Enterprise customer loss early warning and retention method and system

Abstract

The invention relates to the technical field of enterprise customer management, in particular to an enterprise customer loss early warning and retention method and system, and the method comprises the steps: collecting customer historical behavior data, customer service interaction data and historical retention execution records, and constructing non-execution retention and customer behavior change data before and after retention execution; performing time alignment and differential processing on the related data to generate a retention behavior influence feature set; and judging whether the retention behavior has a risk amplification trend or not, generating a corresponding retention behavior risk level, and further forming a retention control instruction to adjust the execution state of the retention operation. According to the method, the potential risk possibly introduced by the retention behavior can be identified before retention execution or in the execution process, the customer loss risk is prevented from being amplified by an improper retention strategy, refined evaluation and dynamic control of the retention behavior are realized, and the stability and reliability of a customer relationship management system in a complex customer behavior scene are improved.

Description

Technical Field

[0001] This invention relates to the field of enterprise customer management, specifically to methods and systems for early warning and retention of enterprise customer churn. Background Technology

[0002] With the development of information technology and data processing technology, enterprise customer management systems are gradually shifting from traditional manual experience-based management to intelligent management models based on data analysis and automated decision-making. Especially in the fields of customer churn early warning and customer relationship maintenance, the collection and analysis of customer behavior data, transaction data, and service interaction data are widely adopted to identify customer churn risks and take corresponding intervention measures.

[0003] Existing customer churn warning and retention technology solutions typically calculate and classify customer churn risk based on customers' historical behavioral characteristics, changes in transaction frequency, service response status, and abnormal behavior indicators. When the risk of customer churn exceeds a preset threshold, the system automatically or semi-automatically triggers retention strategies, such as arranging manual follow-up visits, offering preferential conditions, or strengthening service support.

[0004] The inventors of this application have discovered the following technical problems with the aforementioned technology: In existing technical solutions, retention actions are pre-set as a positive intervention to reduce customer churn risk. The system mainly focuses on whether the customer remains after the retention action is executed, and uses the retention result as the main evaluation criterion for retention effectiveness, without further analyzing or modeling the potential impact of the retention action itself on the customer's subsequent behavior. In reality, inappropriate retention actions may directly stimulate customer aversion or vigilance, thereby increasing the risk of customer churn. Furthermore, retention actions can change the customer's threshold for judging the acceptability of customer relationships, causing the customer's tolerance for service quality or cooperative relationships to decrease. The customer may develop a subconscious belief that "if I leave, the other party will make concessions," which means that the customer's threshold for tolerating dissatisfaction decreases, and the threshold for triggering churn negotiations is advanced. Moreover, inappropriate retention actions can also cause the customer's focus to shift from the product and service itself to retention conditions and room for concessions, thus shifting the customer's decision-making basis. In other words, the customer's original focus was on the quality of the product and service itself, but after the focus shifts, the customer will pay more attention to the company's room for concessions, compare different retention conditions, and regard their own churn as a bargaining tool. Current technology cannot identify the impact of retention efforts on customer relationship thresholds and customer focus, which means that even if retention is successful in the short term, it may actually amplify the risk of customer churn in subsequent stages. Summary of the Invention

[0005] This application provides a method and system for early warning and retention of enterprise customers, which solves the problem that inappropriate retention strategies in the existing technology amplify the risk of customer churn.

[0006] To achieve the above objectives, the embodiments of this application disclose the following technical solutions:

[0007] On the one hand, this solution discloses enterprise customer churn early warning and retention methods, including the following steps: obtaining customer historical behavior data, customer service interaction data, historical retention execution records, and customer behavior change data. The historical retention execution records include at least the retention triggering time, retention method, and retention intensity.

[0008] The customer behavior change data includes customer behavior change data generated under the condition that no retention operation was performed, and customer behavior change data before and after retention operation generated with the retention operation time point as a reference. The customer behavior change data before and after retention operation is used to characterize the changes in customer behavior caused by retention operation.

[0009] Perform feature extraction processing on customer historical behavior data and customer service interaction data to generate customer behavior feature values;

[0010] Time alignment processing is performed on customer historical behavior data, customer service interaction data, and customer behavior change data before and after retention efforts to obtain time alignment results. Based on the time alignment results, differential processing is performed on customer behavior change data before and after retention efforts to generate a set of retention behavior impact features.

[0011] Based on the set of characteristics affecting retention behaviors, risk assessment parameters corresponding to retention behaviors are calculated. These risk assessment parameters include short-term churn risk parameters, customer relationship threshold change parameters, and customer focus shift parameters.

[0012] Based on risk assessment parameters, determine whether retention actions have a risk amplification trend, and generate a risk level for retention actions accordingly;

[0013] Retention control instructions are generated based on the risk level of the retention behavior, and these instructions are used to control the execution status of the corresponding retention operation.

[0014] On the other hand, this solution discloses an enterprise customer churn early warning and retention system, including:

[0015] The data acquisition module is used to acquire customer historical behavior data, customer service interaction data, historical retention execution records, and customer behavior change data. The historical retention execution records include at least the retention triggering time, retention method, and retention intensity. The customer behavior change data includes customer behavior change data generated under the condition that no retention operation was performed, as well as customer behavior change data before and after retention execution generated with the retention execution time point as a reference. The customer behavior change data before and after retention execution is used to characterize the changes in customer behavior caused by retention execution.

[0016] The feature processing module is used to extract features from historical customer behavior data and customer service interaction data to generate customer behavior feature values.

[0017] The time alignment and impact feature generation module is used to perform time alignment processing on customer historical behavior data, customer service interaction data, and customer behavior change data before and after retention execution. Based on the time alignment results, the module performs differential processing on the customer behavior change data before and after retention execution to generate a set of retention behavior impact features.

[0018] The risk assessment module is used to calculate the risk assessment parameters corresponding to retention behaviors based on the set of impact characteristics of retention behaviors. The risk assessment parameters include short-term churn risk parameters, customer relationship threshold change parameters, and customer focus shift parameters.

[0019] The risk level determination module is used to determine whether retention actions have a risk amplification trend based on risk assessment parameters, and to generate the risk level of retention actions.

[0020] The retention control module is used to generate retention control instructions based on the risk level of retention actions. These instructions are used to control the execution status of the corresponding retention operations.

[0021] This solution unifies the modeling of historical customer behavior data, customer service interaction data, and historical retention efforts records. It introduces a comparative analysis mechanism that compares changes in customer behavior before and after retention efforts with changes in behavior before or after retention efforts, enabling a quantitative assessment of the true impact of retention actions. Compared to existing solutions that trigger retention actions solely based on churn probability or static rules, this solution, through time alignment and differential processing, accurately characterizes the actual effect of retention actions on changes in customer behavior, thereby identifying potential risk amplification trends that retention actions may trigger. By constructing a multi-dimensional risk assessment system including short-term churn risk parameters, customer relationship threshold change parameters, and customer focus shift parameters, retention decisions not only focus on short-term retention effects but also comprehensively reflect customer relationship stability and long-term behavioral change characteristics, avoiding the negative impact of excessive or inappropriate retention efforts on customer relationships. Furthermore, this solution dynamically generates retention control instructions based on the risk level of retention actions, achieving refined control over the execution status of retention operations and improving the controllability and security of retention strategies. Overall, this solution effectively improves the accuracy of customer churn early warning and the scientific nature of retention decisions, reduces the probability of ineffective or reverse retention actions, and has significant practical application value and technological advancement effects. Attached Figure Description

[0022] Figure 1 This is a flowchart of the method according to Embodiment 1 of the present invention;

[0023] Figure 2This is a schematic diagram illustrating the composition of customer behavior change data in Embodiment 1 of the present invention;

[0024] Figure 3 This is a logic diagram of time alignment and differential processing in Embodiment 1 of the present invention;

[0025] Figure 4 This is a diagram illustrating the relationship between the risk level and control of retention behavior in Embodiment 1 of the present invention.

[0026] Figure 5 This is a system structure block diagram of Embodiment 2 of the present invention. Detailed Implementation

[0027] Specific embodiments of the invention will now be described in detail. Although the invention is described in conjunction with these specific embodiments, it should be understood that the invention is not intended to be limited to these specific embodiments. Rather, these embodiments are intended to cover alternative, modified, or equivalent embodiments that may be included within the spirit and scope of the invention as defined by the claims. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. The invention may be practiced without some or all of these specific details. In other instances, well-known processes have not been described in detail so as not to unnecessarily obscure the invention.

[0028] When used in conjunction with the terms "comprising," "method comprising," or similar language in this specification and appended claims, the singular forms "a," "some," and "the" include plural references unless the context clearly indicates otherwise. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0029] Application Overview:

[0030] In existing technologies, enterprise customer churn warning and retention solutions typically identify churn risks based on customers' historical behavioral characteristics and service interaction data, and directly trigger retention strategies when the risk exceeds a threshold. For example, this might involve manual follow-ups or offering incentives to encourage customer retention. However, such solutions assume that retention actions must have a positive effect, using customer retention as the sole evaluation criterion and ignoring the potential impact of retention actions on subsequent customer behavior. If these issues are not addressed, inappropriate retention actions can ultimately alter customers' tolerance threshold for customer relationships, causing them to view churn as a bargaining chip, and even shifting their focus from products and services to room for concessions, thus amplifying the risk of churn in later stages.

[0031] To address the aforementioned issues, this application first considers incorporating the retention efforts themselves into the risk assessment, identifying potential side effects by analyzing changes in customer behavior before and after retention efforts are implemented. This application attempts to quantify the impact of retention efforts on customer behavior trajectories by constructing data on changes in customer behavior before and after retention efforts are implemented and comparing it with natural behavior changes under conditions where retention efforts were not implemented. This aims to solve the problem that existing technologies cannot identify the amplification of retention risks, thereby enabling tiered assessment and dynamic control of retention efforts, and ultimately reducing the long-term negative impact of retention strategies on customer relationships at the system level.

[0032] Example 1

[0033] Methods for early warning and retention of enterprise customer churn include the following steps:

[0034] Step 1: Acquire customer historical behavior data, customer service interaction data, historical retention execution records, and customer behavior change data. The historical retention execution records include at least the retention triggering time, retention method, and retention intensity. The customer behavior change data includes customer behavior change data generated under conditions where no retention operation was performed, and customer behavior change data before and after retention execution generated with the retention execution time point as a reference. The customer behavior change data before and after retention execution is used to characterize the changes in customer behavior caused by retention execution. Customer historical behavior data includes customer usage frequency data, customer interaction interval data, and customer behavior stability data. Customer service interaction data includes service request record data and service response record data. The customer behavior change data before and after retention execution is generated by associating customer historical behavior data and historical retention execution records before and after the retention execution time point. The customer behavior change data without retention execution is generated by associating customer historical behavior data within the time window when no retention operation was triggered.

[0035] In this embodiment, customer historical behavior data is automatically collected by the enterprise business system, usage log system, or customer management system to reflect customers' usage habits and behavioral rhythms in a natural state. Customer service interaction data is collected by the customer service system or work order system to reflect the interaction characteristics between customers and service providers. Historical retention execution records are process record data automatically collected and formed by the enterprise customer relationship management system (CRM), customer service system, or marketing automation system during the retention operation execution process, used to clarify the time point, method, and intensity of retention. When the system triggers a retention control instruction based on the risk level of retention behavior, the execution process of the retention operation will be automatically recorded by the system in the form of logs or business records, forming a corresponding historical retention execution record. This record corresponds one-to-one with a specific customer identifier, retention trigger time point, and retention execution status, and serves as the basic data source for risk assessment parameter calculation model modeling and risk assessment parameters in subsequent analysis.

[0036] Customer behavior change data is not collected directly, but rather constructed based on historical customer behavior data under different time conditions. Specifically, under the condition that no retention efforts were executed, historical customer behavior data is continuously compared by selecting a time window where retention efforts were not triggered, generating customer behavior change data before retention efforts were executed, which is used to characterize the natural trend of customer behavior changes. Under the condition that retention efforts were executed, the time interval before and after the retention efforts were executed is selected as a reference, generating customer behavior change data before and after the retention efforts were executed, which is used to characterize the disturbance impact of retention efforts on customer behavior trajectories.

[0037] Step 2: Perform feature extraction processing on customer historical behavior data and customer service interaction data to generate customer behavior feature values.

[0038] In this embodiment, the purpose of feature extraction processing is to transform raw, discrete, and heterogeneous data into computable and alignable customer behavior feature values. Feature extraction processing can pre-define behavioral dimensions based on domain knowledge, such as behavior frequency, interaction interval, service response correlation, and behavior continuity. Through this step, historical behavior data and customer service interaction data are uniformly mapped into a multi-dimensional behavioral feature space, ensuring a consistent measurement basis for subsequent differential calculations of customer behavior changes before and after retention efforts. This avoids noise interference caused by directly comparing historical behavior data and customer service interaction data.

[0039] Step 3: Perform time alignment processing on customer historical behavior data, customer service interaction data, and customer behavior change data before and after retention efforts to obtain time alignment results. Based on the time alignment results, perform differential processing on the customer behavior change data before and after retention efforts to generate a set of retention behavior impact features. The time alignment processing includes constructing a unified time axis based on the retention trigger timing and mapping customer historical behavior data, customer service interaction data, and customer behavior change data before and after retention efforts to the unified time axis to form a comparable data sequence. The differential processing includes: the customer behavior feature values ​​include customer behavior feature values ​​before retention efforts and customer behavior feature values ​​after retention efforts. Perform differential processing on the customer behavior feature values ​​before and after retention efforts based on the time alignment processing to obtain differential results. Use the differential results as a representation parameter of the retention behavior on customer behavior changes and include them in the set of retention behavior impact features.

[0040] Because customer historical behavior data, customer service interaction data, and historical retention execution records are asynchronous in terms of time distribution, this embodiment first constructs a unified timeline based on the retention trigger timing, and maps various types of data to this timeline to form a time series that can be directly compared.

[0041] After time alignment is completed, differential processing is performed on the customer behavior characteristic values ​​before and after the retention action, centered on the retention action execution time. This differential processing is not limited to simple numerical subtraction, but is used to characterize the directional and magnitude changes in customer behavior characteristics after the introduction of retention actions.

[0042] In order to quantitatively characterize the directional and magnitude changes in customer behavior characteristics caused by retention efforts, this embodiment performs differential calculations on the customer behavior characteristic values ​​before and after retention efforts are executed, based on the retention execution time point after the time alignment process is completed.

[0043] By comparing customer behavior characteristics before and after retention efforts, centered on the retention action execution time, we can eliminate baseline differences in behavior between different customers, retaining only the behavioral changes introduced by the retention action. This leads to the fundamental calculation formula for the impact characteristics of retention actions:

[0044] ;

[0045] in, This indicates the time before the retention execution time, after time alignment processing. A set of customer behavior feature values ​​is obtained by performing feature extraction processing on customer historical behavior data and customer service interaction data. In this embodiment, taking the retention execution time point as a reference, a pre-set time interval is selected before the retention execution time point, and feature extraction processing is performed on customer historical behavior data and customer service interaction data within this time interval to obtain a set of customer behavior feature values ​​before retention execution. In the time alignment processing stage, a unified time axis is constructed based on the retention trigger timing, and the customer behavior feature values ​​before retention execution are mapped to the corresponding time positions in the unified time axis, thereby forming a corresponding set at time point t. ;

[0046] This indicates that after the point in time for retaining the execution, and with The first one located at the same time axis position Each customer behavior feature value is derived from customer historical behavior data and customer service interaction data, but corresponds to behavior data after the retention execution time point. In this embodiment, the system takes the retention execution time point as the starting point, selects a preset time interval after the retention execution time point, and performs feature extraction processing on customer historical behavior data and customer service interaction data within the time interval to obtain a set of customer behavior feature values ​​after retention execution.

[0047] After completing the time alignment process, the system maps the customer behavior feature values ​​after the retention attempt to the same timeline as the behavior feature values ​​before the retention attempt, and determines the corresponding customer behavior feature value after the retention attempt at time point t. .

[0048] This represents the change in the i-th customer behavior characteristic caused by retention actions at time point t, and is used to characterize the direct impact of retention actions on customer behavior.

[0049] The differential results are considered a direct representation of the impact of retention actions on customer behavior and, as a component of the set of characteristics of the impact of retention actions, provide basic inputs for risk assessment parameters.

[0050] Step 4: Calculate the risk assessment parameters corresponding to retention behaviors based on the set of impact characteristics of retention behaviors. These parameters include short-term churn risk parameters, customer relationship threshold change parameters, and customer focus shift parameters. The short-term churn risk parameter is calculated based on the magnitude of changes in customer behavior data before and after the retention action within a preset time window. The customer relationship threshold change parameter is calculated based on the long-term trend of customer behavior stability parameters. The customer focus shift parameter is calculated based on the weight changes of different interaction features in customer service interaction data. Before calculating the risk assessment parameters corresponding to retention behaviors, the following steps are also included: classifying retention behaviors according to retention methods and intensity based on historical retention execution records, and constructing corresponding risk assessment parameter calculation models for different categories of retention behaviors to match the risk assessment parameters with specific retention behavior types; and calculating the volatility, trend consistency analysis, or variance of customer behavior characteristic values ​​at continuous time points according to a preset time span, based on customer behavior characteristic values, to generate customer behavior stability parameters that characterize the long-term stability of customer behavior.

[0051] In this embodiment, the set of features affecting retention behavior is used as input data to calculate short-term churn risk parameters, customer relationship threshold change parameters, and customer focus shift parameters, respectively.

[0052] Among them, the short-term churn risk parameter is used to reflect the degree of abnormal changes in customer behavior in a short period of time after retention efforts are implemented; the customer relationship threshold change parameter is used to reflect the trend of changes in customer behavior stability over a longer time scale, and characterizes the changes in customer tolerance for the cooperative relationship.

[0053] In order to characterize the structural impact of retention behaviors on customer behavior stability over a longer time scale, this embodiment constructs a customer relationship threshold change parameter based on the changes in customer behavior stability parameters at different time stages.

[0054] By comparing and analyzing the changing trends of customer behavior stability parameters over multiple time periods before and after retention efforts, we can reflect whether the customer's tolerance threshold for the cooperative relationship has shifted, and thus obtain the calculation formula for the customer relationship threshold change parameter:

[0055] ;

[0056] This refers to customer behavior stability parameters calculated based on a preset time span before retention efforts are implemented. These parameters are generated by extracting features from historical customer behavior data and performing stability analysis. In this embodiment, before the retention effort is implemented, the system calculates volatility, performs trend consistency analysis, or calculates variance of customer behavior feature values ​​based on consecutive time points in historical customer behavior data, according to a preset time span, to generate customer behavior stability parameters that characterize the long-term stability of customer behavior. After time alignment, these parameters are mapped to positions on a unified timeline corresponding to the time period before retention efforts are implemented, and are determined at a reference time point before retention efforts are implemented. ;

[0057] This represents the customer behavior stability parameter calculated based on the same time span after the retention attempt is executed. It also originates from historical customer behavior data, but corresponds to the behavior after the retention attempt occurs. In this embodiment, after the retention attempt execution time point, the system selects a preset time span of the same length as before the execution, performs stability analysis on the customer behavior feature values ​​at consecutive time points in the historical customer behavior data, and generates the customer behavior stability parameter after the retention attempt. After completing the time alignment process, the system maps the customer behavior stability parameter after the retention attempt to a unified time axis and aligns it with... The corresponding time position is determined as .

[0058] To measure the concentration and abnormality of customer behavior changes within a preset short-term time window after retention efforts are implemented, this embodiment aggregates and calculates the magnitude of customer behavior changes after retention efforts are implemented based on a set of influence features of retention behaviors. By weighted summarization of changes in multiple customer behavior features within the short-term time window, a short-term churn risk parameter can be obtained to characterize the short-term churn risk level, leading to the following calculation formula:

[0059] ;

[0060] in, This parameter represents the short-term churn risk and is used to characterize the comprehensive degree of abnormal changes in customer behavior in the short term after retention efforts are implemented.

[0061] This represents the changes in customer behavior before and after the retention attempt corresponding to the i-th customer behavior characteristic within a preset short-term time window. To avoid interference from fluctuations at a single point in time on short-term risk assessment and to reflect the overall trend of customer behavior changes in a short period after the retention attempt, this embodiment uses the retention attempt time point as a reference within the preset time window. Within, for multiple time points corresponding to Aggregation processing is performed to obtain windowed changes in customer behavior. .

[0062] The value represents the importance coefficient of the i-th customer behavior feature in the short-term churn risk assessment. Based on historical retention execution records, the correlation between changes in different customer behavior features and actual customer churn results is analyzed. The corresponding weight is determined according to the strength of the impact of changes in each behavior feature on the short-term churn result.

[0063] The total number of customer behavioral characteristics.

[0064] This embodiment uses customer service interaction features to assess changes in customer behavior. Customer service interaction features refer to various characteristics exhibited by customers during service interactions with the enterprise, including but not limited to service request type, service response latency, service processing method, interaction frequency, interaction duration, and interaction channel characteristics. To further quantify the impact of these interaction features on changes in customer behavior, this application introduces the concept of customer service interaction feature weights. These weights characterize the relative impact of different customer service interaction features on changes in customer behavior within a certain time frame.

[0065] The customer focus shift parameter analyzes changes in the weights of customer service interaction features to determine whether customers have shifted their focus from the product or service itself to retention conditions or room for concessions.

[0066] In order to identify whether the customer’s focus has changed before and after the retention efforts, this embodiment first extracts customer service interaction features based on customer service interaction data, and then introduces customer service interaction feature weights by statistically analyzing the degree of influence of different customer service interaction features on changes in customer behavior within a certain time range.

[0067] Based on this, by comparing and analyzing the changes in the weights of customer service interaction features before and after retention efforts, it can be determined whether the customer's focus has shifted, thus deriving the formula for calculating the customer focus shift parameter:

[0068] ;

[0069] It is a customer focus shift parameter, used to characterize the overall degree of change in the customer's focus before and after the retention process;

[0070] This represents the weight of the customer service interaction feature corresponding to the j-th customer service interaction feature before the retention attempt is executed. Before the retention attempt is executed, the system determines the corresponding behavioral baseline time window based on the retention trigger time. Within the behavioral baseline time window, the system obtains the customer behavior feature value of the target customer under the corresponding dimension of the j-th customer service interaction feature or customer behavior feature. The customer behavior feature value is the behavioral feature result extracted based on the customer's historical behavior data and customer service interaction data. The customer behavior feature values ​​within the behavioral baseline time window are processed for time alignment and numerical aggregation to obtain the baseline behavior value corresponding to the j-th attention behavior before the retention attempt is executed. .

[0071] This represents the weight of the customer service interaction feature corresponding to the j-th customer service interaction feature after the retention attempt is executed. After the retention attempt is executed, a corresponding behavior evaluation time window is determined with the retention attempt completion time as a reference. Within the behavior evaluation time window, the system obtains the target customer's customer behavior feature value under the dimension corresponding to the j-th customer service interaction feature or customer behavior feature. The customer behavior feature value is the behavior feature result extracted based on historical customer behavior data and customer service interaction data. The customer behavior feature values ​​within the behavior evaluation time window undergo time alignment and numerical aggregation processing consistent with the behavior baseline time window to obtain the behavior value corresponding to the j-th attention behavior after the retention attempt is executed. .

[0072] This indicates the number of customer service interaction features.

[0073] Before calculating the aforementioned risk assessment parameters, the system can classify retention behaviors based on historical retention execution records and match corresponding risk assessment parameter calculation models for different categories of retention behaviors to avoid mutual interference between risk assessment results of different retention methods.

[0074] Step 5: Based on the risk assessment parameters, determine whether the retention behavior has a risk amplification trend, and generate a retention behavior risk level accordingly. Determining whether the retention behavior has a risk amplification trend includes: comparing and analyzing the risk assessment parameters corresponding to the retention behavior with the behavioral change data of customers who did not perform retention actions, obtaining the comparison analysis results, determining the direction of risk change of the retention behavior relative to not performing retention actions, and generating a retention behavior risk level based on the comparison analysis results. The retention behavior risk level includes at least an allowed execution level, a restricted execution level, and a blocked execution level; different retention behavior risk levels correspond to different retention control instructions.

[0075] In this embodiment, the determination of the risk amplification trend is not based on an isolated analysis of behavioral changes after retention efforts are implemented, but rather by comparing and analyzing the risk assessment parameters corresponding to the retention efforts with the behavioral change data of customers who did not undergo retention efforts.

[0076] By comparing and analyzing data, it can be determined whether retention efforts amplify the risks associated with customer churn compared to the natural evolution of customer behavior. When the direction of risk change after retention efforts deviates significantly from the natural trend, it is determined that the retention efforts have a risk amplification trend, and a corresponding risk level for the retention efforts is generated accordingly.

[0077] Different retention behaviors have different risk levels, corresponding to different strategies, which are used to constrain the execution of subsequent retention control instructions at the system level. In this implementation, retention strategies are proposed to characterize the specific retention methods to be taken for the target customer and their execution intensity.

[0078] Step 6: Generate retention control instructions based on the risk level of the retention behavior. The retention control instructions are used to control the execution status of the corresponding retention strategy, including whether to allow the execution of the retention strategy, restrict the execution method or intensity of the retention strategy, or prevent the execution of the retention strategy.

[0079] In this implementation, to classify and control the risk levels of retention actions and to achieve differentiated scheduling of retention strategies, the risk levels of retention actions are divided into permitted execution levels, restricted execution levels, and blocked execution levels. Different retention action risk levels are used to characterize the acceptability of retention operations in the current customer situation.

[0080] Based on this, retention control instructions are generated directly driven by the risk level of retention actions, and are used to control the execution status of retention operations. Specifically, when the risk level of retention actions is at the permissible execution level, the system allows the corresponding retention operations to be executed according to the predetermined retention strategy; when the risk level of retention actions is at the restricted execution level, the system constrains the retention methods or intensity of retention operations to reduce potential risks; when the risk level of retention actions is at the blocking execution level, the system blocks the execution of the corresponding retention operations to avoid negatively impacting customer relationships.

[0081] Through the above control mechanisms, inappropriate retention behaviors can be avoided at the system level from causing long-term structural damage to customer relationships, enabling proactive identification and dynamic control of retention risks, thereby improving the stability and security of the customer management system in complex behavioral scenarios.

[0082] Example 2

[0083] Enterprise customer churn early warning and retention system, including:

[0084] The data acquisition module is used to acquire customer historical behavior data, customer service interaction data, historical retention execution records, and customer behavior change data. The historical retention execution records include at least the retention triggering time, retention method, and retention intensity. The customer behavior change data includes customer behavior change data generated under the condition that no retention operation was performed, as well as customer behavior change data before and after retention execution generated with the retention execution time point as a reference. The customer behavior change data before and after retention execution is used to characterize the changes in customer behavior caused by retention execution.

[0085] The feature processing module is used to extract features from historical customer behavior data and customer service interaction data to generate customer behavior feature values.

[0086] The time alignment and impact feature generation module is used to perform time alignment processing on customer historical behavior data, customer service interaction data, and customer behavior change data before and after retention execution. Based on the time alignment results, the module performs differential processing on the customer behavior change data before and after retention execution to generate a set of retention behavior impact features.

[0087] The risk assessment module is used to calculate the risk assessment parameters corresponding to retention behaviors based on the set of impact characteristics of retention behaviors. The risk assessment parameters include short-term churn risk parameters, customer relationship threshold change parameters, and customer focus shift parameters.

[0088] The risk level determination module is used to determine whether retention actions have a risk amplification trend based on risk assessment parameters, and to generate the risk level of retention actions.

[0089] The retention control module is used to generate retention control instructions based on the risk level of retention actions. These instructions are used to control the execution status of the corresponding retention operations.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them; although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications can still be made to the specific implementation methods of the present invention or equivalent substitutions can be made to some technical features without departing from the spirit of the technical solutions of the present invention, and all such modifications should be covered within the scope of the technical solutions claimed in the present invention.

Claims

1. A method for early warning and retention of enterprise customer churn, characterized in that, The process includes the following steps: acquiring customer historical behavior data, customer service interaction data, historical retention execution records, and customer behavior change data. The historical retention execution records include at least the retention triggering time, retention method, and retention intensity. The customer behavior change data includes customer behavior change data generated under the condition that no retention operation was performed, and customer behavior change data before and after retention operation generated with the retention operation time point as a reference. The customer behavior change data before and after retention operation is used to characterize the changes in customer behavior caused by retention operation. Perform feature extraction processing on customer historical behavior data and customer service interaction data to generate customer behavior feature values; Time alignment processing is performed on customer historical behavior data, customer service interaction data, and customer behavior change data before and after retention efforts to obtain time alignment results. Based on the time alignment results, differential processing is performed on customer behavior change data before and after retention efforts to generate a set of retention behavior impact features. Based on the set of characteristics affecting retention behaviors, risk assessment parameters corresponding to retention behaviors are calculated. These risk assessment parameters include short-term churn risk parameters, customer relationship threshold change parameters, and customer focus shift parameters. Based on risk assessment parameters, determine whether retention actions have a risk amplification trend, and generate a risk level for retention actions accordingly; Retention control instructions are generated based on the risk level of the retention behavior, and these instructions are used to control the execution status of the corresponding retention operation.

2. The enterprise customer churn early warning and retention method according to claim 1, characterized in that, Customer historical behavior data includes customer usage frequency data, customer interaction interval data, and customer behavior stability data. Customer service interaction data includes service request record data and service response record data. The customer behavior change data before and after retention efforts are generated by associating customer historical behavior data with historical retention efforts records before and after the retention efforts execution time. The customer behavior change data for customers who did not undergo retention efforts are generated by associating customer historical behavior data with the time window during which no retention efforts were triggered.

3. The enterprise customer churn early warning and retention method according to claim 1, characterized in that, Time alignment processing involves constructing a unified timeline based on the timing of retention attempts, and mapping customer historical behavior data, customer service interaction data, and customer behavior change data before and after retention attempts to the unified timeline to form a comparable data sequence.

4. The enterprise customer churn early warning and retention method according to claim 1, characterized in that, The differential processing includes: the customer behavior feature values ​​include customer behavior feature values ​​before retention action and customer behavior feature values ​​after retention action; differential processing is performed on the customer behavior feature values ​​before retention action and customer behavior feature values ​​after retention action based on time alignment processing to obtain differential results; and the differential results are used as a representation parameter of the change in customer behavior caused by retention action and included in the feature set of retention action influence.

5. The enterprise customer churn early warning and retention method according to claim 1, characterized in that, Based on customer behavior characteristic values, and according to a preset time span, the volatility, trend consistency analysis, or variance calculation of customer behavior characteristic values ​​at continuous time points are performed to generate customer behavior stability parameters that characterize the long-term stability of customer behavior.

6. The enterprise customer churn early warning and retention method according to claim 1, characterized in that, The short-term churn risk parameter is calculated based on the magnitude of changes in customer behavior data before and after the retention action within a preset time window after the retention action is implemented. The customer relationship threshold change parameter is calculated based on the long-term trend of the customer behavior stability parameter. The customer focus shift parameter is calculated based on the weight changes of different interaction features in customer service interaction data.

7. The enterprise customer churn early warning and retention method according to claim 1, characterized in that, The risk levels of retention actions include at least three levels: permitted execution level, restricted execution level, and blocked execution level. Different retention action risk levels correspond to different retention control instructions.

8. The enterprise customer churn early warning and retention method according to claim 1, characterized in that, Before calculating the risk assessment parameters corresponding to retention actions, the following are also included: Based on historical retention execution records, retention behaviors are classified according to retention methods and retention intensity, and corresponding risk assessment parameter calculation models are constructed for different categories of retention behaviors to match the risk assessment parameters with the specific retention behavior type.

9. The enterprise customer churn early warning and retention method according to claim 1, characterized in that, When determining whether retention efforts have a risk amplification trend, the following steps are taken: compare and analyze the risk assessment parameters corresponding to retention efforts with the data on changes in customer behavior when retention efforts are not performed, obtain the comparative analysis results, determine the direction of risk change of retention efforts relative to when retention efforts are not performed, and generate the risk level of retention efforts based on the comparative analysis results.

10. A customer churn early warning and retention system for enterprises, used to execute the method described in any one of claims 1-6, characterized in that, include: The data acquisition module is used to acquire customer historical behavior data, customer service interaction data, historical retention execution records, and customer behavior change data. The historical retention execution records include at least the retention triggering time, retention method, and retention intensity. The customer behavior change data includes customer behavior change data generated under the condition that no retention operation was performed, as well as customer behavior change data before and after retention execution generated with the retention execution time point as a reference. The customer behavior change data before and after retention execution is used to characterize the changes in customer behavior caused by retention execution. The feature processing module is used to extract features from historical customer behavior data and customer service interaction data to generate customer behavior feature values. The time alignment and impact feature generation module is used to perform time alignment processing on customer historical behavior data, customer service interaction data, and customer behavior change data before and after retention execution. Based on the time alignment results, the module performs differential processing on the customer behavior change data before and after retention execution to generate a set of retention behavior impact features. The risk assessment module is used to calculate the risk assessment parameters corresponding to retention behaviors based on the set of impact characteristics of retention behaviors. The risk assessment parameters include short-term churn risk parameters, customer relationship threshold change parameters, and customer focus shift parameters. The risk level determination module is used to determine whether retention actions have a risk amplification trend based on risk assessment parameters, and to generate the risk level of retention actions. The retention control module is used to generate retention control instructions based on the risk level of retention actions. These instructions are used to control the execution status of the corresponding retention operations.

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