A method for obtaining intervention measures for customers with high churn risk and related equipment

Abstract

The embodiments of the present application belong to the field of intelligent decision-making technology, and relate to a method for obtaining intervention measures for customers with high churn risk and related equipment. The method includes: extracting a feature subset related to the activity from the customer attribute features and dividing them into an intervention group and a control group; training a random forest Uplift model for each feature subset; predicting and evaluating the incremental effect of each intervention measure based on the trained random forest Uplift model to obtain the incremental effect; constructing a personalized Uplift scoring matrix based on the comprehensive score of each user; and prioritizing the intervention measures for each customer based on the personalized Uplift scoring matrix to determine the optimal intervention measure. The present application can more accurately identify groups with high churn risk and take targeted intervention measures to retain these customers. This can not only improve customer satisfaction and loyalty, but also reduce customer churn rate and enhance the overall competitiveness of the enterprise.

Description

Technical Field

[0001] The present application relates to the field of intelligent decision-making technology, and in particular to a method for obtaining intervention measures for customers with high churn risk and related equipment. Background Art

[0002] In potential customer churn prediction, we face a key technical challenge: how to accurately evaluate the impact of different interventions on customer churn rate.

[0003] Traditional customer churn prediction models typically focus solely on historical customer behavior and attribute characteristics, while ignoring the effectiveness of campaigns. This makes it difficult to effectively identify which customers are most sensitive to interventions, making it difficult to develop targeted retention strategies. Summary of the Invention

[0004] The purpose of the embodiments of the present application is to propose a method and related equipment for obtaining intervention measures for customers with high churn risk, so as to solve the problem that traditional customer churn prediction models are difficult to formulate targeted retention strategies.

[0005] In order to solve the above technical problems, the embodiment of the present application provides a method for obtaining intervention measures for customers with high churn risk, which adopts the following technical solutions:

[0006] Obtain customer attribute characteristics of high-churn risk groups;

[0007] Extracting a feature subset related to the activity from the customer attribute features, wherein each feature subset corresponds to an intervention measure;

[0008] Each characteristic subset was divided into intervention and control groups;

[0009] A random forest uplift model is trained for each feature subset to obtain several trained random forest uplift models.

[0010] According to the trained random forest Uplift model, the incremental effect of each intervention measure is predicted and evaluated to obtain the incremental effect;

[0011] For each user in the high-churn risk group, the incremental effects of all the random forest uplift models are integrated, and the current customer's comprehensive score for each intervention measure is calculated by weighted average;

[0012] Build a personalized Uplift rating matrix based on each user's comprehensive rating;

[0013] Prioritize interventions for each client based on the personalized Uplift scoring matrix to determine the optimal intervention.

[0014] Furthermore, before the step of obtaining the customer attribute characteristics of the high churn risk group, the following steps are also included:

[0015] Obtain all customer historical behavior data and attribute feature data;

[0016] Constructing a customer churn prediction model based on a decision tree algorithm and the attribute feature data;

[0017] Perform prediction operations on all user data according to the churn prediction model to obtain churn probability information for each user;

[0018] The users corresponding to the churn probability information that meets the preset churn threshold are divided into the high churn risk group.

[0019] Furthermore, after the step of obtaining all the customer historical behavior data and attribute feature data, the following steps are also included:

[0020] Preprocessing operations are performed on the historical behavior data and the attribute feature data.

[0021] Furthermore, the step of prioritizing intervention measures for each client based on the personalized Uplift scoring matrix and determining the optimal intervention measure specifically includes the following steps:

[0022] obtaining actual response data of users to the optimal intervention measures;

[0023] A model optimization operation is performed on the random forest Uplift model according to the actual response data to improve the accuracy of the personalized Uplift scoring matrix.

[0024] Furthermore, the step of prioritizing intervention measures for each client based on the personalized Uplift scoring matrix and determining the optimal intervention measure specifically includes the following steps:

[0025] Clustering users in the high-churn risk group based on the K-means clustering algorithm and each user's personalized Uplift score matrix, dividing customers with similar responses into the same customer subgroup;

[0026] For each client subgroup, the Uplift score distribution of different intervention combinations is statistically analyzed to identify the optimal intervention measures that can maximize the positive response.

[0027] Furthermore, after the step of calculating the Uplift score distribution of different intervention combinations for each customer subgroup and identifying the optimal intervention measure that can maximize the positive response, the following step is also included:

[0028] Constructing an intervention combination strategy selection model based on the customer attribute characteristics of the high churn risk group and a decision tree algorithm;

[0029] When a new user arrives, the customer attribute characteristics of the new user are analyzed according to the intervention combination strategy selection model, the optimal intervention combination is obtained and the intervention is implemented.

[0030] In order to solve the above technical problems, the embodiment of the present application further provides a device for obtaining intervention measures for customers with high churn risk, which adopts the following technical solution:

[0031] Customer attribute acquisition module, used to obtain customer attribute characteristics of high churn risk groups;

[0032] a feature subset acquisition module, configured to extract a feature subset related to an activity from the customer attribute features, wherein each feature subset corresponds to an intervention measure;

[0033] Feature subset partitioning module, used to divide each feature subset into intervention group and control group;

[0034] The Uplift model training module is used to train a random forest Uplift model for each feature subset to obtain several trained random forest Uplift models;

[0035] A prediction and evaluation module is used to predict and evaluate the incremental effect of each intervention measure based on the trained random forest Uplift model to obtain the incremental effect;

[0036] A comprehensive score calculation module is used to calculate the current customer's comprehensive score for each intervention measure by combining the incremental effects of all the random forest uplift models for each user in the high churn risk group through a weighted average method;

[0037] Rating matrix building module, used to build a personalized Uplift rating matrix based on each user's comprehensive rating;

[0038] The optimal intervention measure acquisition module is used to prioritize the intervention measures for each customer based on the personalized Uplift scoring matrix and determine the optimal intervention measure.

[0039] Furthermore, the device further comprises:

[0040] Full customer attribute acquisition module, used to obtain full customer historical behavior data and attribute feature data;

[0041] A churn prediction model building module, used to build a customer churn prediction model based on a decision tree algorithm and the attribute feature data;

[0042] A churn prediction module is used to perform prediction operations on all user data based on the churn prediction model to obtain churn probability information for each user;

[0043] The high churn group classification module is used to classify users corresponding to churn probability information that meets a preset churn threshold into the high churn risk group.

[0044] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0045] The system comprises a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the method for obtaining intervention measures for customers with high churn risks as described above are implemented.

[0046] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0047] The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the method for obtaining intervention measures for customers with high churn risks as described above.

[0048] This application provides a method for obtaining intervention measures for customers with high churn risk, comprising: obtaining customer attribute characteristics of a high-churn risk group; extracting activity-related feature subsets from the customer attribute characteristics, where each feature subset corresponds to an intervention measure; dividing each feature subset into an intervention group and a control group; training a random forest uplift model for each feature subset to obtain several trained random forest uplift models; predicting and evaluating the incremental effect of each intervention measure based on the trained random forest uplift models to obtain the incremental effect; for each user in the high-churn risk group, combining the incremental effects of all the random forest uplift models and calculating the current customer's comprehensive rating for each intervention measure by weighted average; constructing a personalized uplift rating matrix based on each user's comprehensive rating; and prioritizing each customer's intervention measures based on the personalized uplift rating matrix to determine the optimal intervention measure. Compared with existing technologies, this application can more accurately identify high-churn risk groups and implement targeted intervention measures to retain these customers. This not only improves customer satisfaction and loyalty, but also reduces customer churn rates and enhances the overall competitiveness of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the solutions in this application, a brief introduction will be given below to the drawings required for use in the description of the embodiments of this application. Obviously, the drawings described below are some embodiments of this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0050] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0051] Figure 2 This is a flowchart of a method for obtaining intervention measures for high-churn risk customers provided in an embodiment of the present application;

[0052] Figure 3 This is a schematic diagram of the structure of a device for obtaining intervention measures for customers with high churn risk, provided in an embodiment of the present application;

[0053] Figure 4 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which this application belongs. The terms used in the specification of the application are for the purpose of describing specific embodiments only and are not intended to limit this application. The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0055] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0056] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0057] like Figure 1As shown, system architecture 100 may include a terminal device 101, a network 102, and a server 103. Terminal device 101 may be a laptop computer 1011, a tablet computer 1012, or a mobile phone 1013. Network 102 is a medium for providing a communication link between terminal device 101 and server 103. Network 102 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0058] The user can use the terminal device 101 to interact with the server 103 via the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0059] The terminal device 101 can be various electronic devices with a display screen and supporting web browsing. In addition to the laptop computer 1011, tablet computer 1012 or mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV) player, a laptop computer and a desktop computer, etc.

[0060] The server 103 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal device 101 .

[0061] It should be noted that the method for obtaining intervention measures for customers with high churn risk provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the device for obtaining intervention measures for customers with high churn risk is generally set in the server / terminal device.

[0062] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0063] Continue to refer Figure 2 , which shows a flow chart of an embodiment of a method for obtaining intervention measures for high-churn risk customers according to the present application. The method for obtaining intervention measures for high-churn risk customers includes: step S201, step S202, step S203, step S204, step S205, step S206, step S207, and step S208.

[0064] In step S201, customer attribute characteristics of a high churn risk group are obtained.

[0065] In step S202 , feature subsets related to the activity are extracted from the customer attribute features, where each feature subset corresponds to an intervention measure.

[0066] In step S203, each feature subset is divided into an intervention group and a control group.

[0067] In step S204, a random forest uplift model is trained for each feature subset to obtain several trained random forest uplift models.

[0068] In step S205, the incremental effect of each intervention measure is predicted and evaluated based on the trained random forest Uplift model to obtain the incremental effect.

[0069] In step S206, for each user in the high churn risk group, the incremental effects of all random forest Uplift models are integrated, and the current customer's comprehensive score for each intervention measure is calculated by weighted average.

[0070] In step S207, a personalized Uplift rating matrix is ​​constructed based on the comprehensive rating of each user.

[0071] In step S208, the intervention measures for each customer are prioritized according to the personalized Uplift scoring matrix to determine the optimal intervention measure.

[0072] In the embodiment of the present application, it is necessary to identify which customer groups have a higher risk of churn through data analysis. This is usually based on historical data, such as customer purchase history, service usage, account activity records, etc.

[0073] In an embodiment of the present application, a churn risk prediction model can be constructed through machine learning algorithms (such as logistic regression, decision trees, etc.) or statistical methods to identify groups with high churn risk.

[0074] In the embodiment of the present application, once these high-risk groups are identified, it is necessary to collect their customer attribute characteristics, such as age, gender, income level, geographic location, usage habits, etc.

[0075] In this embodiment of the present application, among the collected customer attribute characteristics, it is necessary to screen out those directly related to the intervention activity. These characteristics should be able to reflect the customer's potential response to different intervention measures. For example, if the intervention measure is to provide coupons, then characteristics such as the customer's purchase frequency and average spending amount may be directly related to the response rate.

[0076] In the embodiment of the present application, each feature subset corresponds to an intervention measure, so that the effects of different intervention measures can be evaluated based on different feature subsets.

[0077] In the embodiment of the present application, for each feature subset, the customers are randomly divided into an intervention group and a control group. The intervention group will receive specific intervention measures, while the control group will not receive any intervention as a baseline comparison.

[0078] In the present application embodiment, for each feature subset (i.e., each intervention measure), an Uplift model is trained using the random forest algorithm. The Uplift model is a special machine learning model used to evaluate the incremental effect of an intervention measure on a target variable (i.e., the difference between the intervention group and the control group). By training these models, the potential impact of different intervention measures on the customers in each feature subset can be predicted.

[0079] In the examples of this application, the trained random forest Uplift model is used to predict and evaluate the incremental effect of each intervention measure. This typically involves calculating the difference between the intervention group and the control group on the target variable (such as retention rate, purchase amount, etc.).

[0080] In this embodiment, for each user in the high-churn risk group, the incremental effects of all random forest uplift models are combined. A weighted average is used to calculate the current customer's overall score for each intervention measure. This score reflects the customer's potential response to different intervention measures.

[0081] In this embodiment of the application, a personalized Uplift rating matrix is ​​constructed based on each user's comprehensive rating. This matrix shows each customer's rating and priority for different intervention measures.

[0082] In this embodiment of the application, intervention measures for each customer are prioritized based on a personalized Uplift scoring matrix. The intervention with the highest score or the most significant incremental effect is selected as the optimal intervention. This helps companies develop personalized retention strategies and improve the effectiveness and efficiency of intervention measures.

[0083] In an embodiment of the present application, a method for obtaining intervention measures for customers with high churn risk is provided, comprising: obtaining customer attribute characteristics of a high-churn risk group; extracting feature subsets related to activities from the customer attribute characteristics, wherein each feature subset corresponds to an intervention measure; dividing each feature subset into an intervention group and a control group; training a random forest uplift model for each feature subset to obtain several trained random forest uplift models; predicting and evaluating the incremental effect of each intervention measure based on the trained random forest uplift models to obtain the incremental effect; for each user in the high-churn risk group, combining the incremental effects of all random forest uplift models and calculating the current customer's comprehensive score for each intervention measure by weighted average; constructing a personalized uplift rating matrix based on each user's comprehensive score; and prioritizing the intervention measures for each customer based on the personalized uplift rating matrix to determine the optimal intervention measure. Compared with existing technologies, the present application can more accurately identify high-churn risk groups and implement targeted intervention measures to retain these customers. This can not only improve customer satisfaction and loyalty, but also reduce customer churn rates and enhance the overall competitiveness of the enterprise.

[0084] In some optional implementations of the embodiments of the present application, before the step of obtaining the customer attribute characteristics of the high churn risk group, the following steps are also included:

[0085] Obtain all customer historical behavior data and attribute feature data;

[0086] Build a customer churn prediction model based on the decision tree algorithm and attribute feature data;

[0087] Perform prediction operations on all user data based on the churn prediction model to obtain the churn probability information of each user;

[0088] The users whose churn probability information meets the preset churn threshold are divided into high churn risk groups.

[0089] In this embodiment of the present application, the full set of historical customer behavior data refers to all interactions between customers and the company over the past period of time, such as purchase history, browsing history, customer service consultation history, complaint history, etc. This data can reflect the customer's consumption habits, preferences, and satisfaction with the company's products or services.

[0090] In the embodiment of the present application, attribute feature data includes static information of customers, such as age, gender, occupation, income level, geographic location, etc. This information helps to understand the background and socioeconomic characteristics of customers, thereby more comprehensively understanding customer behavior and churn risk.

[0091] In the embodiment of the present application, these data can be obtained through channels such as internal databases, customer relationship management systems (CRMs), data warehouses, etc. At the same time, it is also possible to consider purchasing relevant data from third-party data providers to enrich and improve customer portraits.

[0092] In the embodiment of the present application, the decision tree constructs a model by recursively partitioning the data set. In customer churn prediction, the decision tree can predict whether a customer will churn based on the customer's attribute feature data.

[0093] In an embodiment of the present application, the prediction operation may input all user data into a trained churn prediction model. The model then calculates the churn probability of each user based on the user's attribute feature data. The churn probability information is a value between 0 and 1, indicating the likelihood of the user churn within a certain period of time. The closer the value is to 1, the higher the risk of user churn; the closer the value is to 0, the lower the risk of user churn.

[0094] In the embodiments of the present application, the preset churn threshold is primarily used to determine whether a user's churn risk is sufficiently high to classify them into a high-churn risk group. This classification operation involves comparing each user's churn probability with the preset churn threshold. If a user's churn probability is greater than or equal to the threshold, they are classified as a high-churn risk group; if a user's churn probability is less than the threshold, they are considered low-risk or normal users.

[0095] In the embodiments of this application, it is possible to accurately identify which users are at high risk of churn, thereby taking targeted retention measures, improving customer satisfaction and loyalty, and reducing customer churn rates. This also provides companies with valuable market insights and decision-making support, helping them develop more precise and effective marketing strategies.

[0096] In some optional implementations of the embodiments of the present application, after the step of obtaining the full amount of customer historical behavior data and attribute feature data, the following steps are also included:

[0097] Perform preprocessing operations on historical behavior data and attribute feature data.

[0098] In the embodiment of the present application, through preprocessing operations, the quality of historical behavior data and attribute feature data can be ensured, providing a solid foundation for subsequent data analysis and machine learning model training.

[0099] In some optional implementations of the embodiments of the present application, the steps of prioritizing intervention measures for each customer based on the personalized Uplift scoring matrix and determining the optimal intervention measure specifically include the following steps:

[0100] Obtaining actual user response data on optimal interventions;

[0101] The random forest uplift model is optimized based on the actual response data to improve the accuracy of the personalized uplift score matrix.

[0102] In the embodiment of this application, based on the previous analysis, each user is assigned the optimal intervention measures (such as coupons, customized recommendations, customer service follow-up, etc.). These measures are communicated to users through the company's marketing channels (such as email, SMS, APP push, etc.).

[0103] In the embodiment of the present application, once intervention measures are implemented, the enterprise needs to track the user's actual response to these measures, including whether the user opens the pushed message, clicks on the link, uses the coupon, makes a purchase, or other related behaviors.

[0104] In the embodiment of the present application, the user's response behavior will be recorded in the company's data system, such as CRM system, order system, log system, etc. This data includes response time, response type (such as click, purchase), response amount, etc.

[0105] In the embodiment of the present application, the model optimization operation of the random forest uplift model according to the actual response data can be:

[0106] ① Data integration: The collected actual response data is integrated with previous user attribute feature data, historical behavior data, and the feature subset previously used to train the Uplift model. This ensures that the data used in the model optimization process is comprehensive and consistent.

[0107] ② Model evaluation: Use actual response data to evaluate the performance of the previously trained random forest Uplift model. This includes calculating evaluation metrics such as the model's accuracy, recall, and F1 score, as well as comparing the intervention group with the control group through methods such as A / B testing.

[0108] ③ Feature Importance Analysis: Analyze which features are most important in predicting user responses. This helps companies understand which user attributes or behavior patterns significantly influence the effectiveness of interventions, allowing them to better design future interventions.

[0109] ④ Model Adjustment: Based on the evaluation results and feature importance analysis, adjust the Random Forest Uplift model. This may include adding or removing features, adjusting model parameters (such as the number of trees, maximum depth, etc.), using different data preprocessing techniques, etc.

[0110] ⑤ Retraining: Retrain the Random Forest Uplift model using the adjusted features and parameters. This step aims to improve the model’s predictive power, enabling it to more accurately predict user responses to different interventions.

[0111] ⑥ Model validation: After retraining the model, use a portion of actual response data that did not participate in model training for validation to ensure that the performance of the model has improved after optimization.

[0112] In the embodiment of the present application, the random forest Uplift model can be continuously optimized to improve the accuracy of the personalized Uplift scoring matrix, thereby formulating more effective personalized marketing strategies, reducing customer churn rate, and improving customer satisfaction and loyalty.

[0113] In some optional implementations of the embodiments of the present application, the steps of prioritizing intervention measures for each customer based on the personalized Uplift scoring matrix and determining the optimal intervention measure specifically include the following steps:

[0114] Cluster users in high-churn risk groups using the K-means clustering algorithm and each user's personalized Uplift score matrix, dividing customers with similar responses into the same customer subgroup.

[0115] For each client subgroup, the Uplift score distribution of different intervention combinations was statistically analyzed to identify the optimal intervention measures that can maximize positive responses.

[0116] In an embodiment of the present application, the clustering operation may be as follows: first extract user data of a high-churn risk group, including their personalized Uplift rating matrix. This matrix contains the user's ratings for a variety of different interventions, reflecting the user's potential response to different interventions; determine the K value, which can be determined by business logic, data characteristics, or experimental methods; cluster users using the K-means algorithm, which assigns users to K cluster centers (i.e., customer subgroups) based on their personalized Uplift rating matrix, so that users in the same subgroup have similar Uplift rating patterns; use indicators such as the Silhouette Coefficient and the Calinski-Harabasz Index to evaluate the quality of clustering to ensure that users are correctly divided into different subgroups.

[0117] In this example, for each customer subgroup, their Uplift scores for different intervention combinations are collected. These scores reflect the potential response of users within the subgroup to a specific intervention measure. For each intervention combination, the distribution of its Uplift scores within the subgroup is calculated. This includes statistics such as the mean, median, and variance of the scores to understand the performance of different intervention measures within the subgroup.

[0118] In the present embodiment, the effects of different interventions in subgroups are compared based on the distribution of Uplift scores. Generally, interventions with high mean and low variance are considered to be more effective because they can induce more stable and significant positive responses.

[0119] In this embodiment, for each customer subgroup, the intervention measure with the best Uplift score distribution is selected as the optimal intervention measure for that subgroup. This means that the measure can maximize the positive response in that subgroup and reduce the risk of customer churn.

[0120] In this embodiment of the application, a personalized marketing strategy is developed based on the optimal intervention measures for each customer subgroup. This includes determining marketing channels, message content, discount strength, etc. to ensure that the intervention measures can accurately reach the target users and trigger a positive response.

[0121] In this embodiment of the present application, users in high-churn risk groups can be more accurately identified and divided into different customer subgroups. For each subgroup, the company can develop personalized marketing strategies and select the optimal intervention measures to improve customer satisfaction and loyalty and reduce customer churn rate.

[0122] In some optional implementations of the embodiments of the present application, after the above steps of calculating the Uplift score distribution of different intervention combinations for each customer subgroup and identifying the optimal intervention measure that can maximize the positive response, the following steps are also included:

[0123] Construct an intervention combination strategy selection model based on the customer attribute characteristics of the high-churn risk group and the decision tree algorithm;

[0124] When a new user arrives, the customer attribute characteristics of the new user are analyzed according to the intervention combination strategy selection model, the optimal intervention combination is obtained and the intervention is implemented.

[0125] In the embodiments of this application, a precise intervention combination strategy selection model can be constructed, and personalized intervention measures can be provided to new users. This helps reduce the risk of customer churn, improve customer satisfaction and loyalty, and thus enhance the overall competitiveness of the enterprise.

[0126] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to achieve optimal results.

[0127] Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0128] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware via computer-readable instructions. The computer-readable instructions can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes in the above-described method embodiments. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0129] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0130] Further references Figure 3 , as a response to the above Figure 2 The present application provides an embodiment of a device for obtaining intervention measures for customers with high churn risk. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0131] like Figure 3 As shown, the device 200 for obtaining intervention measures for high-churn risk customers in the embodiment of the present application includes:

[0132] The customer attribute acquisition module 210 is used to obtain the customer attribute characteristics of the high churn risk group;

[0133] A feature subset acquisition module 220 is configured to extract feature subsets related to activities from customer attribute features, wherein each feature subset corresponds to an intervention measure;

[0134] A feature subset partitioning module 230 is configured to partition each feature subset into an intervention group and a control group;

[0135] An Uplift model training module 240 is configured to train a random forest Uplift model for each feature subset to obtain a plurality of trained random forest Uplift models;

[0136] The prediction and evaluation module 250 is used to predict and evaluate the incremental effect of each intervention measure based on the trained random forest Uplift model to obtain the incremental effect;

[0137] Comprehensive score calculation module 260 is used to calculate the current customer's comprehensive score for each intervention measure by combining the incremental effects of all random forest uplift models for each user in the high churn risk group through weighted average;

[0138] A rating matrix construction module 270 is used to construct a personalized Uplift rating matrix based on the comprehensive rating of each user;

[0139] The optimal intervention measure acquisition module 280 is used to prioritize the intervention measures for each customer based on the personalized Uplift scoring matrix and determine the optimal intervention measure.

[0140] In an embodiment of the present application, a device 200 for acquiring intervention measures for customers with high churn risk is provided, comprising: a customer attribute acquisition module 210 for acquiring customer attribute characteristics of a group with high churn risk; a feature subset acquisition module 220 for extracting feature subsets related to activities from the customer attribute characteristics, wherein each feature subset corresponds to an intervention measure; a feature subset partitioning module 230 for partitioning each feature subset into an intervention group and a control group; an Uplift model training module 240 for training a random forest Uplift model for each feature subset to obtain a plurality of trained random forest Uplift models; and a prediction and evaluation module 250 for extracting feature subsets related to activities from the customer attribute characteristics, wherein each feature subset corresponds to an intervention measure; and a feature subset partitioning module 230 for partitioning each feature subset into an intervention group and a control group. 0, used to predict and evaluate the incremental effect of each intervention measure based on the trained random forest Uplift model to obtain the incremental effect; the comprehensive score calculation module 260, used to integrate the incremental effects of all random forest Uplift models for each user in the high churn risk group, and calculate the current customer's comprehensive score for each intervention measure by weighted average; the scoring matrix construction module 270, used to construct a personalized Uplift scoring matrix based on the comprehensive score of each user; the optimal intervention measure acquisition module 280, used to prioritize the intervention measures for each customer based on the personalized Uplift scoring matrix, and determine the optimal intervention measure. Compared with the existing technology, the present application can more accurately identify high churn risk groups and take targeted intervention measures to retain these customers. This can not only improve customer satisfaction and loyalty, but also reduce customer churn rate and enhance the overall competitiveness of the enterprise.

[0141] In some optional implementations of the embodiments of the present application, the above-mentioned intervention measure acquisition device 200 for high-churn risk customers further includes:

[0142] Full customer attribute acquisition module, used to obtain full customer historical behavior data and attribute feature data;

[0143] Churn prediction model building module, used to build a customer churn prediction model based on the decision tree algorithm and attribute feature data;

[0144] The churn prediction module is used to perform prediction operations on all user data based on the churn prediction model to obtain the churn probability information of each user;

[0145] The high churn group classification module is used to classify users corresponding to churn probability information that meets a preset churn threshold into a high churn risk group.

[0146] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device according to an embodiment of the present application.

[0147] The computer device 300 includes a memory 310, a processor 320, and a network interface 330 that are interconnected through a system bus. It should be noted that the figure only shows the computer device 300 having components 310-330, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0148] The computer device may be a desktop computer, notebook computer, PDA, cloud server, etc. The computer device may interact with the user via a keyboard, mouse, remote control, touchpad, or voice control device.

[0149] The memory 310 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic storage, magnetic disk, optical disk, etc. In some embodiments, the memory 310 may be an internal storage unit of the computer device 300, such as a hard disk or memory of the computer device 300. In other embodiments, the memory 310 may also be an external storage device of the computer device 300, such as a plug-in hard disk equipped on the computer device 300, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Of course, the memory 310 may also include both the internal storage unit of the computer device 300 and its external storage device. In the embodiment of the present application, the memory 310 is typically used to store an operating system and various application software installed on the computer device 300, such as computer-readable instructions for obtaining intervention measures for high-churn risk customers. Furthermore, the memory 310 can also be used to temporarily store various data that has been output or is about to be output.

[0150] In some embodiments, the processor 320 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 320 is generally used to control the overall operation of the computer device 300. In the embodiment of the present application, the processor 320 is used to execute computer-readable instructions or process data stored in the memory 310, such as executing computer-readable instructions for the method for obtaining intervention measures for high-churn risk customers.

[0151] The network interface 330 may include a wireless network interface or a wired network interface. The network interface 330 is generally used to establish a communication connection between the computer device 300 and other electronic devices.

[0152] The computer device provided by this application can more accurately identify high-churn risk groups and implement targeted intervention measures to retain these customers. This can not only improve customer satisfaction and loyalty, but also reduce customer churn rates and enhance the overall competitiveness of the enterprise.

[0153] The present application also provides another embodiment, namely, providing a computer-readable storage medium, which stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the above-mentioned method for obtaining intervention measures for customers with high churn risk.

[0154] The computer-readable storage medium provided in this application can more accurately identify high-churn risk groups and take targeted intervention measures to retain these customers. This can not only improve customer satisfaction and loyalty, but also reduce customer churn rates and enhance the overall competitiveness of the enterprise.

[0155] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0156] Obviously, the embodiments described above are only some of the embodiments of the present application, rather than all of the embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions described in the aforementioned specific embodiments, or to make equivalent replacements for some of the technical features therein. Any equivalent structure made using the contents of the present application specification and the accompanying drawings, directly or indirectly used in other related technical fields, is also within the scope of patent protection of the present application.

Claims

1. A method for obtaining intervention measures for customers with high churn risk, characterized in that: The steps include: Obtain customer attribute characteristics of high-churn risk groups; Extracting feature subsets related to intervention activities from the customer attribute features, wherein each feature subset corresponds to an intervention measure; Clients within each characteristic subset were divided into intervention and control groups; A random forest uplift model is trained for each feature subset to obtain several trained random forest uplift models. According to the trained random forest Uplift model, the incremental effect of each intervention measure is predicted and evaluated to obtain the incremental effect; For each user in the high-churn risk group, the incremental effects of all the random forest uplift models are integrated, and the current customer's comprehensive score for each intervention measure is calculated by weighted average; Build a personalized Uplift rating matrix based on each user's comprehensive rating; Prioritize interventions for each client based on the personalized Uplift scoring matrix to determine the optimal intervention; The step of prioritizing intervention measures for each client based on the personalized Uplift scoring matrix and determining the optimal intervention measure specifically comprises the following steps: Clustering users in the high-churn risk group based on the K-means clustering algorithm and each user's personalized Uplift score matrix, dividing customers with similar responses into the same customer subgroup; For each client subgroup, we statistically analyze the Uplift score distribution of different intervention combinations and identify the optimal intervention that maximizes positive responses. After the step of calculating the Uplift score distribution of different intervention combinations for each customer subgroup and identifying the optimal intervention measure that can maximize the positive response, the following step is also included: Constructing an intervention combination strategy selection model based on the customer attribute characteristics of the high churn risk group and a decision tree algorithm; When a new user arrives, the customer attribute characteristics of the new user are analyzed according to the intervention combination strategy selection model, the optimal intervention combination is obtained and the intervention is implemented.

2. The method for obtaining intervention measures for high-risk customers according to claim 1, characterized in that: Before the step of obtaining the customer attribute characteristics of the high churn risk group, the following steps are also included: Obtain all customer historical behavior data and attribute feature data; Constructing a customer churn prediction model based on a decision tree algorithm and the attribute feature data; Perform prediction operations on all user data according to the churn prediction model to obtain churn probability information for each user; The users corresponding to the churn probability information that meets the preset churn threshold are divided into the high churn risk group.

3. The method for obtaining intervention measures for high-churn risk customers according to claim 2, characterized in that: After the step of obtaining all the customer historical behavior data and attribute feature data, the following steps are also included: Preprocessing operations are performed on the historical behavior data and the attribute feature data.

4. The method for obtaining intervention measures for high-churn risk customers according to claim 1, characterized in that: The step of prioritizing intervention measures for each client based on the personalized Uplift scoring matrix and determining the optimal intervention measure specifically comprises the following steps: obtaining actual response data of users to the optimal intervention measures; A model optimization operation is performed on the random forest Uplift model according to the actual response data to improve the accuracy of the personalized Uplift scoring matrix.

5. A device for obtaining intervention measures for customers with high churn risk, characterized in that The device is used to implement the steps of the method for obtaining intervention measures for customers with high churn risk according to any one of claims 1 to 4, and the device comprises: Customer attribute acquisition module, used to obtain customer attribute characteristics of high churn risk groups; a feature subset acquisition module, configured to extract feature subsets related to intervention activities from the customer attribute features, wherein each feature subset corresponds to an intervention measure; A feature subset division module is used to divide the clients of each feature subset into an intervention group and a control group; The Uplift model training module is used to train a random forest Uplift model for each feature subset to obtain several trained random forest Uplift models; A prediction and evaluation module is used to predict and evaluate the incremental effect of each intervention measure based on the trained random forest Uplift model to obtain the incremental effect; A comprehensive score calculation module is used to calculate the current customer's comprehensive score for each intervention measure by combining the incremental effects of all the random forest uplift models for each user in the high churn risk group through a weighted average method; Rating matrix building module, used to build a personalized Uplift rating matrix based on each user's comprehensive rating; An optimal intervention measure acquisition module, configured to prioritize intervention measures for each client based on the personalized Uplift scoring matrix and determine the optimal intervention measure; The step of prioritizing intervention measures for each client based on the personalized Uplift scoring matrix and determining the optimal intervention measure specifically comprises the following steps: Clustering users in the high-churn risk group based on the K-means clustering algorithm and each user's personalized Uplift score matrix, dividing customers with similar responses into the same customer subgroup; For each client subgroup, we statistically analyze the Uplift score distribution of different intervention combinations and identify the optimal intervention that maximizes positive responses. After the step of calculating the Uplift score distribution of different intervention combinations for each customer subgroup and identifying the optimal intervention measure that can maximize the positive response, the following step is also included: Constructing an intervention combination strategy selection model based on the customer attribute characteristics of the high churn risk group and a decision tree algorithm; When a new user arrives, the customer attribute characteristics of the new user are analyzed according to the intervention combination strategy selection model, the optimal intervention combination is obtained and the intervention is implemented.

6. The device for obtaining intervention measures for high-risk customers according to claim 5, characterized in that: The device further comprises: Full customer attribute acquisition module, used to obtain full customer historical behavior data and attribute feature data; A churn prediction model building module, used to build a customer churn prediction model based on a decision tree algorithm and the attribute feature data; A churn prediction module is used to perform prediction operations on all user data based on the churn prediction model to obtain churn probability information for each user; The high churn group classification module is used to classify users corresponding to churn probability information that meets a preset churn threshold into the high churn risk group.

7. A computer device comprising a memory and a processor, characterized in that: The memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the method for obtaining intervention measures for customers with high churn risks according to any one of claims 1 to 4 are implemented.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the method for obtaining intervention measures for customers with high churn risks according to any one of claims 1 to 4.

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