Intelligent sales call full-period auxiliary system based on artificial intelligence

Abstract

The invention relates to the technical field of sales call full-period intelligent auxiliary systems, in particular to a sales call full-period intelligent auxiliary system based on artificial intelligence, which comprises a customer archive intelligent construction module for collecting and integrating customer full-dimension information, calculating customer archive integrity D and providing parameters for evaluation preparation before call; the customer value layering management module is used for realizing accurate layering of customer values based on customer archive integrity D and optimizing an application scene for preparing evaluation parameters before conversation; the verbal skill strategy intelligent pre-generation module is used for generating personalized verbal skill based on the customer label, calculating a verbal skill strategy matching degree T and perfecting parameters to be evaluated before a call; according to the invention, through cooperative operation of independent modules, an intelligent auxiliary closed loop of a sales call full period is constructed, and intelligent integration of customer information, accurate assistance of real-time communication and automatic processing of call redisk are realized.

Description

Technical Field

[0001] This invention relates to the field of intelligent assistance systems for the entire sales call lifecycle, and in particular to an intelligent assistance system for the entire sales call lifecycle based on artificial intelligence. Background Technology

[0002] In the core scenarios of sales and service, phone calls are the key medium connecting sales and customers, and their efficiency and quality directly determine the conversion rate.

[0003] In the traditional sales model, before a call, fragmented customer information needs to be manually organized, making it difficult to quickly extract core needs and key points of past interactions. During the call, multiple tasks such as understanding customer expressions, organizing the script, and handling objections need to be carried out simultaneously, which can easily lead to problems such as untimely responses and deviations in the direction of communication. After the call, the reliance on manual transcription and summarization of key points is not only time-consuming and labor-intensive, but also subject to subjective bias. Summary of the Invention

[0004] To address the technical problem in traditional sales models where fragmented customer information needs to be manually organized before a call, making it difficult to quickly extract core needs and key points of historical interactions, this invention provides an intelligent assistance system for the entire sales call lifecycle based on artificial intelligence.

[0005] The technical solution adopted in this invention is: an intelligent assistance system for the entire sales call cycle based on artificial intelligence, comprising: Intelligent Customer Profile Construction Module: Collects and integrates customer information from all dimensions, calculates the completeness of the customer profile (D), and provides parameters for pre-call preparation assessment; Customer Value Segmentation Management Module: Based on the completeness of customer profiles (D), it enables precise segmentation of customer value and optimizes the application scenarios of pre-call preparation assessment parameters; Intelligent pre-generation module for dialogue strategies: Generates personalized dialogue based on customer tags, calculates the dialogue strategy matching degree T, and improves the parameters for pre-call preparation assessment; Intelligent material matching and preparation module: Matches materials with customer needs and sales scripts, calculates material matching degree M, and outputs the final result of pre-call preparation assessment; Real-time intelligent call sensing module: Monitors call status in real time and calculates customer emotional state. This provides parameters for assessing conversion potential after a call; Dynamic script recommendation module: Recommends scripts and materials based on real-time data to improve call quality score (Q) and refine parameters for evaluating post-call conversion potential. Intelligent Customer Objection Handling Module: Intelligently handles customer objections, optimizing both call quality score (Q) and customer emotional positivity (E). Post-call debriefing strategy generation module: Automatically completes call debriefing, calculates demand matching degree F, outputs post-call conversion potential assessment results and generates follow-up strategies.

[0006] In one embodiment, the intelligent customer profile construction module specifically comprises the following: Automatically collect and integrate comprehensive customer information, calculate customer profile completeness (D), and provide parameters for pre-call preparation assessment. The system automatically collects basic customer information, uses artificial intelligence to analyze customers' historical call records and chat content, and automatically extracts information such as symptoms, communication preferences, purchased services, and service progress, and populates them into the corresponding fields in the customer profile. Sales personnel can double-click on a field to customize its editing, and click on a blank area or press Enter to save. The system synchronously generates customer tags, including consumption intention, decision type, and loyalty. Customer files are categorized horizontally according to customer attributes, decision-making factors, and consumption characteristics; After this module is completed, the system calculates the ratio of the number of filled fields to the total number of fields, and obtains the customer profile completeness D. The higher the D value, the more comprehensive the customer information.

[0007] In one embodiment, the customer value tiered management module specifically includes the following: the system combines the completeness of customer profiles... Customers are categorized into three value levels—A, B, and C—based on their total spending, historical call count, and purchase intent data. The higher the customer's value, the more accurate the value segmentation results. Category A customers: High total spending, strong intention to make a purchase. Value 20.8; Category B customers: those with spending history but moderate interest. Category C customers: New customers or those with low intent. ; The system supports batch import of customer information tables, and sales staff can perform batch management operations on customers, with the management status automatically marked in the customer list; The customer list displays search results, profile picture, name, last call time, and value-added information.

[0008] In one embodiment, the intelligent pre-generation module for dialogue strategies specifically includes the following: Artificial intelligence matches the optimal communication strategy based on customer tags; the system has a built-in library of commonly used responses, covering basic greetings and conversation-ending scripts; sales staff can modify, delete, and add scripts through the "management" function, and new scripts are automatically placed at the end of the list; the artificial intelligence calculates the dialogue strategy matching degree. The calculation logic is based on the overlap between the pre-generated script and customer tags and historical success cases, with a value range of [0,1]. This parameter will be incorporated into the calculation system for pre-call preparation assessment; if If the value is below 0.7, the system will automatically prompt the salesperson to adjust the direction of their sales pitch.

[0009] In one embodiment, the intelligent material matching preparation module specifically performs the following: Artificial intelligence automatically matches relevant materials, including product manuals, double-blind trial reports, and health questionnaires, based on customer needs and pre-generated scripts; the accuracy of material matching directly affects… value; Sales staff can upload custom materials. Newly uploaded materials are automatically sorted at the top of the list, and can be renamed, deleted, or dragged to adjust the order. Artificial intelligence computing science matching The calculation logic is that the recommended materials and customer needs are within the range of [0,1]. This parameter will be used together with DT in the Tonghua pre-assessment. All matching materials are synchronized to the call support module, allowing sales staff to send them to customers with a single click. After this module completes, the system calculates the customer's readiness level using a formula.

[0010] ; in , , As weight, if If it is determined to be ready; The system automatically prompts sales staff to supplement customer information or optimize sales scripts and materials.

[0011] In one embodiment, the real-time intelligent call sensing module specifically works as follows: After a salesperson initiates a call, the system automatically opens a floating window to display the call duration, dialogue stage, and customer emotional state in real time. The calculation of values ​​provides a real-time data source; In each round of dialogue, the artificial intelligence identifies the customer's concerns, symptoms, motivations, and intentions, generates a 3-5 word tag, and updates the tag content in real time. The system analyzes customer emotions every 5 rounds of dialogue, with different emotions corresponding to different colors. The percentage of each emotion type is... The basis for value calculation: The system calculates the customer's emotional positivity based on the proportion of positive emotions and the stability of emotional fluctuations. The value range is [0,1]. This parameter will be included in the calculation system for post-call conversion potential assessment; if The system automatically prompts sales staff to adjust their communication strategies to avoid... The value decreased further.

[0012] In one embodiment, the dynamic script recommendation module specifically includes the following: artificial intelligence combines real-time dialogue content and customer emotional state. Customer tags are used to generate targeted recommendation replies. Salespeople can simply click to fill the message into the chat box and send it. The relevance of the message directly impacts the salesperson's response. value; When a customer raises an objection, the system automatically matches relevant materials and recommends double-blind trial reports, which sales personnel can send with a single click, improving the timing and accuracy of material delivery. value; The system supports AI-powered automated response functionality, allowing sales staff to delegate customer requests to AI for automatic replies. The automated response status is automatically marked, and sales staff can take over the conversation at any time. The accuracy of the AI-managed responses is also taken into account. The range of values ​​to be calculated; Artificial intelligence calculates the speech matching degree in real time, with a value ranging from [0,1]. This parameter will be incorporated into the calculation system for post-call conversion potential assessment. If the speech matching degree is lower than 0.6, the system will automatically optimize the recommendation strategy and directly improve the conversion rate. value.

[0013] In one embodiment, the intelligent customer objection handling module specifically includes the following: the system has a built-in library of common objection handling scripts, covering price objections, effectiveness objections, and trust objections. Artificial intelligence automatically recommends the optimal handling solution based on the objection type, and the timeliness of objection handling is optimized. Key factors in value; when encountering objections not covered by the script library, artificial intelligence generates personalized handling scripts in real time and synchronizes them to the frequently used response library; The system records the entire objection handling process and automatically analyzes the handling effect: successful handling improves customer's positive emotions. And score the call quality. Increase by 0.1-0.2; if processing fails, indicate the reason for the sales record failure.

[0014] In one embodiment, the debriefing strategy generation module specifically performs the following: after the call ends, the system automatically uploads the recording and completes the transcription; AI identifies all stages of the call process, including icebreaking, need confirmation, and product introduction, generates a call summary, and outputs sales script evaluations in the form of Lark comments. The evaluation value is included in the final calculation of the Q value. The system generates a recording analysis report containing information such as call duration, effective duration, and Q value. At the same time, artificial intelligence analyzes the fit between customer needs and product functions and calculates the required cooperation range [0,1]. This parameter, along with E, participates in the post-call conversion potential assessment. The system calculates the order conversion potential C using a formula; C = α × E + β × Q + γ × F; The default values ​​for α, β, and γ weights are 0.4, 0.3, and 0.3, respectively. Follow-up strategies are generated based on the c value: follow up with high-potential customers within 1-2 days, follow up with medium-potential customers within 3-5 days, and follow up with low-potential customers within 7 days.

[0015] The beneficial effects of this invention are as follows: Compared with the prior art, this invention constructs an intelligent auxiliary closed loop for the entire sales call cycle through the collaborative operation of independent modules, realizing intelligent integration of customer information, precise assistance in real-time communication, and automated processing of call review, thereby improving sales communication efficiency and conversion rate; the system's hierarchical permission management function meets the different needs of sales, supervisors, and super administrators. Sales can view their own recordings and customer data, supervisors can view all data of their team, and super administrators can view all data within the system, facilitating team management and assessment; the system's script library and material library can be continuously iterated and optimized. With the increase in usage, the accuracy of artificial intelligence recommendations continues to improve, forming a virtuous cycle of self-evolution; the system's sentiment analysis and objection handling functions effectively improve the customer communication experience and reduce customer churn rate. Attached Figure Description

[0016] Figure 1 This is a system structure block diagram of the present invention; Figure 2 This is a flowchart of the intelligent customer profile construction module in this invention; Figure 3 This is a flowchart of the customer value tiered management module in this invention; Figure 4 This is a flowchart of the intelligent pre-generation module for dialogue strategies in this invention; Figure 5 This is a flowchart of the intelligent material matching and preparation module in this invention; Figure 6 This is a flowchart of the dynamic recommendation of dialogue and material delivery module in this invention; Figure 7 This is a flowchart of the intelligent customer objection processing module in this invention; Figure 8 This is a flowchart of the automatic post-call review and follow-up strategy generation module in this invention. Detailed Implementation

[0017] In the description of this invention, it should be noted that the terms "front", "up", "down", "left", "right", "vertical", "horizontal", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0018] refer to Figure 1-8 To address the problems existing in the background technology, this application proposes the following technical solution: an intelligent assistance system for the entire sales call cycle based on artificial intelligence, specifically including the following modules: Intelligent Customer Profile Building Module: Automatically collects and integrates comprehensive customer information, and calculates the completeness of customer profiles. This provides parameters for pre-call preparation assessment.

[0019] The system automatically collects basic customer information, including name, age, gender, age at diagnosis, contact person, and customer origin. The customer origin field cannot be manually modified to ensure data authenticity. Artificial intelligence analyzes customers' historical call records and chat content, automatically extracting information such as symptoms, communication preferences, purchased services, and service progress, and populating the corresponding fields in the customer profile, reducing the workload of manual data entry.

[0020] Salespeople can double-click fields to customize their work, and click on a blank area or press Enter to save. The system simultaneously generates nine types of customer tags that cannot be manually modified, including consumption intention, decision type, and loyalty, providing basic data support for subsequent parameter calculations.

[0021] Customer profiles are categorized horizontally by customer attributes, decision-making factors, and consumption characteristics, allowing sales staff to view them with a single click. After this module is completed, the system calculates the ratio of the number of filled fields to the total number of fields to determine the completeness of the customer profiles. , A higher value indicates more comprehensive customer information.

[0022] The above technical solution utilizes artificial intelligence to automatically collect and integrate comprehensive customer information, significantly reducing manual data entry while ensuring the authenticity of core data such as customer origins. The system-generated customer tags are objective and cannot be manually modified, providing reliable support for subsequent analysis. The categorized display of customer profiles allows sales staff to quickly locate key information and fully grasp customer characteristics. This module effectively solves the problems of low efficiency and fragmented information in traditional customer information processing, laying a solid data foundation for accurate preparation before sales calls and preventing communication effectiveness from being affected by information omissions.

[0023] Customer value tiered management module: based on customer profile completeness This enables precise segmentation of customer value and optimizes the application scenarios of pre-call preparation assessment parameters.

[0024] The system combines customer file completeness Based on data such as total customer spending, historical call count, and purchase intent, customers are categorized into three value levels: A, B, and C. The higher the customer's value, the more accurate the value segmentation results.

[0025] Category A customers (important, marked in red): high total spending, strong purchase intention. Value 20.8; Category B customers (minor, marked in yellow): have a spending history but moderate intention to spend. Category C customers (general, marked in green): new customers or those with low intent. .

[0026] The system supports batch import of customer information tables, and sales staff can perform batch management operations on customers. The management status is automatically marked in the customer list, which facilitates team collaboration and management.

[0027] The customer list displays information such as search history, profile picture, name, last call time, and value tips, allowing sales staff to quickly filter target customers and focus on high-value customers. For Class A clients, priority should be given to allocating resources for preparation.

[0028] The above technical solution enables precise segmentation of customer value based on customer profile information, helping sales clearly distinguish customer priorities and thus rationally allocate communication efforts and resources. Batch import and managed functions improve the efficiency of team collaboration management, avoiding resource waste caused by repetitive operations. The clear display of the customer list allows sales to quickly filter target customers and focus on high-value customers for targeted preparation. Simultaneously, the segmented management model optimizes the utilization efficiency of customer resources, preventing sales from investing too much energy in low-value customers, promoting the development of customer management towards refinement and efficiency, and helping the team improve its overall customer operation capabilities.

[0029] Intelligent pre-generation module for sales scripts: Generates personalized sales scripts based on customer tags and calculates the matching degree of sales script strategies. Improve the parameters for pre-call preparation assessment.

[0030] Artificial intelligence matches the optimal communication strategy based on customer tags. For example, it generates sales pitches that emphasize cost-effectiveness for "price-sensitive" customers and sales pitches that include clinical data for "authority-oriented" customers.

[0031] The system has a built-in library of commonly used replies, covering basic phrases such as greetings and ending conversations. Sales staff can modify, delete, and add phrases through the "Management" function. New phrases are automatically placed at the end of the list, enriching the phrase library while improving matching flexibility.

[0032] AI-powered speech strategy matching degree The calculation logic is the overlap between the pre-generated script and customer tags and historical success cases, with a value range of [0,1]. This parameter will be incorporated into the calculation system of pre-call preparation assessment.

[0033] like If the value is below 0.7, the system will automatically prompt the salesperson to adjust the direction of the sales pitch and recommend successful communication cases of similar customers; the pre-generated scripts will be synchronized to the call assistance module to support real-time recommendations.

[0034] The above technical solution leverages artificial intelligence to generate personalized communication scripts for sales, tailoring them to the unique characteristics of different customers, making the scripts more targeted and effectively improving communication effectiveness. The built-in library of commonly used responses meets the needs of basic communication scenarios, reducing the time cost of sales script preparation. The function of supporting customization and adding scripts allows for continuous enrichment of the script reserve, adapting to more diverse communication scenarios. When the script's relevance is insufficient, the system's prompt function helps sales adjust their direction in a timely manner, avoiding the use of ineffective scripts. This module provides strong script support for sales calls, giving salespeople more confidence in communication and laying the foundation for efficient responses.

[0035] Intelligent Material Matching and Preparation Module: Matches materials to customer needs and sales pitches, and calculates the material matching degree. Output the final results of the pre-call preparation assessment.

[0036] Artificial intelligence automatically matches relevant materials, including product manuals, double-blind trial reports, and health questionnaires, based on core customer needs and pre-generated scripts, supporting Word and PDF formats. The accuracy of material matching directly impacts... value.

[0037] Sales staff can upload custom materials through "Link Library" and "File Library". Newly uploaded materials are automatically sorted at the top of the list, and can be renamed, deleted, or dragged to adjust the order.

[0038] Artificial intelligence computing science matching The calculation logic is that the recommended materials and customer needs are within the range of [0,]. This parameter will be used together with DT in the Tonghua pre-assessment. ; in , , As weight, if If it is determined to be ready; The system automatically prompts sales staff to supplement customer information or optimize sales scripts and materials.

[0039] The above technical solution utilizes artificial intelligence to accurately match customer needs with corresponding materials, eliminating the tedious process of sales staff manually searching for materials and improving the efficiency of material preparation. It supports custom uploading and management of materials, ensuring that material reserves better align with the team's business needs, while flexibly adjusting the order of materials to guarantee priority display of important materials. The one-click material sending function allows sales staff to provide supporting materials promptly during communication, enhancing product persuasiveness. Furthermore, the module's assessment of preparation status ensures that sales staff are fully prepared with materials before calls, avoiding disruptions to the communication rhythm due to missing materials, and promoting the standardization and completeness of pre-call preparation.

[0040] Real-time intelligent call sensing module: Monitors call status in real time and calculates customer emotional state. This provides parameters for assessing conversion potential after a call.

[0041] After a salesperson initiates a call, the system automatically opens a floating window that displays the call duration, conversation stage (icebreaker, needs confirmation, etc.), and customer emotional state in real time, providing a clear view of the conversation. The calculation of values ​​provides a real-time data source.

[0042] Each round of dialogue, the AI ​​identifies the customer's concerns, symptoms, motivations, and intentions, generates a 3-5 word tag (such as "symptom description" or "needs follow-up"), and updates the tag content in real time.

[0043] The system analyzes customer emotions every 5 rounds of dialogue, covering 9 types including positive, sad, and angry. Different emotions are marked with different colors: green (positive), red (negative), and orange (neutral). The percentage of each emotion type is as follows: The core basis for value calculation.

[0044] The system calculates the customer's emotional positivity based on the percentage of positive emotions and the stability of emotional fluctuations. The value range is [0,1]. This parameter will be included in the calculation system for post-call conversion potential assessment. If The system automatically prompts sales staff to adjust their communication strategies to avoid... The value decreased further.

[0045] The above technical solution includes: real-time monitoring of call status during the call, generating customer focus tags to help sales quickly grasp core customer needs and adjust communication priorities accordingly; real-time analysis and tagging of customer emotions allowing sales to keenly perceive changes in customer mood, prompting adjustments to communication strategies when negative emotions arise to prevent escalation; and a floating window display format enabling sales to access key data without switching pages, ensuring seamless communication. This module enhances sales' responsiveness during calls, making communication more aligned with the customer's real-time state, optimizing the customer's communication experience, and reducing communication errors caused by information delays.

[0046] Dynamic script recommendation module: Recommends scripts and materials based on real-time data to improve call quality scores. Improve the parameters for evaluating conversion potential after a call.

[0047] Artificial intelligence combines real-time conversation content and customer emotional state. Customer tags are used to generate targeted recommendation replies. Salespeople can simply click to fill the message into the chat box and send it. The relevance of the message directly impacts the salesperson's response. value.

[0048] When customers raise objections, the system automatically matches relevant materials. For example, if a customer asks, "Is there clinical data to support this?", the system automatically recommends double-blind trial reports, which sales personnel can send with a single click. This improves the timing and accuracy of material delivery. value.

[0049] The system supports AI-powered automated response functionality, allowing sales staff to delegate customer requests to AI for automatic replies. The automated response status is automatically marked, and sales staff can take over the conversation at any time. The accuracy of the AI-managed responses is also taken into account. The range of values ​​to be calculated.

[0050] Artificial intelligence calculates the matching degree of the dialogue in real time; this metric is a call quality score. A core component of this parameter, with a value range of [0,1], it will be incorporated into the calculation system for post-call conversion potential assessment. If the script matching degree is lower than 0.6, the system will automatically optimize the recommendation strategy, directly improving... value.

[0051] In the above technical solution: Real-time call data is used to push tailored sales scripts, solving the problems of untimely sales responses and low script matching accuracy, thus improving communication precision. When customers raise objections, the system automatically matches relevant materials to provide strong supporting evidence for sales, enhancing customer trust in the product. The AI-powered automated function frees up sales staff's time, enabling them to handle scenarios with multiple customers simultaneously, and allowing sales staff to take over conversations at any time, ensuring continuity of communication. This module effectively improves communication efficiency during calls, making sales responses more professional and timely, and steadily pushing communication towards a sale.

[0052] Intelligent Customer Objection Handling Module: Intelligently handles customer objections and optimizes call quality scoring in both directions. Customer emotional positivity .

[0053] The system has a built-in library of common objection handling scripts, covering types such as price objections, effectiveness objections, and trust objections. Artificial intelligence automatically recommends the optimal handling solution based on the objection type, and the timeliness of objection handling is a key optimization factor. Key factors in value.

[0054] When encountering objections not covered by the existing script library, AI utilizes a comprehensive professional database to generate personalized response scripts in real time, which are then synchronized to a frequently used response library to enrich the script reserves for future use. Accumulate data to improve value.

[0055] The system records the entire objection handling process and automatically analyzes the handling effect: successful handling improves customer's positive emotions. (The improvement ranges from 0.1 to 0.2), and call quality scores are also given. Improve by 0.1-0.2; if processing fails, indicate the reason in the sales record to provide a reference for future optimization and avoid similar issues. and value.

[0056] This module achieves this through proactive objection handling. Value and Optimizing values ​​in sync lays the foundation for enhancing the potential for final sales conversion.

[0057] The above technical solution includes: a built-in rich library of objection handling scripts to help sales efficiently resolve customer concerns about price, effectiveness, etc., increasing the success rate of objection handling. For objections not covered by the script library, the system can generate personalized solutions in real time, continuously enriching the script reserves and adapting to more diverse objection scenarios. Recording and analyzing the effectiveness of objection handling provides a reference for subsequent script optimization, driving continuous improvement in objection handling capabilities. This module enhances sales' ability to handle customer objections, reduces customer churn due to improper objection handling, increases customer acceptance of the product, and removes obstacles to closing the deal.

[0058] Post-call debriefing strategy generation module: Automatically completes call debriefing, calculates demand matching degree F, outputs post-call conversion potential assessment results and generates follow-up strategies.

[0059] After the call ends, the system automatically uploads the recording and completes the transcription. The transcript is displayed in the speaker's text content format [HH:MM:SS-HH:MM:SS]. Key information such as customer needs and objections is automatically highlighted to provide data support for the calculation of the F value.

[0060] AI identifies all stages of the call process, including icebreaking, need confirmation, and product introduction, generates a call summary, and outputs sales script evaluations in the form of Lark comments. The evaluation value is incorporated into the final calculation of the Q value.

[0061] The system generates a recording analysis report containing information such as call duration, effective duration, and Q value. At the same time, artificial intelligence analyzes the fit between customer needs and product functions and calculates the required cooperation range [0,1]. This parameter, along with E, participates in the post-call conversion potential assessment. The system calculates the sales conversion potential C using the formula: C = α × E + β × Q + γ × F; The default weights for α, β, and γ are 0.4, 0.3, and 0.3, respectively. Follow-up strategies are generated based on the c-value: high-potential clients are followed up within 1-2 days, medium-potential clients within 3-5 days, and low-potential clients within 7 days. Sales staff can click to create a schedule; the system will provide a reminder 1 minute in advance by default. The above technical solution eliminates the tedious process of manual review, automatically organizing the core content of calls through artificial intelligence and objectively evaluating the effectiveness of sales scripts, making the review process more efficient and comprehensive. The generated targeted follow-up strategies help sales clarify the direction of subsequent work and avoid blindly carrying out follow-up work. The schedule reminder function ensures that sales do not miss important follow-up tasks, guaranteeing the timeliness of follow-up work. At the same time, the review results provide data support for sales to optimize communication strategies, driving continuous improvement in sales capabilities. This module forms a closed-loop management of sales calls, improving the efficiency of review and follow-up, and helping the team increase the conversion rate of sales.

[0062] In this embodiment, the collaborative operation of the system's independent modules constructs an intelligent auxiliary closed loop throughout the entire sales call cycle, realizing intelligent integration of customer information, precise assistance in real-time communication, and automated processing of call debriefing, thereby improving sales communication efficiency and conversion rates. The system's hierarchical permission management function meets the different needs of sales personnel, supervisors, and super administrators. Sales personnel can view their own recordings and customer data, supervisors can view all data of their team, and super administrators can view all data within the system, facilitating team management and performance evaluation. The system's script library and material library can be continuously iterated and optimized. With increased usage, the accuracy of AI recommendations continues to improve, forming a virtuous cycle of self-evolution. The system's sentiment analysis and objection handling functions effectively enhance the customer communication experience and reduce customer churn.

[0063] Although embodiments of the invention have been shown and described, the scope of the invention will be defined by the appended claims and their equivalents by those skilled in the art.

Claims

1. An intelligent assistance system for the entire sales call lifecycle based on artificial intelligence, characterized in that, include: Intelligent Customer Profile Construction Module: Collects and integrates customer information from all dimensions, calculates the completeness of the customer profile (D), and provides parameters for pre-call preparation assessment; Customer Value Segmentation Management Module: Based on the completeness of customer profiles (D), it enables precise segmentation of customer value and optimizes the application scenarios of pre-call preparation assessment parameters; Intelligent pre-generation module for dialogue strategies: Generates personalized dialogue based on customer tags, calculates the dialogue strategy matching degree T, and improves the parameters for pre-call preparation assessment; Intelligent material matching and preparation module: Matches materials with customer needs and sales scripts, calculates material matching degree M, and outputs the final result of pre-call preparation assessment; Real-time intelligent call sensing module: Monitors call status in real time and calculates customer emotional state. This provides parameters for assessing conversion potential after a call; Dynamic script recommendation module: Recommends scripts and materials based on real-time data to improve call quality score (Q) and refine parameters for evaluating post-call conversion potential. Intelligent Customer Objection Handling Module: Intelligently handles customer objections, optimizing both call quality score (Q) and customer emotional positivity (E). Post-call debriefing strategy generation module: Automatically completes call debriefing, calculates demand matching degree F, outputs post-call conversion potential assessment results and generates follow-up strategies.

2. The intelligent assistance system for the entire sales call cycle based on artificial intelligence according to claim 1, characterized in that, The intelligent customer profile construction module specifically includes the following: Automatically collect and integrate comprehensive customer information, calculate customer profile completeness (D), and provide parameters for pre-call preparation assessment. The system automatically collects basic customer information, uses artificial intelligence to analyze customers' historical call records and chat content, and automatically extracts symptoms, communication preferences, purchased services, and service progress information, which are then populated into the corresponding fields in the customer profile. Sales personnel can double-click on a field to customize its editing, and click on a blank area or press Enter to save. The system synchronously generates customer tags, including consumption intention, decision type, and loyalty. Customer files are categorized horizontally according to customer attributes, decision-making factors, and consumption characteristics; After this module is completed, the system calculates the ratio of the number of filled fields to the total number of fields, and obtains the customer profile completeness D. The higher the D value, the more comprehensive the customer information.

3. The intelligent assistance system for the entire sales call cycle based on artificial intelligence according to claim 2, characterized in that, The customer value tiered management module specifically includes the following: The system combines the completeness of customer profiles... Customers are categorized into three value levels—A, B, and C—based on their total spending, historical call count, and purchase intent data. The higher the customer's value, the more accurate the value segmentation results. Category A customers: High total spending, strong intention to make a purchase. Value 20.8; Category B customers: those with spending history but moderate interest. Category C customers: New customers or those with low intent. ; The system supports batch import of customer information tables, and sales staff can perform batch management operations on customers, with the management status automatically marked in the customer list; The customer list displays search results, profile picture, name, last call time, and value-added information.

4. The intelligent assistance system for the entire sales call cycle based on artificial intelligence according to claim 3, characterized in that, The intelligent pre-generation module for communication strategies includes the following: Artificial intelligence matches the optimal communication strategy based on customer tags; the system has a built-in library of commonly used responses, covering basic greetings and conversation endings; sales staff can modify, delete, and add responses through the "Management" function, with new responses automatically appearing at the end of the list; and artificial intelligence calculates the matching degree of the communication strategy. The calculation logic is based on the overlap between the pre-generated script and customer tags and historical success cases, with a value range of [0,1]. This parameter will be incorporated into the calculation system for pre-call preparation assessment; if If the value is below 0.7, the system will automatically prompt the salesperson to adjust the direction of their sales pitch.

5. The intelligent assistance system for the entire sales call cycle based on artificial intelligence according to claim 4, characterized in that, The intelligent material matching and preparation module specifically includes the following: Artificial intelligence automatically matches relevant materials, including product manuals, double-blind trial reports, and health questionnaires, based on customer needs and pre-generated scripts; the accuracy of material matching directly affects… value; Sales staff can upload custom materials. Newly uploaded materials are automatically sorted at the top of the list, and can be renamed, deleted, or dragged to adjust the order. Artificial intelligence computing science matching The calculation logic is that the recommended materials and customer needs are within the range of [0,1]. This parameter will be used together with DT in the Tonghua pre-assessment. All matching materials are synchronized to the call support module, allowing sales staff to send them to customers with a single click. After this module completes, the system calculates the customer's readiness level using a formula. ; ; in , , As weight, if If it is determined to be ready; The system automatically prompts sales staff to supplement customer information or optimize sales scripts and materials.

6. The intelligent assistance system for the entire sales call cycle based on artificial intelligence according to claim 5, characterized in that, The real-time intelligent call sensing module specifically includes the following: After a salesperson initiates a call, the system automatically opens a floating window to display the call duration, conversation stage, and customer emotional state in real time. The calculation of values ​​provides a real-time data source; In each round of dialogue, the artificial intelligence identifies the customer's concerns, symptoms, motivations, and intentions, generates a 3-5 word tag, and updates the tag content in real time. The system analyzes customer emotions every 5 rounds of dialogue, with different emotions corresponding to different colors. The percentage of each emotion type is... The basis for value calculation: The system calculates the customer's emotional positivity based on the proportion of positive emotions and the stability of emotional fluctuations. The value range is [0,1]. This parameter will be included in the calculation system for post-call conversion potential assessment; if The system automatically prompts sales staff to adjust their communication strategies to avoid... The value decreased further.

7. The intelligent assistance system for the entire sales call cycle based on artificial intelligence according to claim 6, characterized in that, The specific details of the dynamic script recommendation module are as follows: Artificial intelligence combines real-time dialogue content and customer emotional state. Customer tags are used to generate targeted recommendation replies. Salespeople can simply click to fill the message into the chat box and send it. The relevance of the message directly impacts the salesperson's response. value; When a customer raises an objection, the system automatically matches relevant materials and recommends double-blind trial reports, which sales personnel can send with a single click, improving the timing and accuracy of material delivery. value; The system supports AI-powered automated response functionality, allowing sales staff to delegate customer requests to AI for automatic replies. The automated response status is automatically marked, and sales staff can take over the conversation at any time. The accuracy of the AI-managed responses is also taken into account. The range of values ​​to be calculated; Artificial intelligence calculates the speech matching degree in real time, with a value ranging from [0,1]. This parameter will be incorporated into the calculation system for post-call conversion potential assessment. If the speech matching degree is lower than 0.6, the system will automatically optimize the recommendation strategy and directly improve the conversion rate. value.

8. The intelligent assistance system for the entire sales call cycle based on artificial intelligence according to claim 7, characterized in that, The intelligent customer objection handling module specifically includes the following: The system has a built-in library of common objection handling scripts, covering price objections, effectiveness objections, and trust objections. Artificial intelligence automatically recommends the optimal handling solution based on the objection type. Timeliness of objection handling is optimized. Key factors in value; when encountering objections not covered by the script library, artificial intelligence generates personalized handling scripts in real time and synchronizes them to the frequently used response library; The system records the entire objection handling process and automatically analyzes the handling effect: successful handling improves customer's positive emotions. And score the call quality. Increase by 0.1-0.2; if processing fails, indicate the reason for the sales record failure.

9. The intelligent assistance system for the entire sales call cycle based on artificial intelligence according to claim 8, characterized in that, The specific details of the debriefing strategy generation module are as follows: After the call ends, the system automatically uploads the recording and completes the transcription; AI identifies all stages of a call, including icebreaking, need confirmation, product introduction, generating a call summary, and outputting sales script evaluations in the form of Lark comments. The evaluation value is incorporated into the final calculation of the Q value. The system generates a recording analysis report that includes call duration, effective duration, and Q-value information. At the same time, artificial intelligence analyzes the fit between customer needs and product functions and calculates the required cooperation degree within the range of [0,1]. This parameter, along with E, participates in the post-call conversion potential assessment. The system calculates the order conversion potential C using a formula; C = α × E + β × Q + γ × F; The default values ​​for α, β, and γ weights are 0.4, 0.3, and 0.3, respectively. Follow-up strategies are generated based on the c value: follow up with high-potential customers within 1-2 days, follow up with medium-potential customers within 3-5 days, and follow up with low-potential customers within 7 days.

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