A wechat enterprise customer loss risk early warning method and system
By employing multi-dimensional feature calculation and event callback mechanisms, the problem of low accuracy and imprecise management in customer churn detection in existing technologies has been solved, enabling real-time, accurate early warning and efficient management of customer churn risk.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HANGZHOU BATONG NETWORK TECHNOLOGY CO LTD
- Filing Date
- 2026-04-09
- Publication Date
- 2026-06-09
AI Technical Summary
Current technologies for customer churn detection rely on a single dimension, resulting in low accuracy. They cannot promptly capture customers' proactive deletion or blocking behavior and lack refined management capabilities, making it impossible to pinpoint responsibility for churn.
By acquiring conversation messages and customer behavior in real time through the WeChat Work interface, multi-dimensional feature scores are calculated, including activity decline rate, response delay growth, customer churn signals, follow-up quality and risk history. An event callback mechanism is used to capture proactive deletion and blocking behaviors, and a hierarchical response mechanism is established.
It enables multi-dimensional dynamic assessment of customer churn risk, improves the accuracy and timeliness of early warnings, reduces the rate of missed and false alarms, and enhances customer recovery and management efficiency.
Smart Images
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Abstract
Description
Technical Field
[0001] This invention relates to the field of session management system technology. Background Technology
[0002] In existing technologies, customer churn detection typically relies on the single dimension of the last session time for judgment, which has the drawbacks of simple judgment logic and low accuracy. Furthermore, the lack of ability to detect customers' proactive deletion or blocking behavior makes it impossible for companies to promptly perceive customers' intentions to churn. At the same time, the risk level assessment is too simplistic and cannot meet the needs of refined management. Furthermore, existing technology does not differentiate between employee follow-up quality and cannot pinpoint the responsibility for employee turnover.
[0003] Therefore, there is an urgent need to develop a method and system for early warning of customer churn risk on WeChat for enterprises to solve the problems in existing technologies. Summary of the Invention
[0004] The purpose of this invention is to provide a method and system for early warning of customer churn risk on WeChat for enterprises, which can achieve real-time and accurate capture of customers' active deletion and blocking behaviors, effectively reducing the false alarm rate and the missed alarm rate. Moreover, it has a simple structure and is easy to use, so as to solve the problems mentioned in the background art.
[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for early warning of customer churn risk on WeChat for businesses includes the following steps: S1: Real-time acquisition of conversation messages, customer behavior events, and employee follow-up status via the WeChat Work interface; S2: Clean and normalize the collected data, and calculate feature scores for five dimensions: activity decline rate, response delay growth, customer churn signal, follow-up quality score, and historical risk accumulation. Among them, the activity decline rate is calculated based on the ratio of the number of messages in the recent period to the number of messages in the historical period; the response delay growth is calculated based on the ratio of the recent average response time to the historical average response time; the customer churn signal is determined based on the customer's active deletion and blocking behavior; the follow-up quality score is calculated based on the ratio of the number of effective communication messages to the total number of messages sent; and the historical risk accumulation is calculated based on the number of risk triggers within a preset time window. S3: Sum the feature scores of each dimension according to preset weights to obtain a comprehensive risk score, and determine the risk level based on the preset interval in which the comprehensive risk score is located. S4: Execute corresponding early warning information push and customer management actions based on the risk level.
[0006] By adopting the above technical solution, a multi-dimensional dynamic assessment method for customer churn risk early warning has been realized. By comprehensively calculating the feature scores of five dimensions, namely activity level, response latency, churn signals, follow-up quality, and historical risks, the method solves the problem of low accuracy of single-dimensional judgment in existing technologies and comprehensively improves the accuracy of churn early warning.
[0007] As a further aspect of the present invention: the session messages in S1 include message quantity, message timestamp, message interval, session duration, and message content type; the message quantity is respectively counted as the number of messages sent by employees to customers and the number of messages sent by customers to employees.
[0008] By adopting the above technical solution and refining the granularity of conversation message collection, accurate statistics on interaction frequency and content type are achieved, providing detailed basic data support for the feature calculation layer and ensuring the accuracy of subsequent interaction trend analysis.
[0009] As a further aspect of the present invention: the employee follow-up status in S1 includes the employee's most recent follow-up time, a summary of the follow-up record content, and a valid communication marker; the valid communication marker is determined by recognizing that the number of characters in the message body is greater than a preset number and contains substantive content.
[0010] By adopting the above technical solution and introducing the judgment logic of effective communication markers, a quantitative assessment of the quality of employee follow-up was achieved, solving the problem of being unable to locate follow-up responsibility and providing data basis for distinguishing the reasons for customer churn.
[0011] As a further aspect of the present invention: the customer churn signal determination step in S2 includes: By receiving notifications of deleted enterprise members through the WeChat Work event callback mechanism, we can directly capture customers' proactive deletion behavior. By periodically checking the message read status, if the read rate is zero and there is no reply after sending a preset number of messages, and combined with the signal of decreasing interaction time, it is determined to be a suspected blocking behavior.
[0012] By adopting the above technical solution, and through a combination of event callbacks and indirect detection, real-time capture of customer-initiated deletion and blocking behaviors is achieved, which solves the problem of the lack of proactive behavior capture capabilities in existing technologies and significantly improves the timeliness of high-risk warnings.
[0013] As a further aspect of the present invention: the calculation logic of the feature score in S3 is as follows: the lower the ratio of activity decline rate, the higher the score; the higher the ratio of response delay growth, the higher the score; customer-initiated deletion behavior is assigned the highest score, suspected blacklisting behavior is assigned the second highest score, and no abnormal behavior is assigned zero score; the lower the follow-up quality ratio, the higher the score; and the more times the historical risk has been accumulated, the higher the score.
[0014] By adopting the above technical solution and setting specific scoring mapping logic for each dimension of characteristic factors, the degree of risk is scientifically quantified, ensuring that the comprehensive risk score can objectively reflect the true likelihood of customer churn.
[0015] As a further aspect of the present invention: the risk levels in S3 include high risk, medium risk and low risk; high risk corresponds to the highest score range and is directly triggered based on customer churn signals, executing an instant push of administrator messages and a customer allocation mechanism; medium risk corresponds to the middle score range, executing timed summary pushes and inclusion in the key attention list; low risk corresponds to the lowest score range, executing record archiving and report statistics.
[0016] By adopting the above technical solutions, and by classifying risks into high, medium and low levels and matching them with differentiated response actions, hierarchical early warning management is achieved, which helps managers focus on core risks and improves management efficiency and the rationality of resource allocation.
[0017] As a further aspect of the present invention: in S2, the weight of the activity decline rate is 25%, the weight of the response delay increase is 20%, the weight of the customer churn signal is 30%, the weight of the follow-up quality score is 15%, and the weight of the historical risk accumulation is 10%. In S3, the comprehensive risk score range corresponding to the high-risk level is 70 to 100 points, the comprehensive risk score range corresponding to the medium-risk level is 40 to 69 points, and the comprehensive risk score range corresponding to the low-risk level is 0 to 39 points.
[0018] By adopting the above technical solution and setting specific weight ratios and scoring interval thresholds, the standardized configuration of the scoring model was achieved, ensuring the uniformity and feasibility of the risk assessment standards.
[0019] This invention also discloses an enterprise WeChat customer churn risk early warning system, comprising: The data acquisition module is used to obtain conversation messages, customer behavior events, and employee follow-up status in real time through the WeChat Work interface; The feature calculation module is used to clean and normalize the collected data and calculate feature scores in five dimensions: activity decline rate, response latency growth, customer churn signal, follow-up quality score, and historical risk accumulation. The early warning judgment module is used to obtain a comprehensive risk score by weighting and summing the feature scores of each dimension according to preset weights, and to determine the risk level according to a preset range. The notification push module is used to push early warning information and perform customer management actions based on the risk level.
[0020] By adopting the above technical solutions, a modular customer churn risk early warning system was built. Through the collaborative work of each module, the entire process from data collection to early warning push was automated, ensuring the implementation of the technical solutions.
[0021] As a further aspect of the present invention, the system also includes a result feedback module, which is used to receive the administrator's annotations on the warning results, write the results back to the database, and use the results as sample data for the historical cumulative characteristic factors of risk.
[0022] By adopting the above technical solutions and adding a result feedback and sample data utilization mechanism, the closed-loop optimization of the early warning model was achieved, enabling the system to continuously adjust the scoring weights based on historical handling results and improve the system's adaptability.
[0023] As a further aspect of the present invention: the application server is configured with an 8-core CPU and 16GB of memory, and the operating system is Linux; the database server uses MySQL version 8.0; the caching component uses Redis version 6.x; the system connects to WeChat Work through a server configured with a public IP address and HTTPS domain name.
[0024] By adopting the above technical solutions and limiting specific hardware configurations and software environments, a stable operating foundation is provided for the system's high-concurrency processing and real-time computing, ensuring the reliability and continuity of the early warning service under high load.
[0025] Compared with the prior art, the beneficial effects of the present invention are: This invention solves the problem of low accuracy of single-dimensional judgment in existing technologies by constructing a five-dimensional weighted scoring model and capturing customers' active deletion and blocking behaviors in real time, which greatly improves the accuracy of churn warning and effectively reduces false alarms and missed reports. This invention enables real-time early warning of high risks through an event callback mechanism and establishes a tiered response mechanism, shortening the response time for high-risk customer warnings to the minute level, helping managers focus on core issues, and significantly improving customer recovery efficiency and management efficiency.
[0026] Other features and advantages of the present invention will be disclosed in detail in the following detailed description and accompanying drawings. Attached Figure Description
[0027] Figure 1 This is a schematic diagram of the overall process of a method and system for early warning of customer churn risk on WeChat in an embodiment of the present invention. Detailed Implementation
[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0029] In this embodiment of the invention, a method and system for early warning of customer churn risk via WeChat for enterprises are described below. Figure 1 As shown, it includes the following: I. System Architecture and Hardware Environment This embodiment provides an enterprise WeChat customer churn risk early warning system. The system is deployed on an enterprise private server or cloud server, and the hardware environment includes an application server, a database server, and a caching component.
[0030] The application server is configured with an 8-core CPU and 16GB of memory, running a Linux operating system such as CentOS 7 or Ubuntu 20.04, to execute the main alert service and callback listening service; the database server uses MySQL 8.0 and has more than 500GB of storage space to store session data, feature snapshots and risk status; the caching component uses Redis 6.x to store real-time message cache.
[0031] The system needs to be configured with a public IP address and an HTTPS domain name to meet the communication requirements of the WeChat callback interface.
[0032] The system is logically divided into four layers: data acquisition layer, feature calculation layer, early warning judgment layer, and notification push layer.
[0033] II. Method Implementation Process This embodiment provides a method for early warning of customer churn risk on WeChat for businesses, which is executed through the aforementioned system and specifically includes the following steps: S1: Data Acquisition Steps As the system's input layer, the data acquisition layer is primarily responsible for acquiring conversation messages, customer behavior events, and employee follow-up status in real time through the WeChat Work interface.
[0034] In one feasible embodiment, the following are specifically included: First, the system obtains session data in real time through the WeChat Work Session Archive API (msgaudit interface) via polling or Webhook callback.
[0035] Session data includes: Message count: Count the number of messages sent by employees to customers and the number of messages sent by customers to employees; Message timestamp: The time each message was sent; Message interval: the time difference between adjacent messages; Session duration: The total duration from the first message to the last message; Message content types: text, images, voice, emoticons, etc.
[0036] This data is written to the local message cache table, message_cache.
[0037] Secondly, the system performs customer-initiated behavior capture to address the issue that existing technologies cannot capture customer-initiated deletion / blocking behavior.
[0038] (1) For customers to actively delete: The system uses the WeChat event callback mechanism to configure the callback URL.
[0039] When a customer actively deletes a member of an enterprise from their WeChat contacts, the WeChat Enterprise server pushes a JSON-formatted event notification to the system. The event type is "Delete Enterprise Member Event" del_follow_user.
[0040] The system receives and parses the notification, obtains the open_userid of the operating customer, the userid of the deleted employee, and the timestamp, and writes them to the event_log table.
[0041] (2) Regarding customer blacklisting behavior: Since the official WeChat API does not directly provide blacklisting event push, the system adopts a composite detection strategy to indirectly capture it.
[0042] After an employee sends a message, the system periodically polls the message read status through the message status query interface. If a customer sends N consecutive messages (e.g., N=3) and the message read rate remains at 0 with no response, the system, combined with the "sudden drop in recent interaction time" signal, determines that the customer is suspected of being blocked.
[0043] Finally, the system collects employee follow-up status data, including the time of the employee's most recent follow-up and a summary of the follow-up record.
[0044] The system extracts text messages sent by employees in the past 7 days, while filtering out pure emoticons and pure interjections such as "um" and "okay". Messages with a body text greater than a preset character limit (e.g., 10 characters) and containing substantive content, and not greetings or templates, are marked as effective communication.
[0045] S2: Feature Calculation Steps The feature calculation layer cleans and normalizes the collected data, and calculates feature scores in five dimensions according to a pre-defined scoring model; specifically including: (1) Activity decline rate: Calculated based on the ratio of the number of messages in the recent period to the number of messages in the historical period; for example, the ratio of the number of messages in the last 7 days to the number of messages in the last 30 days is calculated. The lower the ratio, the more serious the decline in customer activity, and the higher the feature score. The calculation results are linearly mapped to the 0-100 score range.
[0046] (2) Response delay growth: Calculated based on the ratio of recent average response time to historical average response time; for example, calculate the ratio of the average response time of the past 7 days to the historical average response time. If the ratio is greater than a preset multiple, such as 2 times, the score is full, 100 points; if it is less than a preset multiple, such as 1 time, the score is 0 points. The higher the ratio value, the higher the score.
[0047] (3) Customer churn signal: Based on the customer’s active deletion and blocking behavior. If a deletion event is detected, the highest score is directly assigned, such as 100 points; if it is judged as suspected blocking behavior, the second highest score is assigned, such as 80 points; if there is no abnormality, the score is zero.
[0048] (4) Follow-up quality score: Calculated based on the ratio of effective communication messages to total messages sent. The lower the effective communication ratio, the worse the employee's follow-up quality, and the higher the feature score.
[0049] (5) Historical risk accumulation: Calculated based on a preset time window, such as the number of risk triggers in the past 30 days. Each risk trigger increases the preset score, such as 10 points, with a maximum of the full score.
[0050] S3: Risk Scoring and Assessment Steps The early warning judgment layer summarizes the scores of each dimension and outputs a comprehensive risk score and risk level through a weighted scoring model.
[0051] The calculation logic for the comprehensive risk score is as follows: multiply the feature scores of each dimension by their corresponding preset weights and then sum them.
[0052] In this embodiment, the weights for each dimension are configured as follows: activity decline rate is 25%, response latency growth is 20%, customer churn signal is 30%, follow-up quality score is 15%, and historical risk accumulation is 10%.
[0053] The system determines the risk level based on the preset range in which the comprehensive risk score falls: High risk: The overall risk score is between 70 and 100 points, or the customer churn signal characteristic score directly triggers the high risk threshold; Medium risk: The overall risk score is between 40 and 69 points; Low risk: The overall risk score is between 0 and 39.
[0054] S4: Notification Push and Execution Steps The notification push layer executes corresponding early warning information push and customer management actions based on the risk level.
[0055] For high-risk customers: The system immediately sends an application message interface via WeChat Work to push information such as the warning level, customer name, risk score, and responsible employee to the designated administrator.
[0056] At the same time, the customer allocation mechanism is triggered, generating a customer transfer work order in the background. After the administrator confirms the order, the external contact / transfer_customer interface of WeChat Work is called to automatically transfer the customer to another employee.
[0057] For medium-risk customers: The system performs scheduled summary pushes, such as generating a risk list report at 23:00 every day, distributing it to the administrator's email and WeChat, and adding the customer to the key monitoring list.
[0058] For low-risk customers: System execution records are archived and statistically analyzed in monthly reports.
[0059] S5: Results Feedback and Model Optimization Steps To continuously improve the accuracy of early warnings, the system introduces a result feedback mechanism. Administrators can mark the handling of early warning results in the early warning management backend. The marking types include "followed up", "misjudged", or "transferred".
[0060] The system receives the administrator's handling annotations through the result feedback module and writes the handling results back to the customer risk status table, customer_risk.
[0061] The system constructs training samples from the characteristic data and handling results of this early warning event and inputs them into the scoring optimization model.
[0062] The optimization process of the scoring model includes the following: The system has pre-configured parameter optimization algorithms, such as gradient descent or heuristic adjustment algorithms. As sample data accumulates, the system periodically performs optimization tasks. Input: Historical feature data, including activity level, latency, etc.; and corresponding real tags, including real risks or misjudgments marked by the administrator.
[0063] Processing: Calculate the prediction error under the current weight configuration, i.e., the loss function value; if the misclassification rate is too high, the algorithm analyzes the contribution of each feature dimension to the misclassification.
[0064] For example, if it is found that the follow-up quality score dimension frequently leads to misjudgments, the weight coefficient of that dimension is adaptively reduced, and the weights of other features with higher discriminative power are increased accordingly.
[0065] Output: The updated set of feature weight parameters.
[0066] Through the closed-loop process described above, the system can continuously adjust the parameters of the scoring model to better suit the actual business scenarios of enterprises, thus achieving adaptive evolution of the model.
[0067] Example 1 The following example, using a sales-oriented company (50 employees and approximately 2,000 WeChat management clients), illustrates the complete operating logic of this system.
[0068] Employee Zhang San is responsible for following up with a key client, Mr. Li. The two have been communicating continuously via WeChat for six months. Recently, the company's management noticed that Mr. Li has not been responding proactively for an extended period, raising concerns about the risk of client loss. They hope the system can provide early warnings.
[0069] (1) Data acquisition stage The system's scheduled task synchronizes incrementally every 30 minutes and at 1:00 AM daily, pulling the conversation records between Zhang San and Li Mou through the msgaudit interface and storing them in the message_cache table.
[0070] Meanwhile, 13 days ago (at 14:32 on [Date]), the callback service received an event notification pushed by WeChat Work: Event type: change_external_contact, change_type: del_follow_user, user_id: zhangsan, external_user_id: xxxxxxLiMou, timestamp:1712xxxxxxx The system performs signature verification on the event. If the aes_key is decrypted, the event is written to the event_log table, marking that Li has performed an active deletion operation on Zhang San, and the customer churn signal characteristic factor is directly assigned a value of 100.
[0071] (2) Feature calculation stage The scheduled task triggers feature calculation at 0:30 the next day, reads message_cache and event_log, and calculates as follows: Overall risk score = 95×25% + 100×20% + 100×30% + 88×15% + 20×10% = 23.75 + 20 + 30 + 13.2 + 2 = 88.95 points (3) Early warning judgment and push stage The overall risk score is 88.95, falling into the high-risk range (70-100 points). The system will immediately execute the following actions: Enterprise WeChat push notification: Send an application message to administrator Wang Wu with the following content: "[High Risk Warning] Customer Li (person in charge: Zhang San) has a risk score of 88.95. Active deletion behavior has been detected. It is recommended to intervene immediately." Customer allocation mechanism triggered: A customer transfer work order is generated in the background. After the administrator Wang Wu confirms, Li is transferred to senior sales Zhao Liu through the externalcontact / transfer_customer interface of WeChat.
[0072] List Report: Li was marked as a high-risk customer for the day and written into the daily risk list Excel, which was sent out along with the scheduled task at 23:00.
[0073] (4) Administrator handling and feedback After receiving the warning, administrator Wang Wu marked the handling result as "transferred" in the system backend. The system then wrote the handling status of the warning back to the customer_risk table and included the event feature data into the risk history sample pool for continuous optimization and iteration of subsequent scoring weights.
[0074] This invention provides a method and system for early warning of customer churn risk on WeChat for enterprises, which can achieve real-time and accurate capture of customers' proactive deletion and blocking behaviors, effectively reducing the false alarm rate and the missed alarm rate.
[0075] 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 implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.
[0076] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.
Claims
1. A method for early warning of customer churn risk on WeChat for businesses, characterized in that, Includes the following steps: S1: Real-time acquisition of conversation messages, customer behavior events, and employee follow-up status via the WeChat Work interface; S2: Clean and normalize the collected data, and calculate feature scores for five dimensions: activity decline rate, response delay growth, customer churn signal, follow-up quality score, and historical risk accumulation. Among them, the activity decline rate is calculated based on the ratio of the number of messages in the recent period to the number of messages in the historical period; the response delay growth is calculated based on the ratio of the recent average response time to the historical average response time; the customer churn signal is determined based on the customer's active deletion and blocking behavior; the follow-up quality score is calculated based on the ratio of the number of effective communication messages to the total number of messages sent; and the historical risk accumulation is calculated based on the number of risk triggers within a preset time window. S3: Sum the feature scores of each dimension according to preset weights to obtain a comprehensive risk score, and determine the risk level based on the preset interval in which the comprehensive risk score is located. S4: Execute corresponding early warning information push and customer management actions based on the risk level.
2. The enterprise WeChat customer churn risk early warning method according to claim 1, characterized in that, The session messages in S1 include message quantity, message timestamp, message interval, session duration, and message content type; the message quantity is calculated by counting the number of messages sent by employees to customers and the number of messages sent by customers to employees.
3. The enterprise WeChat customer churn risk early warning method according to claim 1, characterized in that, The employee follow-up status in S1 includes the employee's most recent follow-up time, a summary of the follow-up record content, and a valid communication marker; the valid communication marker is determined by recognizing that the number of characters in the message body is greater than a preset number and contains substantive content.
4. The enterprise WeChat customer churn risk early warning method according to claim 1, characterized in that, The steps for determining customer churn signals in S2 include: By receiving notifications of deleted enterprise members through the WeChat Work event callback mechanism, we can directly capture customers' proactive deletion behavior. By periodically checking the message read status, if the read rate is zero and there is no reply after sending a preset number of messages, and combined with the signal of decreasing interaction time, it is determined to be a suspected blocking behavior.
5. The enterprise WeChat customer churn risk early warning method according to claim 1, characterized in that, The calculation logic for feature scores in S3 is as follows: the lower the ratio of activity decline rate, the higher the score; the higher the ratio of response delay growth, the higher the score; customer-initiated deletion behavior is assigned the highest score; suspected blacklisting behavior is assigned the second highest score; no abnormal behavior is assigned zero score; the lower the follow-up quality ratio, the higher the score; and the more times the historical risk has been accumulated, the higher the score.
6. The enterprise WeChat customer churn risk early warning method according to claim 1, characterized in that, The risk levels in S3 include high risk, medium risk, and low risk; high risk corresponds to the highest score range and is directly triggered based on customer churn signals, executing instant push notifications to administrators and a customer allocation mechanism; medium risk corresponds to the middle score range, executing timed summary pushes and inclusion in a key attention list; low risk corresponds to the lowest score range, executing record archiving and report statistics.
7. The enterprise WeChat customer churn risk early warning method according to claim 1, characterized in that, In S2, the weight of activity decline rate is 25%, the weight of response delay increase is 20%, the weight of customer churn signal is 30%, the weight of follow-up quality score is 15%, and the weight of historical risk accumulation is 10%. In S3, the comprehensive risk score range corresponding to the high-risk level is 70 to 100 points, the comprehensive risk score range corresponding to the medium-risk level is 40 to 69 points, and the comprehensive risk score range corresponding to the low-risk level is 0 to 39 points.
8. A business WeChat customer churn risk early warning system, characterized in that, The method for early warning of customer churn risk using WeChat according to any one of claims 1-7 includes: The data acquisition module is used to obtain conversation messages, customer behavior events, and employee follow-up status in real time through the WeChat Work interface; The feature calculation module is used to clean and normalize the collected data and calculate feature scores in five dimensions: activity decline rate, response latency growth, customer churn signal, follow-up quality score, and historical risk accumulation. The early warning judgment module is used to obtain a comprehensive risk score by weighting and summing the feature scores of each dimension according to preset weights, and to determine the risk level according to a preset range. The notification push module is used to push early warning information and perform customer management actions based on the risk level.
9. A business WeChat customer churn risk early warning system according to claim 8, characterized in that, The system also includes a result feedback module, which is used to receive the administrator's annotations on the handling of the early warning results, write the handling results back to the database, and use the handling results as sample data for the historical cumulative characteristic factors of risk.
10. A business WeChat customer churn risk early warning system according to claim 8, characterized in that, The enterprise WeChat customer churn risk early warning system according to claim 8 is characterized in that it further includes an application server, a database server and a caching component; the system connects to enterprise WeChat through a server configured with a public IP address and an HTTPS domain name.