AI-driven customer condition dynamic portraying and intelligent reaching system and AI-driven customer condition dynamic portraying and intelligent reaching method

Abstract

The invention discloses a customer condition dynamic portraying and intelligent reaching system and method based on AI driving, and relates to the technical field of customer condition management. Customer service historical data are stored, the transaction rate and number of various products are analyzed, the average transaction rate and number are counted, the historical data of various products of customers are collected, and an interest transfer period is set. Analyzing an interest coefficient of a customer on a product; counting a product transaction probability in a customer cycle; analyzing a chain prediction coefficient of the customer; analyzing a comprehensive intention coefficient by combining the interest coefficient and the chain prediction coefficient; the average value of the customer matching degree is calculated, the customer service staff is notified to receive from high to low until the customer service staff accepts the customer service staff, and when the customer reselects the customer service staff, the demand for the transaction rate when the matching degree is analyzed is adjusted, and by establishing a flexible service adjustment mechanism, the whole service process is more adaptive.

Description

Technical Field

[0001] The present invention relates to the technical field of customer relationship management, and specifically to an AI-driven customer relationship dynamic profiling and intelligent contact system and method. Background Art

[0002] Traditional customer relationship management and customer service allocation technologies suffer from numerous pain points that hinder service efficiency and customer experience. Previous customer service allocation mechanisms often relied on manual experience or random assignments, lacking systematic analysis of historical customer service data. This made it difficult to accurately assess each agent's service capabilities and transaction performance across different product categories. This blind allocation approach often resulted in a mismatch between agent expertise and customer needs, wasting service resources and hindering the improvement of transaction conversion rates, thus failing to meet the demands of refined operations. Traditional customer demand analysis primarily focuses on current preferences, lacking dynamic monitoring of customer interest shifts and in-depth analysis of historical transaction patterns. Failing to capture trends in customer demand across product categories, recommendations often remain static and fail to proactively predict potential demand. This limitation results in a poor fit between recommended products and actual customer needs, leading to low customer acceptance and negative customer retention. Traditional customer service systems lack the flexibility to respond to dynamic changes in customer service needs. When customers request personalized requests, such as changing agent, the system struggles to quickly optimize the matching solution and often relies on legacy allocation logic, resulting in persistent service mismatches. This rigid approach not only easily leads to customer dissatisfaction, but may also cause customer churn. It is difficult to adapt to modern customers' demands for personalized and immediate service experience. A more adaptive dynamic adjustment mechanism is urgently needed to optimize service processes. Summary of the Invention

[0003] The purpose of the present invention is to provide an AI-driven customer dynamic profiling and intelligent contact system and method to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solution: an AI-driven customer dynamic profiling and intelligent contact method, comprising the following steps:

[0005] S1. Store customer service history data, analyze the transaction rate and quantity of various products, and calculate the average transaction rate and quantity;

[0006] S2. Collect historical data on various products from customers, set interest transfer cycles, and analyze customer interest coefficients for products;

[0007] S3. Calculate the probability of product transaction within the customer cycle and analyze the customer's chain prediction coefficient;

[0008] S4. Analyze the comprehensive intention coefficient by combining the interest coefficient and the chain prediction coefficient, and select the priority recommended products;

[0009] S5. Collect the customer's transaction probability for the recommended products and analyze the matching degree between customer service and customers;

[0010] S6. Calculate the average customer matching degree and notify the customer service reception from high to low until a customer service representative accepts it. When the customer reselects a customer service representative, adjust the requirements for the transaction rate when analyzing the matching degree.

[0011] Furthermore, in step S1, after authorization, the historical data of the customer service personnel is stored in the database, and the status of the customer service personnel is queried. The idle customer service personnel include {A1, A2, ..., A x ,…,A X}, where X represents the number of idle customer service staff, A x Indicates the xth idle customer service staff, for customer service staff A x Perform analysis, taking the current time point as the end point, query the historical data of customer service personnel for T monitoring time periods, the monitoring time period with the current time point as the end point is the Tth monitoring time period, where T is the number of established monitoring time periods, analyze the tth monitoring time period, t=1,2,…,T, there are Y product categories, in the tth monitoring time period, customer service personnel A x The transaction rate for the yth category product is C x_y , the transaction quantity of the yth product is D x_y Customer Service Staff A x The average transaction rate for Y products is C x , the average transaction quantity for Y products is D x , and then substitute x=1,2,…,X one by one to get the transaction rate of X customer service staff for the yth product {C 1_y ,C 2_y ,…,C x_y ,…,C X_y}, the number of transactions for product category y by X customer service staff is {D 1_y ,D 2_y ,…,D x_y ,…,D X_y}, the average transaction rate of X customer service staff for Y products is {C1, C2, ..., C x ,…,C X}, the average number of transactions for Y products by X customer service staff is {D1,D2,…,D x ,…,D XBy authorizing the storage of historical customer service data and querying its status, you can fully grasp the available customer service resources. By conducting in-depth analysis of the transaction status of each customer service representative for various products in different time periods, you can accurately grasp the capabilities, characteristics, and performance levels of each customer service representative in different product services. Based on these analysis results, you can scientifically match product categories with customer service expertise to improve the pertinence and effectiveness of services, thereby increasing product transaction rates. At the same time, by calculating the average transaction rate and quantity, it provides an objective basis for evaluating customer service work, helping management optimize staffing and resource scheduling, enhancing the overall efficiency of the customer service team, providing data support and decision-making reference for business development, and promoting service quality improvement.

[0012] Furthermore, in step S2, after authorization, historical data of the nth customer on category Y products is collected, and an interest transfer period is set. An interest transfer period ending at the current time is set as the Ith interest transfer period, where I is the number of set interest transfer periods. The i-th interest transfer period is analyzed, i=x=1,2,…,I. In the i-th interest transfer period, the customer's transaction volume for category Y products is {E1,E2,…,E y ,…,E α ,…,E Y}, where E y represents the customer's transaction volume for the yth category of products, E α represents the customer's transaction volume for the αth category of products, and then obtains the interest coefficient w for the yth category of products when the customer applies for customer service y :

[0013] ;

[0014] By authorizing the collection of historical customer data on various products and analyzing the interest transfer cycle, we can accurately capture customers' interest preferences and changing trends for different products at different stages. This in-depth analysis of customer interest dynamics can help companies clearly understand the direction of customer demand. When customers apply for customer service, we can accurately match corresponding products and services based on their interest coefficients, thereby improving the pertinence and fit of services. At the same time, analysis based on the interest cycle can provide a scientific basis for companies to formulate product recommendation strategies and optimize resource allocation, making services more in line with customers' real needs, enhancing customer recognition and satisfaction with the services, and thus promoting the improvement of customers' transaction intentions, helping companies achieve more accurate customer service and more efficient business conversion.

[0015] Furthermore, in step S3, when a customer makes a deal for a product of category α in one interest transfer cycle, the probability of making a deal for a product of category y in the next interest transfer cycle is calculated as F α_y, one by one into α = 1, 2, ..., Y, the statistical results show that when a customer makes a deal for a product of category Y in one interest transfer cycle, the probability of making a deal for a product of category y in the next interest transfer cycle is {F 1_y ,F 2_y ,…,F α_y ,…,F Y_y}, count the customer's transaction status for category Y products in the Tth interest transfer cycle, and calculate the chain prediction coefficient W of the customer for category y products y :

[0016] ;

[0017] where f α_y is the transaction prediction coefficient for the y-th product in the next interest transfer cycle obtained based on the customer's transaction situation for the α-th product in one interest transfer cycle. When the customer completes the transaction for the α-th product in one interest transfer cycle, the transaction prediction coefficient for the y-th product in the next interest transfer cycle is f. α_y =F α_y Otherwise, f α_y =0; By calculating the probability of transactions associated with different products during a customer's interest shift cycle, we can deeply explore the continuity and underlying trends of customer consumption behavior. This chain analysis of transaction probabilities can accurately predict a customer's likelihood of purchasing various products in subsequent cycles, allowing companies to clearly understand the dynamic evolution of customer demand. This allows companies to proactively formulate response strategies to potential customer interest shifts and provide more proactive product recommendations during the customer service process, shifting service from reactive response to proactive prediction. This analysis also provides data support for companies to optimize resource allocation and plan product and service direction, helping them more accurately meet potential customer needs, improve the predictability and effectiveness of customer service, and enhance customer reliance and loyalty, thereby creating more transaction opportunities and driving sustainable business growth.

[0018] Furthermore, in step S4, according to the customer's interest coefficient w for the yth category product y and the chain prediction coefficient W for the yth product y Calculate the customer's comprehensive intention coefficient M for product type y y , the comprehensive intention coefficient M y By the interest coefficient w y and comprehensive intention coefficient M y Obtain weighted, substitute y=1,2,…,Y one by one, and obtain the comprehensive intention coefficient of customers for Y products {M1,M2,…,M y ,…,M Y}, select the beta products with the highest comprehensive intention coefficient as the priority recommended products to customers; by calculating the comprehensive intention coefficient based on the customer's interest coefficient in the product and the chain prediction coefficient, it is possible to deeply analyze the customer's true intention from the dual dimensions of static interest preferences and dynamic demand evolution. This evaluation method that integrates multi-dimensional data can accurately capture the changing trajectory of customer demand and enable enterprises to break free from the limitations of single-dimensional analysis. Screening priority recommended products based on the comprehensive intention coefficient can achieve a full-chain service upgrade from customer interest insights to demand predictions, so that the recommendation service is both in line with current preferences and forward-looking for future needs. This can not only improve the accuracy and foresight of customer service, enhance customer acceptance and satisfaction with recommended products, but also help enterprises optimize resource allocation, improve product recommendation efficiency, create more transaction opportunities, and promote the upgrade of service models to intelligent and precise directions, so as to achieve efficient matching of customer needs and enterprise services.

[0019] Furthermore, in step S5, when a customer applies for customer service, after authorization, it is monitored that the customer's β-category priority recommended products include the y-th category product, and the transaction probability H of the n-th customer for the y-th product is collected. n_y , one by one into n = 1, 2, ..., N, and then get the transaction probability of N customers for the yth product {H 1_y ,H 2_y ,…,H n_y ,…,H N_y}, where the maximum transaction rate is H max_y , and then calculate the matching degree J of the x-th customer service to the n-th customer regarding product y x_y :

[0020] ;

[0021] Among them, k1 is the influence weight of transaction rate on matching degree, k1= , k2 is the weight of the impact of trading volume on matching degree, k2= By obtaining customers' preferred products and collecting transaction probabilities, we can accurately grasp customers' preferences for specific products. Based on the transaction probability data of different customers, combined with the weight of the impact of transaction rate and transaction volume on matching, we can scientifically calculate the degree of adaptation between customer service and customers in specific product services. This matching mechanism can achieve the precise connection between customer service resources and customer needs, allowing customer service staff with corresponding product and service advantages to provide services to customers with high transaction intentions, thereby improving the pertinence and effectiveness of services. At the same time, this method provides a scientific basis for enterprises to optimize the allocation of customer service resources, help improve customer service efficiency, enhance customer recognition of services, thereby increasing the probability of product transactions and promoting the improvement of enterprise service quality.

[0022] Furthermore, in step S6, the average matching degree of the X customer service representatives to the nth customer regarding the β-category product is calculated, and customer reception notifications are issued to the customer service representatives in descending order of the matching degree averages until a customer service representative accepts the customer reception notification.

[0023] If a customer requests a change of agent during customer service, the system increases the weighting of the match between the closing rate and the nth customer. The system then re-analyzes the average match between X agents and the nth customer for β-products. Customer service notifications are then sent to agents in descending order of the average match until a single agent accepts the notification. By calculating the average match between agents and customers and sorting notifications by highest and lowest, this system accurately aligns customer service resources with customer needs, ensuring that highly compatible agents receive priority service and improving the efficiency and quality of initial service matching. When a customer requests a change of agent, the system automatically increases the weighting of the closing rate and re-analyzes the ranking. This dynamic adjustment mechanism optimizes matching logic based on actual service feedback, further aligning it with customers' personalized needs and true service expectations. This process not only improves the relevance of customer service and customer satisfaction, but also reduces service interruptions by continuously optimizing matching strategies, strengthening customer trust and reliance on the service. It also helps companies efficiently allocate customer service resources, reduce resource waste, improve overall service efficiency and product close probability, and foster a more intelligent and flexible customer service system.

[0024] An AI-driven customer dynamic profiling and intelligent engagement system, comprising: a customer service data storage module, a customer interest coefficient analysis module, a transaction probability chain analysis module, a comprehensive intention coefficient analysis module, a matching degree analysis module, and a customer service order management module;

[0025] The customer service data storage module is used to store customer service historical data, analyze the transaction rate and quantity of various products, and calculate the average transaction rate and quantity;

[0026] The customer interest coefficient analysis module is used to collect historical data of various products of customers, set interest transfer cycles, and analyze customer interest coefficients for products;

[0027] The transaction probability chain analysis module is used to calculate the probability of product transaction within the customer cycle and analyze the customer's chain prediction coefficient;

[0028] The comprehensive intention coefficient analysis module is used to analyze the comprehensive intention coefficient by combining the interest coefficient and the chain prediction coefficient, and select the priority recommended products;

[0029] The matching analysis module is used to collect the customer's transaction probability for the priority recommended products and analyze the matching degree between customer service and customers for the products;

[0030] The customer service order management module is used to calculate the average value of customer matching, and notify the customer service reception from high to low until a customer service representative accepts it. When the customer reselects customer service, the demand for transaction rate when analyzing matching is adjusted.

[0031] Compared with existing technologies, this invention achieves the following beneficial effects: First, by systematically analyzing customer service personnel's historical transaction data, it accurately assesses each agent's service capabilities and transaction performance across different product categories. This, combined with customer product preferences and transaction probability, builds a scientific matching assessment model, precisely matching customer service with customer needs, significantly improving service efficiency and transaction conversion rates. This data-driven allocation mechanism avoids the blindness of traditional random allocation and enables more rational utilization of service resources.

[0032] On the one hand, dynamic monitoring of customer interest shift cycles and chain reaction forecasting analysis can capture the shifting patterns of customer demand across different product categories. This allows us to predict potential demand based not only on current interest but also on the chain reaction of historical transactions, forming a comprehensive interest coefficient to recommend prioritized products. This forward-looking demand prediction ensures that recommendations are more aligned with actual customer needs, enhancing customer acceptance of recommended products and ultimately improving customer retention.

[0033] Furthermore, a flexible service adjustment mechanism has been established. When a customer requests a change of customer service representative during a customer service call, the system automatically increases the weight of the closing rate on the matching degree and re-optimizes the customer service matching plan. This dynamic response mechanism promptly addresses personalized customer service needs, preventing customer churn due to service mismatches. Furthermore, weight adjustments further optimize the matching logic, making the entire service process more adaptable and user-friendly, and continuously improving customer satisfaction and service quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0035] Figure 1 This is a structural diagram of an AI-driven customer dynamic profiling and intelligent contact system of the present invention;

[0036] Figure 2 This is a flow chart of an AI-driven customer dynamic profiling and intelligent contact method of the present invention. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0038] See also Figure 1 and Figure 2 The present invention provides a technical solution: an AI-driven customer dynamic profiling and intelligent contact method, comprising the following steps:

[0039] S1. Store customer service history data, analyze the transaction rate and quantity of various products, and calculate the average transaction rate and quantity;

[0040] S2. Collect historical data on various products from customers, set interest transfer cycles, and analyze customer interest coefficients for products;

[0041] S3. Calculate the probability of product transaction within the customer cycle and analyze the customer's chain prediction coefficient;

[0042] S4. Analyze the comprehensive intention coefficient by combining the interest coefficient and the chain prediction coefficient, and select the priority recommended products;

[0043] S5. Collect the customer's transaction probability for the recommended products and analyze the matching degree between customer service and customers;

[0044] S6, calculate the average value of customer matching, notify the customer service reception from high to low, until a customer service accepts, and when the customer reselects the customer service, adjust the demand for the transaction rate when analyzing the matching. In step S1, after authorization, the historical data of the customer service staff is stored in the database, and the status of the customer service staff is queried. The idle customer service staff include {A1, A2, ..., A x ,…,A X}, where X represents the number of idle customer service staff, A x Indicates the xth idle customer service staff, for customer service staff A x Perform analysis, taking the current time point as the end point, query the historical data of customer service personnel for T monitoring time periods, the monitoring time period with the current time point as the end point is the Tth monitoring time period, where T is the number of established monitoring time periods, analyze the tth monitoring time period, t=1,2,…,T, there are Y product categories, in the tth monitoring time period, customer service personnel A x The transaction rate for the yth category product is C x_y , the transaction quantity of the yth product is D x_y Customer Service Staff A x The average transaction rate for Y products is Cx , the average transaction quantity for Y products is D x , and then substitute x=1,2,…,X one by one to get the transaction rate of X customer service staff for the yth product {C 1_y ,C 2_y ,…,C x_y ,…,C X_y}, the number of transactions for product category y by X customer service staff is {D 1_y ,D 2_y ,…,D x_y ,…,D X_y}, the average transaction rate of X customer service staff for Y products is {C1, C2, ..., C x ,…,C X}, the average number of transactions for Y products by X customer service staff is {D1,D2,…,D x ,…,D X By authorizing the storage of historical customer service data and querying its status, you can fully grasp the available customer service resources. By conducting in-depth analysis of the transaction status of each customer service representative for various products in different time periods, you can accurately grasp the capabilities, characteristics, and performance levels of each customer service representative in different product services. Based on these analysis results, you can scientifically match product categories with customer service expertise to improve the pertinence and effectiveness of services, thereby increasing product transaction rates. At the same time, by calculating the average transaction rate and quantity, it provides an objective basis for evaluating customer service work, helping management optimize staffing and resource scheduling, enhancing the overall efficiency of the customer service team, providing data support and decision-making reference for business development, and promoting service quality improvement.

[0045] In step S2, after authorization, the historical data of the nth customer on the Y-type product is collected, and the interest transfer period is set. The interest transfer period ending at the current time is set as the I-th interest transfer period, I is the number of the set interest transfer periods, and the i-th interest transfer period is analyzed, i=x=1,2,…,I. In the i-th interest transfer period, the customer's transaction volume for the Y-type product is {E1,E2,…,E y ,…,E α ,…,E Y}, where E y represents the customer's transaction volume for the yth category of products, E α represents the customer's transaction volume for the αth category of products, and then obtains the interest coefficient w for the yth category of products when the customer applies for customer service y :

[0046] ;

[0047] By authorizing the collection of historical customer data on various products and analyzing the interest transfer cycle, we can accurately capture customers' interest preferences and changing trends for different products at different stages. This in-depth analysis of customer interest dynamics can help companies clearly understand the direction of customer demand. When customers apply for customer service, we can accurately match corresponding products and services based on their interest coefficients, thereby improving the pertinence and fit of services. At the same time, analysis based on the interest cycle can provide a scientific basis for companies to formulate product recommendation strategies and optimize resource allocation, making services more in line with customers' real needs, enhancing customer recognition and satisfaction with the services, and thus promoting the improvement of customers' transaction intentions, helping companies achieve more accurate customer service and more efficient business conversion.

[0048] In step S3, when a customer makes a deal for a product of category α in one interest transfer cycle, the probability that the customer will make a deal for a product of category y in the next interest transfer cycle is F α_y , one by one into α = 1, 2, ..., Y, the statistical results show that when a customer makes a deal for a product of category Y in one interest transfer cycle, the probability of making a deal for a product of category y in the next interest transfer cycle is {F 1_y ,F 2_y ,…,F α_y ,…,F Y_y}, count the customer's transaction status for category Y products in the Tth interest transfer cycle, and calculate the chain prediction coefficient W of the customer for category y products y :

[0049] ;

[0050] where f α_y is the transaction prediction coefficient for the y-th product in the next interest transfer cycle obtained based on the customer's transaction situation for the α-th product in one interest transfer cycle. When the customer completes the transaction for the α-th product in one interest transfer cycle, the transaction prediction coefficient for the y-th product in the next interest transfer cycle is f. α_y =F α_y Otherwise, f α_y=0; By calculating the probability of transactions associated with different products during a customer's interest shift cycle, we can deeply explore the continuity and underlying trends of customer consumption behavior. This chain analysis of transaction probabilities can accurately predict a customer's likelihood of purchasing various products in subsequent cycles, allowing companies to clearly understand the dynamic evolution of customer demand. This allows companies to proactively formulate response strategies to potential customer interest shifts and provide more proactive product recommendations during the customer service process, shifting service from reactive response to proactive prediction. This analysis also provides data support for companies to optimize resource allocation and plan product and service direction, helping them more accurately meet potential customer needs, improve the predictability and effectiveness of customer service, and enhance customer reliance and loyalty, thereby creating more transaction opportunities and driving sustainable business growth.

[0051] In step S4, according to the customer's interest coefficient w for the yth category product y and the chain prediction coefficient W for the yth product y Calculate the customer's comprehensive intention coefficient M for product type y y , the comprehensive intention coefficient M y By the interest coefficient w y and comprehensive intention coefficient M y Obtain weighted, substitute y=1,2,…,Y one by one, and obtain the comprehensive intention coefficient of customers for Y products {M1,M2,…,M y ,…,M Y}, select the beta products with the highest comprehensive intention coefficient as the priority recommended products to customers; by calculating the comprehensive intention coefficient based on the customer's interest coefficient in the product and the chain prediction coefficient, it is possible to deeply analyze the customer's true intention from the dual dimensions of static interest preferences and dynamic demand evolution. This evaluation method that integrates multi-dimensional data can accurately capture the changing trajectory of customer demand and enable enterprises to break free from the limitations of single-dimensional analysis. Screening priority recommended products based on the comprehensive intention coefficient can achieve a full-chain service upgrade from customer interest insights to demand predictions, so that the recommendation service is both in line with current preferences and forward-looking for future needs. This can not only improve the accuracy and foresight of customer service, enhance customer acceptance and satisfaction with recommended products, but also help enterprises optimize resource allocation, improve product recommendation efficiency, create more transaction opportunities, and promote the upgrade of service models to intelligent and precise directions, so as to achieve efficient matching of customer needs and enterprise services.

[0052] In step S5, when a customer applies for customer service, after authorization, it is monitored that the customer's β-category priority recommended products include the y-th category product, and the transaction probability H of the n-th customer for the y-th product is collected. n_y , one by one into n = 1, 2, ..., N, and then get the transaction probability of N customers for the yth product {H 1_y ,H 2_y ,…,Hn_y ,…,H N_y}, where the maximum transaction rate is H max_y , and then calculate the matching degree J of the x-th customer service to the n-th customer regarding product y x_y :

[0053] ;

[0054] Among them, k1 is the influence weight of transaction rate on matching degree, k1= , k2 is the weight of the impact of trading volume on matching degree, k2= By obtaining customers' preferred products and collecting transaction probabilities, we can accurately grasp customers' preferences for specific products. Based on the transaction probability data of different customers, combined with the weight of the impact of transaction rate and transaction volume on matching, we can scientifically calculate the degree of adaptation between customer service and customers in specific product services. This matching mechanism can achieve the precise connection between customer service resources and customer needs, allowing customer service staff with corresponding product and service advantages to provide services to customers with high transaction intentions, thereby improving the pertinence and effectiveness of services. At the same time, this method provides a scientific basis for enterprises to optimize the allocation of customer service resources, help improve customer service efficiency, enhance customer recognition of services, thereby increasing the probability of product transactions and promoting the improvement of enterprise service quality.

[0055] In step S6, the average matching degree of the X customer service representatives to the nth customer regarding the β-category product is calculated, and customer reception notifications are issued to the customer service representatives in descending order of matching degree averages until a customer service representative accepts the customer reception notification.

[0056] If a customer requests a change of agent during customer service, the system increases the weighting of the match between the closing rate and the nth customer. The system then re-analyzes the average match between X agents and the nth customer for β-products. Customer service notifications are then sent to agents in descending order of the average match until a single agent accepts the notification. By calculating the average match between agents and customers and sorting notifications by highest and lowest, this system accurately aligns customer service resources with customer needs, ensuring that highly compatible agents receive priority service and improving the efficiency and quality of initial service matching. When a customer requests a change of agent, the system automatically increases the weighting of the closing rate and re-analyzes the ranking. This dynamic adjustment mechanism optimizes matching logic based on actual service feedback, further aligning it with customers' personalized needs and true service expectations. This process not only improves the relevance of customer service and customer satisfaction, but also reduces service interruptions by continuously optimizing matching strategies, strengthening customer trust and reliance on the service. It also helps companies efficiently allocate customer service resources, reduce resource waste, improve overall service efficiency and product close probability, and foster a more intelligent and flexible customer service system.

[0057] An AI-driven customer dynamic profiling and intelligent outreach system, comprising: a customer service data storage module, a customer interest coefficient analysis module, a transaction probability chain analysis module, a comprehensive intention coefficient analysis module, a matching degree analysis module, and a customer service order management module;

[0058] The customer service data storage module is used to store customer service historical data, analyze the transaction rate and quantity of various products, and calculate the average transaction rate and quantity;

[0059] The customer interest coefficient analysis module is used to collect historical data of various products of customers, set interest transfer cycles, and analyze customer interest coefficients for products;

[0060] The transaction probability chain analysis module is used to calculate the probability of product transaction within the customer cycle and analyze the customer's chain prediction coefficient;

[0061] The comprehensive intention coefficient analysis module is used to analyze the comprehensive intention coefficient by combining the interest coefficient and the chain prediction coefficient, and select the priority recommended products;

[0062] The matching analysis module is used to collect the customer's transaction probability for the priority recommended products and analyze the matching degree between customer service and customers for the products;

[0063] The customer service order management module is used to calculate the average value of customer matching, and notify the customer service reception from high to low until a customer service representative accepts it. When the customer reselects customer service, the demand for transaction rate when analyzing matching is adjusted.

[0064] The sorting stability analysis module, task urgency assessment module, operation status determination module, task redistribution module, adjustment effect monitoring module and abnormal overlimit processing module are connected to a cloud disk via a wireless network. The cloud disk receives and stores logs. When the stability of any sorting line is abnormal, an alarm signal is transmitted to the downstream sorting line via blockchain technology. The alarm signal is used to warn of the abnormal stability of the sorting line, and the number of abnormal stability of any sorting line is updated in the log. The blockchain technology prevents the alarm data from being maliciously modified after being uploaded to the chain, ensuring that the information received by the downstream system is authentic and reliable.

[0065] Example 1: After authorization, the customer service system of an e-commerce platform accesses the historical service data of all customer service representatives into a database, including the product categories, inquiry response speed, transaction status, etc. of each customer service representative. When the system detects that a customer service representative is idle, it automatically extracts a list of idle customer service representatives. Taking the home furnishing customer service representative as an example, the system retrieves the service records of an idle customer service representative for the past six months and analyzes their performance in different product categories. The transaction rate and transaction volume data of each period of the customer service representative in multiple monitoring cycles for sofa products are sorted one by one to form a profile of the customer service representative's service capabilities in various product categories. At the same time, the average transaction level of all idle customer service representatives in similar products is compared to provide a data basis for subsequent matching.

[0066] When a customer inquires about sofas, the system simultaneously collects their purchase and browsing history over the past year, setting a quarterly interest shift cycle. Analysis reveals that this customer has frequently browsed high-end, eco-friendly sofas over the past two cycles, and has recently added certain high-priced sofas to their favorites and added to their cart. Based on the customer's actual transaction volume across product categories during each cycle, the system calculates their interest coefficient for the sofa they are currently inquiring about. By determining whether the customer's recent behavior is concentrated in this category, the system quantifies the urgency of their needs and the depth of their preferences.

[0067] The system further calculates the correlations between different product categories in the customer's historical purchases: for example, a customer may purchase an eco-friendly sofa and then, in the next purchase, an eco-friendly carpet from the same brand. By analyzing a large amount of similar behavioral data, the system determines the probability that a customer will switch to another product category after purchasing one. For the current consultation scenario, the system calculates the probability that the customer, after browsing sofas, will subsequently purchase accessories such as carpets and cushions, generating a chain reaction prediction coefficient to determine the customer's potential extended needs.

[0068] The system uses a weighted calculation to generate a comprehensive purchase intention coefficient, combining customer interest in sofas and the chain reaction coefficient for subsequent accessory purchases. The results show that customers have the highest direct purchase intention for high-end, eco-friendly sofas, while also showing a strong potential interest in matching eco-friendly accessories. Based on this, the system prioritizes sofas as a recommended category and includes accessories as a related recommendation option, forming a tiered recommendation strategy.

[0069] Based on the customer's preferred product categories, the system identifies available agents with expertise in those categories. For example, a particular available agent's historical data shows an above-average closing rate in the high-end, eco-friendly sofa category, and they have handled in-depth consultations with similar customers. The system calculates the match between this agent and the current customer, combining the weights of closing rate and transaction volume on matching. The system focuses on whether the agent's experience with similar products closely matches the customer's needs, such as whether they can answer specialized questions about environmentally friendly material certification and long-term maintenance.

[0070] The system sends out notifications to customer service representatives in descending order of match. If the first representative doesn't respond promptly or the customer requests a change during the consultation (for example, if the first representative fails to accurately answer a question about the material), the system automatically increases the weight of "close rate-related factors" and recalculates the match for the remaining representatives. For example, if the second representative demonstrates superior close rate and satisfaction with in-depth consultations for similar products, the system will prioritize their appointment. Based on the customer's historical interest data, this representative proactively provides a material testing report and customized maintenance plan, successfully concluding the transaction and demonstrating the effectiveness of the dynamic weight adjustment mechanism in improving service accuracy.

[0071] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. An AI-driven customer dynamic profiling and intelligent outreach method, characterized by: The method comprises the following steps: S1. Store customer service history data, analyze the transaction rate and quantity of various products, and calculate the average transaction rate and quantity; S2. Collect historical data on various products from customers, set interest transfer cycles, and analyze customer interest coefficients for products; S3. Calculate the probability of product transaction within the customer cycle and analyze the customer's chain prediction coefficient; S4. Analyze the comprehensive intention coefficient by combining the interest coefficient and the chain prediction coefficient, and select the priority recommended products; S5. Collect the customer's transaction probability for the recommended products and analyze the matching degree between customer service and customers; S6. Calculate the average customer matching degree and notify the customer service reception from high to low until a customer service representative accepts it. When the customer reselects a customer service representative, adjust the requirements for the transaction rate when analyzing the matching degree.

2. The AI-driven customer dynamic profiling and intelligent customer engagement method according to claim 1, characterized in that: In step S1, after authorization, the historical data of the customer service personnel is stored in the database, and the status of the customer service personnel is queried. The available customer service personnel include {A1, A2, ..., A x ,…,A X }, where X represents the number of idle customer service staff, A x Indicates the xth idle customer service staff, for customer service staff A x Perform analysis, taking the current time point as the end point, query the historical data of customer service personnel for T monitoring time periods, the monitoring time period with the current time point as the end point is the Tth monitoring time period, where T is the number of established monitoring time periods, analyze the tth monitoring time period, t=1,2,…,T, there are Y product categories, in the tth monitoring time period, customer service personnel A x The transaction rate for the yth category product is C x_y , the transaction quantity of the yth product is D x_y Customer Service Staff A x The average transaction rate for Y products is C x , the average transaction quantity for Y products is D x , and then substitute x=1,2,…,X one by one to get the transaction rate of X customer service staff for the yth product {C 1_y ,C 2_y ,…,C x_y ,…,C X_y }, the number of transactions for product category y by X customer service staff is {D 1_y ,D 2_y ,…,D x_y ,…,D X_y }, the average transaction rate of X customer service staff for Y products is {C1, C2, ..., C x ,…,C X }, the average number of transactions for Y products by X customer service staff is {D1,D2,…,D x ,…,D X }.

3. The AI-driven customer dynamic profiling and intelligent customer engagement method according to claim 2, characterized in that: In step S2, after authorization, the historical data of the nth customer on the Y-type product is collected, and the interest transfer period is set. The interest transfer period ending at the current time is set as the I-th interest transfer period, I is the number of the set interest transfer periods, and the i-th interest transfer period is analyzed, i=x=1,2,…,I. In the i-th interest transfer period, the customer's transaction volume for the Y-type product is {E1,E2,…,E y ,…,E α ,…,E Y }, where E y represents the customer's transaction volume for the yth category of products, E α represents the customer's transaction volume for the αth category of products, and then obtains the interest coefficient w for the yth category of products when the customer applies for customer service y : 。 4. The AI-driven customer dynamic profiling and intelligent customer engagement method according to claim 3, characterized in that: In step S3, when a customer makes a deal for a product of category α in one interest transfer cycle, the probability that the customer will make a deal for a product of category y in the next interest transfer cycle is F α_y , one by one into α = 1, 2, ..., Y, the statistical results show that when a customer makes a deal for a product of category Y in one interest transfer cycle, the probability of making a deal for a product of category y in the next interest transfer cycle is {F 1_y ,F 2_y ,…,F α_y ,…,F Y_y }, count the customer's transaction status for category Y products in the Tth interest transfer cycle, and calculate the chain prediction coefficient W of the customer for category y products y : ； where f α_y is the transaction prediction coefficient for the y-th product in the next interest transfer cycle obtained based on the customer's transaction situation for the α-th product in one interest transfer cycle. When the customer completes the transaction for the α-th product in one interest transfer cycle, the transaction prediction coefficient for the y-th product in the next interest transfer cycle is f. α_y =F α_y Otherwise, f α_y =0.

5. The AI-driven customer dynamic profiling and intelligent customer engagement method according to claim 4, characterized in that: In step S4, according to the customer's interest coefficient w for the yth category product y and the chain prediction coefficient W for the yth product y Calculate the customer's comprehensive intention coefficient M for product type y y , the comprehensive intention coefficient M y By the interest coefficient w y and comprehensive intention coefficient M y Obtain weighted, substitute y=1,2,…,Y one by one, and obtain the comprehensive intention coefficient of customers for Y products {M1,M2,…,M y ,…,M Y }, select the β-product with the highest comprehensive intention coefficient as the priority recommended product to customers.

6. The AI-driven customer dynamic profiling and intelligent customer engagement method according to claim 5, characterized in that: In step S5, when a customer applies for customer service, after authorization, it is monitored that the customer's β-category priority recommended products include the y-th category product, and the transaction probability H of the n-th customer for the y-th product is collected. n_y , one by one into n = 1, 2, ..., N, and then get the transaction probability of N customers for the yth product {H 1_y ,H 2_y ,…,H n_y ,…,H N_y }, where the maximum transaction rate is H max_y , and then calculate the matching degree J of the x-th customer service to the n-th customer regarding product y x_y : ； Among them, k1 is the influence weight of transaction rate on matching degree, k1= , k2 is the weight of the impact of trading volume on matching degree, k2= .

7. The AI-driven customer dynamic profiling and intelligent customer engagement method according to claim 6, characterized in that: In step S6, the average matching degree of the X customer service representatives to the nth customer regarding the β-product is calculated, and customer reception notifications are sent to the customer service representatives in descending order of the matching degree averages until a customer service representative accepts the customer reception notification.

8. The AI-driven customer dynamic profiling and intelligent customer engagement method according to claim 6, characterized in that: If a customer requests a change of customer service representative during customer service, the weight of the impact of the transaction rate on the matching degree will be increased, and the average matching degree of X customer service representatives for the nth customer regarding β products will be re-analyzed. Customer reception notifications will be issued to customer service representatives in descending order of the average matching degree until a customer service representative accepts the customer reception notification.

9. An AI-driven customer dynamic profiling and intelligent outreach system, the system being applied to the AI-driven customer dynamic profiling and intelligent outreach method according to any one of claims 1 to 8, characterized in that: The system includes: a customer service data storage module, a customer interest coefficient analysis module, a transaction probability chain analysis module, a comprehensive intention coefficient analysis module, a matching degree analysis module and a customer service order management module; The customer service data storage module is used to store customer service historical data, analyze the transaction rate and quantity of various products, and calculate the average transaction rate and quantity; The customer interest coefficient analysis module is used to collect historical data of various products of customers, set interest transfer cycles, and analyze customer interest coefficients for products; The transaction probability chain analysis module is used to calculate the probability of product transaction within the customer cycle and analyze the customer's chain prediction coefficient; The comprehensive intention coefficient analysis module is used to analyze the comprehensive intention coefficient by combining the interest coefficient and the chain prediction coefficient, and select the priority recommended products; The matching analysis module is used to collect the customer's transaction probability for the priority recommended products and analyze the matching degree between customer service and customers for the products; The customer service order management module is used to calculate the average value of customer matching, and notify the customer service reception from high to low until a customer service representative accepts it. When the customer reselects customer service, the demand for transaction rate when analyzing matching is adjusted.

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