customer includes: based on a unified data standard, integrating the demand tags and / or profile tags received from the first AI tool module, the third AI tool module, and the fourth AI tool module with the basic customer information of the target customer pre-stored in the customer data module to generate a customer profile containing multi-dimensional tags.

[0089] Preferably, based on the updated customer profile, marketing actions targeting the target customer are automatically triggered, including: Determine whether the confidence level of the updated demand tag in the customer profile exceeds a preset confidence threshold. If so, automatically trigger a marketing action for the target customer; otherwise, stop triggering a marketing action for the target customer. And / or, identify the urgency of the updated demand tags in the customer profile and determine whether the urgency exceeds a preset urgency threshold. If so, automatically trigger marketing actions for the target customer in real time; otherwise, automatically trigger marketing actions for the target customer at regular intervals or in sequence according to the queuing status of marketing tasks. And / or, verify whether the updated customer profile contains the preset minimum necessary fields. If so, automatically trigger marketing actions for the target customer; otherwise, stop triggering marketing actions for the target customer and generate an information completion task for the sales personnel.

[0090] Preferably, automatically triggering marketing actions targeting the target customers includes: Determine whether the time from the most recent marketing timestamp to the current timestamp exceeds a preset time threshold. If so, automatically trigger a marketing action for the target customer. Otherwise, stop triggering the marketing action for the target customer until the time exceeds the preset time threshold. The most recent marketing timestamp refers to the execution timestamp of the most recent historical marketing action for the target customer. And / or, determine whether there is a marketing circuit breaker tag for the target customer in the customer data module. If so, stop triggering marketing actions for the target customer until the tag is canceled. Otherwise, automatically trigger marketing actions for the target customer. The marketing circuit breaker tag is marked when the target customer's marketing feedback intent is a clear rejection and is canceled when the target customer actively initiates a service request. The marketing feedback intent is extracted based on the results of historical marketing actions corresponding to the target customer.

[0091] Preferably, when the specialized simulated sales training task is generated, the second AI tool module obtains the customer profile of the target customer associated with the skill deficiency item from the customer data module, and converts the customer profile into role-building prompts to construct a simulated customer role with the customer characteristics of the target customer in the specialized simulated sales training task.

[0092] Preferably, in performing the special simulated sales training task, the second AI tool module first obtains a preset gold-medal sales interaction record, then constructs a simulated gold-medal sales role based on the gold-medal sales interaction record, and starts the simulated gold-medal sales role to conduct simulated sales training with the salesperson playing the role of the customer, so as to demonstrate to the salesperson the standard coping strategies for the skill gap item.

[0093] Preferably, during the execution of the specialized simulated sales training task, the second AI tool module responds to the salesperson's mode switching command or, based on a preset switching strategy, dynamically switches between the simulated customer role and the simulated top salesperson role, so that the salesperson can alternate between practical exercises and observational learning.

[0094] Preferably, the second AI tool module extracts the personal characteristic information of the sales personnel in training and constructs a personalized memory model of the sales personnel in training based on the historical sales and service interaction data collected by the first AI tool module and / or the historical training interaction data generated by the second AI tool module. The personalized memory model is called when generating the response content of the simulated top sales role so that the generated response content matches the personal style of the sales personnel in training.

[0095] Preferably, during the execution of the specialized simulated sales training task, the second AI tool module uses a retrieval enhancement generation technology to call a preset enterprise private knowledge base to generate the response content of the simulated sales role.

[0096] The working process, working details and technical effects of the aforementioned method provided in this embodiment can be found in the sales training empowerment and customer information management system described in Embodiment 1, and will not be repeated here.

[0097] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A sales training empowerment and customer management system that integrates multiple AI tools, characterized in that: It includes a first AI tool module, a second AI tool module, and a customer data module; The first AI tool module is used to first collect sales and service interaction data generated by the communication between sales personnel and target customers, then analyze the sales and service interaction data and generate first output data and second output data based on the analysis results. The first output data is used to characterize the skill gaps of the sales personnel, and the second output data is used to characterize the first demand tag and / or first profile tag of the target customer. The second AI tool module is communicatively connected to the first AI tool module, and is used to receive the first output data and, in response to the first output data, automatically generate a special simulated sales training task for the skill deficiency item. The customer data module is communicatively connected to the first AI tool module, and is used to receive the second output data and update the customer profile of the target customer in response to the second output data; The customer data module is also used to automatically trigger marketing actions for the target customer based on the updated customer profile.

2. The sales training empowerment and customer management system as described in claim 1, characterized in that, It also includes a third AI tool module that has a communication connection with the customer data module, wherein the third AI tool module is used to perform the marketing action; Based on the updated customer profile, marketing actions targeting the target customer are automatically triggered, including: Based on the updated customer profile, the system calls upon a pre-defined enterprise product inventory and marketing policy knowledge base, uses retrieval-enhanced generation technology to constrain the large language model to generate a personalized marketing plan suitable for the target customer, and triggers the third AI tool module to initiate the first AI robot to call the target customer for automated marketing according to the personalized marketing plan.

3. The sales training empowerment and customer management system as described in claim 1, characterized in that, It also includes an excellent case selection module that is respectively connected to the customer data module, the first AI tool module and the second AI tool module; The excellent case screening module is used to respond to the signal that the customer status of the target customer in the customer data module has changed to a completed transaction. It automatically retrieves the historical sales and service interaction data corresponding to the target customer, calls the preset scoring model to score the historical sales and service interaction data, and then marks the historical sales and service interaction data with a score higher than a preset threshold as excellent interaction records and stores them in the experience library of the second AI tool module to optimize the simulated sales training task. The historical sales and service interaction data includes at least the audio data collected by the first AI tool module.

4. The sales training empowerment and customer management system as described in claim 3, characterized in that, The second AI tool module is also used to respond to the update signal of the experience base, acquire the excellent interaction records, and generate fine-tuning samples based on the excellent interaction records to supervise and fine-tune the preset simulated sales role model and / or simulated customer role model in the second AI tool module.

5. The sales training empowerment and customer management system as described in claim 1, characterized in that, Analyze the sales and service interaction data and generate first output data based on the analysis results, including: By using a unified language model and combining it with multiple preset detection dimension prompts, semantic analysis is performed on the sales and service interaction data to identify sales process nodes that the sales personnel did not execute or product selling points that were not mentioned, which are then used as the skill deficiency items in the first output data.

6. The sales training empowerment and customer management system as described in claim 1, characterized in that, It also includes a third AI tool module and a fourth AI tool module that are respectively connected to the customer data module; The third AI tool module is used to first collect marketing outbound call interaction data generated by the first AI robot / first natural person communicating with the target customer, then analyze the marketing outbound call interaction data and generate third output data based on the analysis results, wherein the third output data is used to characterize the target customer's second demand tag and / or second profile tag; The fourth AI tool module is used to first collect after-sales customer service interaction data generated by the second AI robot / second natural person communicating with the target customer, then analyze the after-sales customer service interaction data and generate fourth output data based on the analysis results, wherein the fourth output data is used to characterize the third demand tag and / or third profile tag of the target customer; Updating the customer profile of the target customer includes: based on a unified data standard, integrating the demand tags and / or profile tags received from the first AI tool module, the third AI tool module, and the fourth AI tool module with the basic customer information of the target customer pre-stored in the customer data module to generate a customer profile containing multi-dimensional tags.

7. The sales training empowerment and customer management system as described in claim 1, characterized in that, Based on the updated customer profile, marketing actions targeting the target customer are automatically triggered, including: Determine whether the confidence level of the updated demand tag in the customer profile exceeds a preset confidence threshold. If so, automatically trigger a marketing action for the target customer; otherwise, stop triggering a marketing action for the target customer. And / or, identify the urgency of the updated demand tags in the customer profile and determine whether the urgency exceeds a preset urgency threshold. If so, automatically trigger marketing actions for the target customer in real time; otherwise, automatically trigger marketing actions for the target customer at regular intervals or in sequence according to the queuing status of marketing tasks. And / or, verify whether the updated customer profile contains the preset minimum necessary fields. If so, automatically trigger marketing actions for the target customer; otherwise, stop triggering marketing actions for the target customer and generate an information completion task for the sales personnel.

8. The sales training empowerment and customer management system as described in claim 1, characterized in that, Automatically trigger marketing actions targeting the aforementioned customer, including: Determine whether the time from the most recent marketing timestamp to the current timestamp exceeds a preset time threshold. If so, automatically trigger a marketing action for the target customer. Otherwise, stop triggering the marketing action for the target customer until the time exceeds the preset time threshold. The most recent marketing timestamp refers to the execution timestamp of the most recent historical marketing action for the target customer. And / or, determine whether there is a marketing circuit breaker tag for the target customer in the customer data module. If so, stop triggering marketing actions for the target customer until the tag is canceled. Otherwise, automatically trigger marketing actions for the target customer. The marketing circuit breaker tag is marked when the target customer's marketing feedback intent is a clear rejection and is canceled when the target customer actively initiates a service request. The marketing feedback intent is extracted based on the results of historical marketing actions corresponding to the target customer.

9. The sales training empowerment and customer management system as described in claim 1, characterized in that, The second AI tool module is also communicatively connected to the customer data module, and is also used to obtain the customer profile of the target customer associated with the skill deficiency item from the customer data module when the special simulated sales training task is generated, and convert the customer profile into role building prompt words, so as to build a simulated customer role with the customer characteristics of the target customer in the special simulated sales training task; And / or, the second AI tool module is also used to, in performing the special simulated sales training task, first obtain a preset gold-medal sales interaction record, then construct a simulated gold-medal sales role based on the gold-medal sales interaction record, and start the simulated gold-medal sales role to conduct simulated sales training with the salesperson playing the role of the customer, so as to demonstrate to the salesperson the standard coping strategy for the skill deficiency item. And / or, the second AI tool module is also used to dynamically switch between the simulated customer role and the simulated top sales role in response to the salesperson's mode switching instruction or based on a preset switching strategy during the execution of the special simulated sales training task, so that the salesperson can alternate between practical exercises and observation and learning. And / or, the second AI tool module is further configured to extract personal characteristic information of the sales personnel in training and construct a personalized memory model of the sales personnel in training based on the historical sales and service interaction data collected by the first AI tool module and / or the historical training interaction data generated by the second AI tool module, and call the personalized memory model when generating the response content of the simulated top sales role so that the generated response content matches the personal style of the sales personnel in training. And / or, during the execution of the special simulated sales training task, the second AI tool module uses enhanced generation technology to call a preset enterprise private knowledge base to generate the response content of the simulated sales role.

10. A sales training empowerment and customer management method that integrates multiple AI tools, characterized in that: The system is applied to the sales training empowerment and customer management system as described in any one of claims 1 to 9, and includes the following steps: The first AI tool module first collects sales and service interaction data generated by the communication between sales personnel and target customers. Then, it analyzes the sales and service interaction data and generates first output data and second output data based on the analysis results. The first output data is used to characterize the skill gaps of the sales personnel, and the second output data is used to characterize the first demand tag and / or first profile tag of the target customer. The second AI tool module receives the first output data and, in response to the first output data, automatically generates a specific simulated sales training task targeting the skill gap item. The customer data module first receives the second output data and, in response to the second output data, updates the customer profile of the target customer. Then, based on the updated customer profile, it automatically triggers marketing actions targeting the target customer.

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