User behavior response method and device, storage medium and computer equipment

Abstract

The invention relates to the technical field of computers, and discloses a user behavior response method and device, a storage medium and computer equipment, and the method can be applied to high-interaction-frequency business scenes such as insurance, financial management and credit loan, and comprises the following steps: responding to an operation behavior of a user for a target business; the method comprises the following steps: generating a user operation identifier, collecting user behavior characteristic data, determining a decision buffer period based on the user behavior characteristic data, then obtaining a behavior response strategy corresponding to a behavior response stage in the decision buffer period, and sequentially pushing a plurality of behavior response strategies to a user terminal. And obtaining an operation decision result of the user for the target business after the decision buffer period is ended. According to the method, the user behavior feature data is collected, the decision buffer period is constructed, intelligent pushing of the staged response strategy is combined, accurate recognition and timely intervention of the user behavior intention are achieved before the user executes key operation, and the user retention rate is increased.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method, device, storage medium and computer equipment for responding to user behavior. Background Art

[0002] With the rapid development of financial services, customer churn management has become a critical factor affecting a company's operational stability and long-term competitiveness. This is especially true in high-interaction scenarios such as insurance, wealth management, and credit. The ability to instantly identify and dynamically intervene in user behavior is directly related to customer retention, service satisfaction, and the company's sustainable development capabilities. For example, in the health insurance business, with intensified market competition and diversified customer needs, policy surrender rates continue to rise. This large number of premature policy surrenders can cause fluctuations in a company's cash flow and increase operating costs, impacting normal operations.

[0003] Existing policy management systems generally lack the ability to accurately identify user cancellation risks and effective customer retention methods. Most systems only trigger manual intervention or simple prompts after the user finally submits a cancellation application, and are unable to capture the user's actual behavior path and decision-making motivation, missing the optimal intervention window. In addition, the current cancellation process lacks a buffer mechanism, and it is impossible to intervene in a timely and effective manner for users' impulsive cancellation behavior completed in a short period of time, further causing a decline in user retention rate and affecting the normal operation of the company. Summary of the Invention

[0004] In view of this, the present application provides a method, apparatus, storage medium and computer equipment for responding to user behavior, the main purpose of which is to solve the technical problems in the prior art of lacking the ability to identify and intervene in user behavior in business scenarios with high interaction frequency such as insurance, and lacking a buffer mechanism.

[0005] According to a first aspect of the present invention, a method for responding to user behavior is provided, the method comprising:

[0006] In response to a user's operation behavior for a target service, a user operation identifier is generated, and user behavior feature data corresponding to the user operation identifier is collected, wherein the user behavior feature data includes operation trigger data, operation behavior data, and operation environment data;

[0007] Determining a decision buffer period based on the user behavior characteristic data, wherein the decision buffer period includes a plurality of behavior response stages divided based on a time sequence;

[0008] Obtain the behavior response strategy corresponding to the behavior response stage, push multiple behavior response strategies to the user terminal in sequence during the decision buffer period, and obtain the user's operation decision result for the target business after the decision buffer period ends.

[0009] Optionally, in response to the user's operation behavior on the target business, a user operation identifier is generated, and user behavior feature data corresponding to the user operation identifier is collected, including: in response to an operation request submitted by the user for the target business, a user operation identifier is generated and an information filling page is displayed, and operation trigger data corresponding to the user operation identifier is collected; in response to a filling operation on the information filling page triggered by the user, operation behavior data corresponding to the user operation identifier is collected; in response to a filling information confirmation operation triggered by the user, operation environment data corresponding to the target operation identifier is collected.

[0010] Optionally, determining the decision buffer period based on the user behavior characteristic data includes: obtaining a preset basic buffer period; determining a user portrait factor based on the user operation identifier, and adjusting the basic buffer period according to the user portrait factor to obtain a first dynamic buffer period; determining a behavior data factor based on the operation trigger data and the operation behavior data, and adjusting the basic buffer period according to the behavior data factor to obtain a second dynamic buffer period; determining an external environment factor based on the operation environment data, and adjusting the basic buffer period according to the external environment factor to obtain a third dynamic buffer period; determining a decision buffer period based on the first dynamic buffer period, and / or the second dynamic buffer period, and / or the third dynamic buffer period.

[0011] Optionally, the user operation identifier includes user identity information, and the decision buffer period includes a recognition stage, an intervention stage, and a guidance stage divided based on time sequence; the behavior response strategy corresponding to the behavior response stage is obtained, and multiple behavior response strategies are pushed to the user terminal in sequence during the decision buffer period, including: in the recognition stage, determining the rights and interests issuance information that the user has enjoyed in the target business based on the user identity information, and pushing the rights and interests issuance information to the user terminal; in the intervention stage, identifying the business preferential information that matches the user in the target business based on the user identity information, pushing the business preferential information to the user terminal, and establishing a service access entrance for the target business; in the guidance stage, identifying the business adjustment information that matches the user in the target business based on the user identity information, pushing the business adjustment information to the user terminal, and establishing a business adjustment entrance for the target business, wherein the business adjustment entrance is used to adjust the target business according to the business adjustment information.

[0012] Optionally, the method also includes: before the decision buffer period begins, pushing decision-making auxiliary information to the user terminal, wherein the decision-making auxiliary information includes the rights issuance information and operation behavior prompt information for the target business; within a preset time before the end of the decision buffer period, pushing the operation behavior prompt information to the user terminal and establishing the business adjustment entry.

[0013] Optionally, the multiple behavior response strategies are pushed to the user terminal in sequence during the decision buffer period, including: in response to the user's information viewing operation for the target business during the decision buffer period, generating a target behavior response strategy corresponding to the user behavior feature data based on preset policy rules, and pushing the target behavior response strategy to the user terminal.

[0014] Optionally, the operation decision result includes canceling the operation behavior and completing the operation behavior; obtaining the user's operation decision result for the target business after the decision buffer period ends includes: when the operation decision result is the cancel operation behavior, terminating the user's operation behavior for the target business, clearing the task queue associated with the user operation identifier, pushing the operation cancellation prompt information to the user terminal, and recording the user behavior feature data and the operation decision result; when the operation decision result is the complete operation behavior, verifying the user identity and executing the user's operation behavior for the target business, pushing the operation completion prompt information to the user terminal, and recording the user behavior feature data and the operation decision result.

[0015] According to a second aspect of the present invention, there is provided a device for responding to user behavior, the device comprising:

[0016] A data collection module, configured to generate a user operation identifier in response to a user's operation behavior for a target service, and collect user behavior feature data corresponding to the user operation identifier, wherein the user behavior feature data includes operation trigger data, operation behavior data, and operation environment data;

[0017] a buffer period determination module, configured to determine a decision buffer period based on the user behavior characteristic data, wherein the decision buffer period includes a plurality of behavior response stages divided based on a time sequence;

[0018] The strategy push module is used to obtain the behavior response strategy corresponding to the behavior response stage, push multiple behavior response strategies to the user terminal in sequence during the decision buffer period, and obtain the user's operation decision result for the target business after the decision buffer period ends.

[0019] According to a third aspect of the present invention, there is provided a storage medium storing a computer program, which implements the above-mentioned method for responding to user behavior when the program is executed by a processor.

[0020] According to a fourth aspect of the present invention, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for responding to user behavior when executing the program.

[0021] The present invention provides a user behavior response method, device, storage medium and computer equipment, which improve the ability to identify user behavior. Specifically, by generating user operation identifiers, it can track and bind key user operations, and collect three types of user behavior feature data, thereby realizing early perception of user intentions and effectively focusing on the motivation and process of user behavior; and constructing a dynamic intervention mechanism, which determines the target decision buffer period based on user behavior feature data rather than a fixed waiting time, and determines corresponding different behavior response strategies at each behavior response stage, supports multiple strategy combinations, realizes phased intervention, and improves intervention effect; and sets a decision buffer period after the user performs a key operation to avoid the operation taking effect immediately, and continuously pushes intervention strategies during the decision buffer period, giving users the opportunity to rethink and adjust, thereby significantly reducing irrational cancellation behavior caused by emotional fluctuations and insufficient information cognition. The above method collects user behavior feature data and constructs a decision buffer period, combined with the intelligent push of phased response strategies, to accurately identify and timely intervene in users' behavioral intentions before they perform key operations. It effectively solves problems such as the difficulty in controlling user churn, the single intervention method, and the lack of a buffer mechanism in high-interaction frequency business scenarios such as insurance, significantly improves user retention rate and customer service satisfaction, and ensures the stable operation of the enterprise.

[0022] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0024] Figure 1 A schematic diagram showing a flow chart of a method for responding to user behavior provided by an embodiment of the present invention;

[0025] Figure 2 A schematic diagram showing a flow chart of another method for responding to user behavior provided by an embodiment of the present invention;

[0026] Figure 3 A schematic structural diagram of a device for responding to user behavior provided by an embodiment of the present invention is shown;

[0027] Figure 4 A schematic diagram of the device structure of a computer device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0028] The following describes exemplary embodiments of the present application in more detail with reference to the accompanying drawings. Although exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0029] It should be noted that the user behavior response method provided by the present invention is applicable to business scenarios with high interaction frequency, such as insurance, wealth management, and credit. In these businesses, the interaction between users and the system is frequent and the decision-making path is complex. Users may make irrational operational decisions due to information misunderstanding deviations, emotional fluctuations or external environmental influences, such as surrendering insurance, redeeming wealth management products, repaying in advance or canceling services. Such behaviors not only affect the actual interests of users, but may also have an adverse impact on the company's customer retention rate, financial stability and operational efficiency.

[0030] Based on this, the embodiment of the present application provides a method for responding to user behavior, such as Figure 1 As shown, the method includes the following steps:

[0031] 101. In response to a user's operation behavior for a target service, generate a user operation identifier and collect user behavior feature data corresponding to the user operation identifier, wherein the user behavior feature data includes operation trigger data, operation behavior data, and operation environment data.

[0032] Among them, users' operational behaviors for target businesses vary in different business scenarios. For example, in the insurance business, the operational behavior is specifically applying for policy cancellation, while in the e-commerce scenario, the operational behavior is specifically applying for a return or refund. The operational behavior serves as the trigger point of the entire response process, indicating that the user may enter a behavioral path that requires attention or intervention. The user operation identifier is used as a unique identifier to bind the data record of the entire operation process, realizing the full tracking of the user's single operation. The user behavior feature data collected based on the user operation identifier can reflect the user's behavior patterns and potential intentions. Among them, the operation trigger data reflects how the user triggers the operation, specifically which button is clicked and the entry point through which the policy cancellation page is accessed, including timestamps, operation paths, device types, etc. Operation behavior data refers to the user's specific behavioral performance throughout the operation process, including the length of time on the page, the number of times the field is filled in, the frequency of modification, the mouse trajectory, and whether the help information is repeatedly opened. The operation environment data refers to the external environmental factors when the operation occurs, including device type, network status, geographic location, weather, and holidays. Based on this, user behavior feature data can characterize the user's current state in multiple dimensions and support subsequent behavior prediction and risk assessment.

[0033] Specifically, the user's operational behavior for the target business is used as the starting point of intervention, and then a user operation identifier is created to collect user behavior feature data to make user behavior traceable. The user behavior feature data is specifically divided into three categories, which can fully construct a user behavior portrait and provide a data basis for the subsequent intervention process.

[0034] In the embodiment of the present application, the present invention achieves accurate identification of user behavior intentions and tracking of the entire process by responding to user operation behaviors and collecting multi-dimensional user behavior feature data, combined with the generation and management of user operation identifiers, providing a data basis for the subsequent construction of a dynamic decision buffer period and a phased response strategy, solving the problems of insufficient user behavior recognition capabilities and delayed intervention in the existing technology, and improving the system response level.

[0035] 102. Determine a decision buffer period based on user behavior feature data, wherein the decision buffer period includes multiple behavior response stages divided based on time sequence.

[0036] Among them, the decision buffer period refers to a calm observation period set from the key operation behavior triggered by the user to the final confirmation of execution, or it is called the intervention window period. The decision buffer period prevents users from making irreversible operations due to impulse, and at the same time provides a time window for pushing intervention strategies to improve user retention and optimize customer experience. Taking the insurance business as an example, when the user clicks the cancellation button, the system does not immediately execute the cancellation process, but enters a period of decision buffer period. During this period, retention information can be pushed to the user, or the user can wait for calmness to cancel the relevant operation by himself; the behavioral response stage is divided into multiple stages based on the entire decision buffer period. Time periods, each time period corresponds to a different intervention strategy, which is specifically divided based on chronological order, from the beginning to the end of the buffer period, and arranged in chronological order. Taking the decision buffer period of 48 hours as an example, the first behavioral response stage corresponds to the 0-12 hours of the user's application for policy cancellation operation. The user can be prompted about the value of rights and interests, such as unused rights and interests. The second behavioral response stage corresponds to the 12-24 hours of the user's application for policy cancellation operation. The user is provided with a limited-time discount and access to manual services. The third behavioral response stage corresponds to the 24-48 hours of the user's application for policy cancellation operation. The insurance business can be further upgraded or relevant alternatives can be sought.

[0037] Specifically, the decision buffer period proposed in this application is dynamically generated based on user behavior feature data, forming a dynamic intervention window. The decision buffer period can be divided into multiple behavior response stages in chronological order, effectively constructing a phased intervention mechanism. Each behavior response stage is used to match specific different behavior response strategies, thereby improving the effectiveness of the intervention.

[0038] In the embodiments of the present application, compared with the traditional system that does not set a buffer mechanism or only sets a fixed waiting time, the present application dynamically determines the decision buffer period based on user behavior feature data, can more flexibly adapt to user behavior differences, and divides the decision buffer period in chronological order to form a multi-stage response mechanism, avoids a single intervention method, and improves the effectiveness and timeliness of the intervention strategy. In summary, the present application constructs an intelligent intervention mechanism by setting a dynamic decision buffer period, which solves the problems of lack of buffer mechanism, delayed intervention timing, and single strategy in the existing technology, and improves the system's responsiveness to user behavior and intervention effect.

[0039] 103. Obtain the behavior response strategy corresponding to the behavior response stage, push multiple behavior response strategies to the user terminal in sequence during the decision buffer period, and obtain the user's operation decision result for the target business after the decision buffer period ends.

[0040] Among them, behavioral response strategy refers to the specific intervention content pushed to users at a specific time point, which is used to influence user behavioral decisions. Types include rights reminders, incentive guidance, and customer service. The theoretical basis for its generation is mainly based on user behavioral feature data, the user's current behavioral response stage, and the system's preset policy rule library; the user terminal is specifically the device used by the user, such as mobile APP, web platform, WeChat applet, etc., which serves as a medium for information push and user interaction and receives intervention strategies pushed by the system; the operation decision result refers to the operation decision finally made by the user at the end of the decision buffer period. The results include revocation of operations, such as cancellation of surrender; completion of operations, such as confirmation of surrender; or alternative operations, such as adjustment of policy terms, selection of family version of policy, etc., and alternative operations are usually completed during the decision buffer period.

[0041] Specifically, the corresponding behavioral response strategy is determined based on the behavioral response stage and pushed to the user terminal, which can reach users through multiple channels and improve the accessibility of intervention. The behavioral response strategy is pushed in stages according to chronological order, and a targeted intervention path is constructed. Finally, the operation decision results are obtained to judge the intervention effect, forming a complete closed-loop feedback mechanism.

[0042] In the embodiment of the present application, by pushing targeted behavioral response strategies in sequence according to the behavioral response stages during the decision buffer period, and obtaining the user's final operation decision result after the buffer period, an intelligent response mechanism with real-time intervention and closed-loop feedback capabilities is constructed, which effectively solves the problems of single intervention means, lack of feedback mechanism, high user churn rate, etc. in the existing technology, and significantly improves the system's intervention efficiency in user behavior and user experience quality.

[0043] The present invention provides a user behavior response method, device, storage medium and computer equipment, which improve the ability to identify user behavior. Specifically, by generating user operation identifiers, it can track and bind key user operations, and collect three types of user behavior feature data, thereby realizing early perception of user intentions and effectively focusing on the motivation and process of user behavior; and constructing a dynamic intervention mechanism, which determines the target decision buffer period based on user behavior feature data rather than a fixed waiting time, and determines corresponding different behavior response strategies at each behavior response stage, supports multiple strategy combinations, realizes phased intervention, and improves intervention effect; and sets a decision buffer period after the user performs a key operation to avoid the operation taking effect immediately, and continuously pushes intervention strategies during the decision buffer period, giving users the opportunity to rethink and adjust, thereby significantly reducing irrational cancellation behavior caused by emotional fluctuations and insufficient information cognition. The above method collects user behavior feature data and constructs a decision buffer period, combined with the intelligent push of phased response strategies, to achieve accurate identification and timely intervention of user behavior intentions before users perform key operations. It effectively solves the problems of difficult user churn control, single intervention methods, and lack of buffer mechanisms in high-interaction frequency business scenarios such as insurance, significantly improves user retention rate and customer service satisfaction, and ensures the stable operation of the enterprise.

[0044] The present application embodiment provides another method for responding to user behavior, such as Figure 2 As shown, the method includes the following steps:

[0045] 201. In response to a user's operation behavior for a target service, generate a user operation identifier, and collect user behavior feature data corresponding to the user operation identifier.

[0046] Among them, in response to the operation request submitted by the user for the target business, a user operation identifier is generated and an information filling page is displayed, and the operation trigger data corresponding to the user operation identifier is collected; in response to the filling operation on the information filling page triggered by the user, the operation behavior data corresponding to the user operation identifier is collected; in response to the filling information confirmation operation triggered by the user, the operation environment data corresponding to the target operation identifier is collected.

[0047] Specifically, taking the example of a user initiating a policy cancellation application through an insurance APP, first, after the user clicks the policy cancellation button, the system generates a unique user operation identifier and synchronously collects operation trigger data. The operation trigger data specifically includes the trigger time, device type, and operation path when the user clicks the policy cancellation button. Among them, the operation path is specifically: jump from the My Policy page to the policy details page, then enter the policy cancellation entrance, and finally the system displays the policy cancellation information filling page; secondly, the user enters the policy cancellation information filling page and starts filling in the content, such as the reason for cancellation and contact information, etc. In the process of the user filling in the information, the system continuously collects operation behavior data, including page dwell time, number of field modifications, mouse trajectory, and click hot zone analysis, that is, whether the help prompt icon is clicked repeatedly, and whether the page is closed and re-entered. This process is used to identify the user's hesitation. Hesitation, uncertainty or emotional fluctuations are marked as high intervention priority; finally, after the user completes the information filling and confirmation to submit the cancellation information, the system immediately blocks the final operation, enters the decision buffer period and synchronously collects operating environment data, including the current network status, device power, geographic location and time factors, to determine whether there are external interference factors, such as whether it is near the airport, there are changes in itinerary, etc.; in addition, according to the different cancellation processes set up by different insurance APPs, there may also be an operation for the user to confirm the receipt of the cancellation fee, that is, the user needs to select the receiving account and click the confirm receipt button. The system can also collect payment confirmation behavior data, including the frequency of account selection, that is, whether the bank account has been switched multiple times; the speed of the second confirmation click, that is, fast click or slow hesitation; and whether to check the historical cancellation records before confirming.

[0048] In the embodiment of the present application, the present application collects multi-dimensional behavioral feature data at key nodes such as when the user submits a policy cancellation request, fills in information, and confirms the operation, and combines it with a unique user operation identifier to track the behavioral path, thereby building a comprehensive perception system of user behavioral intentions, providing a solid foundation for the subsequent generation and push of intelligent intervention strategies, and effectively improving the system's response efficiency and user retention capabilities.

[0049] 202. Adjust the basic buffer period based on the user behavior characteristic data to determine the decision buffer period.

[0050] Among them, a preset basic buffer period is obtained; a user portrait factor is determined based on the user operation identifier, and the basic buffer period is adjusted according to the user portrait factor to obtain a first dynamic buffer period; a behavior data factor is determined based on the operation trigger data and the operation behavior data, and the basic buffer period is adjusted according to the behavior data factor to obtain a second dynamic buffer period; an external environment factor is determined based on the operation environment data, and the basic buffer period is adjusted according to the external environment factor to obtain a third dynamic buffer period; a decision buffer period is determined based on the first dynamic buffer period, and / or the second dynamic buffer period, and / or the third dynamic buffer period.

[0051] Specifically, the basic buffer period is first obtained, which is set to 12 hours by default. The basic buffer period is the starting intervention window for all users to ensure that there is enough time for policy push; then the basic buffer period is adjusted based on different factors. On the one hand, the first dynamic buffer period is generated based on the user portrait factor. The user portrait factor is obtained by querying the historical portrait data according to the user operation identifier, including the remaining value of the policy, complete payment record, no overdue, user age, etc. When the user is determined to be a high-value customer, the basic buffer period is extended. The specific adjustment rule takes the policy value as an example. If the policy value is greater than the threshold, the basic buffer period is extended by 12 hours, and then the first dynamic buffer period is determined to be 24 hours, which can give high-value users more retention opportunities and avoid impulsive loss; on the other hand, the second dynamic buffer period is generated based on the behavioral data factor. The behavioral data factor is determined based on the operation trigger data and the operation behavior data, including the user's behavior trajectory on the cancellation page, such as staying on the page for 5 minutes, modifying the cancellation reason field 3 times, and clicking the help prompt icon multiple times. Finally, the hesitation index ( score) is 0.78, and the preset hesitation index threshold is 0.7. If the hesitation index is determined to be greater than the threshold, the basic buffer period is adjusted by multiplying the coefficient 1.5, and the second dynamic buffer period is determined to be 18 hours. This step identifies the user's hesitation state, extends the intervention window, and improves the retention success rate; thirdly, the third dynamic buffer period is generated based on external environmental factors. The external environmental factors are determined based on the operating environment data, including the time of 11 o'clock in the evening, heavy rain, and 10% device power. The external environmental factors are used to judge whether the user may be in an irrational decision-making state. The established adjustment rules are bad weather or In the case of low battery, the basic buffer period is extended by 12 hours, and the third dynamic buffer period is 24 hours. This step combines the external environment to judge whether the user is irrationally canceling the insurance, thereby improving the rationality of intervention; after determining the three dynamic buffer periods from three aspects, the three dynamic buffer periods can be superimposed to determine the final decision buffer period, or one dynamic buffer period can be selected from the three dynamic buffer periods as the final decision buffer period. For example, the maximum value of the dynamic buffer period is taken to determine the decision buffer period as 24 hours. Finally, the system will push rights reminders, preferential incentives, customer service intervention and other strategies in stages within 24 hours to guide users to re-evaluate their cancellation decisions

[0052] In the embodiment of the present application, the present invention generates multiple dynamic buffer periods based on user portrait factors, behavioral data factors and external environmental factors, and determines the final decision buffer period accordingly, thereby constructing an intelligent intervention mechanism, improving the system's response efficiency to user behavior and the intervention success rate, significantly reducing the incidence of irrational policy surrenders, and enhancing customer retention capabilities.

[0053] 203. Before the decision buffer period begins, push decision-making assistance information to the user terminal, where the decision-making assistance information includes rights issuance information and operation behavior prompt information for the target business.

[0054] Specifically, before entering the decision buffer period, that is, the moment the user clicks the cancellation button, decision-making auxiliary information can be pushed to the user terminal to enter the pre-retention stage. The rights issuance information is specifically used to display the rights that have been enjoyed but not used, such as "the current remaining unused rights value of the policy is 3,500 yuan", as well as operation behavior prompt information for the target business, which is used to remind the user of the possible consequences of the current operation behavior, such as "If you cancel the policy, the 3,500 yuan rights will be permanently invalidated", "If you cancel the policy, you will lose an annual health check-up worth 2,000 yuan", etc. In addition, it also includes personalized service recommendations to enhance the intervention effect.

[0055] In an embodiment of the present application, decision-making assistance information including rights issuance information and operation behavior prompt information is pushed to the user terminal before the decision buffer period begins, forming a proactive intelligent intervention mechanism, which effectively improves the user's awareness of the consequences of policy cancellation and reduces the probability of impulsive policy cancellation by the user.

[0056] 204. The decision buffer period is divided into a cognitive stage, an intervention stage, and a guidance stage based on a chronological order, and the behavioral response strategy corresponding to each action response stage is pushed to the user terminal in sequence.

[0057] Among them, in the recognition stage, the rights and interests issuance information that the user has enjoyed in the target business is determined based on the user identity information, and the rights and interests issuance information is pushed to the user terminal; in the intervention stage, the business preferential information that matches the user in the target business is identified based on the user identity information, the business preferential information is pushed to the user terminal, and a service access portal for the target business is established; in the guidance stage, the business adjustment information that matches the user in the target business is identified based on the user identity information, the business adjustment information is pushed to the user terminal, and a business adjustment portal for the target business is established, wherein the business adjustment portal is used to adjust the target business according to the business adjustment information.

[0058] In the embodiment of the present application, first, in the awareness stage, the user's awareness of existing benefits is awakened to reduce the probability of impulsive policy cancellation. Specifically, the user's identity information is retrieved based on the user's operation identifier, the record of the benefits already enjoyed by the user in the target policy is queried, and benefit issuance information is pushed, such as "The current policy has provided a cumulative claim amount of 18,000 yuan and one expert outpatient appointment service." Secondly, in the intervention stage, incentives are used to enhance user retention intentions and provide immediate retention paths. Specifically, matching business preferential information is identified based on the user profile, such as "Current renewal can enjoy a 20% discount." A manual service access portal is established to push a dedicated customer service channel, and exclusive customer contact information can be added. Finally, in the guidance stage, alternative solutions are provided to users to avoid complete policy cancellation. The user's historical behavior and preferences are specifically analyzed, and relevant business adjustment information is pushed. A business adjustment portal is established to enable policy upgrades and term modifications. The specific content of the business adjustment information may include "The current policy is about to terminate, but it can be upgraded to a family sharing version policy, retaining the original benefits; or replaced with a lower-priced protection plan; or simply suspending payment, while the protection continues to be effective."

[0059] In the embodiment of the present application, an intelligent intervention mechanism is constructed by dividing the decision buffer period into the cognitive stage, the intervention stage and the guidance stage, and pushing rights reminders, preferential incentives and business adjustment suggestions in each stage in turn, which effectively improves the user's awareness of the consequences of policy cancellation and significantly reduces the incidence of irrational policy cancellation.

[0060] 205. Within a preset time before the end of the decision buffer period, push operation behavior prompt information to the user terminal and establish a service adjustment entry.

[0061] In the embodiment of the present application, taking the decision buffer period of 24 hours as an example, 2 hours before the end of the decision buffer period, operation behavior prompt information is pushed through the APP pop-up notification and SMS dual-channel push, such as "remaining unused claim amount of 5,000 yuan", "1 expert outpatient service scheduled but not used", and a view details button can also be provided to jump to the rights and interests details page. At the same time, a business adjustment entrance is established to provide a variety of alternative options, such as renewal with reduced amount and policy protection upgrade, to avoid users from executing policy cancellation in various forms.

[0062] 206. In response to the user's information viewing operation for the target service during the decision buffer period, generate a target behavior response strategy corresponding to the user behavior feature data based on preset strategy rules, and push the target behavior response strategy to the user terminal.

[0063] In an embodiment of the present application, the user has previously submitted a policy cancellation application, and the system has set a 24-hour decision buffer period. During this period, as long as the user logs into the system again and actively views the policy details page, the system will accurately identify this key behavior, and generate and push a personalized intervention strategy that matches the user's behavioral characteristics in real time. The viewing path is to jump from the homepage to my policy, and then jump to the coverage details. The page stays on the page for 2 minutes and 30 seconds. Click the rights display module and scroll to the bottom to determine that the user is still interested in the current policy content and there is a potential for recovery. Then, a target behavior response strategy is generated based on the policy rules, and then a target behavior response strategy is generated according to the preset policy rules. For example, if the user views the policy details within the decision buffer period, a targeted intervention mechanism will be triggered. For example, when the user's historical policy value is high, premium reduction discounts will be pushed. When the user has recently hesitated many times, flexible payment methods will be provided.

[0064] 207. After the decision buffer period ends, obtain the user's operation decision result for the target business.

[0065] Among them, the operation decision results include canceling the operation behavior and completing the operation behavior; when the operation decision result is to cancel the operation behavior, the user's operation behavior for the target business is terminated, the task queue associated with the user operation identifier is cleared, the operation cancellation prompt information is pushed to the user terminal, and the user behavior feature data and the operation decision result are recorded; when the operation decision result is to complete the operation behavior, the user identity is verified and the user's operation behavior for the target business is executed, the operation completion prompt information is pushed to the user terminal, and the user behavior feature data and the operation decision result are recorded.

[0066] Specifically, if the user clicks the Confirm Surrender button, it is determined to be a completed operation; if the user clicks the Cancel Surrender button, it is determined to be a revoked operation; if the operation decision result is to revoke the operation, the system terminates the current surrender process and clears all task queues associated with the user operation identifier, such as unfinished policy push, customer service appointments, etc., and pushes prompt information to the user terminal, records user behavior feature data and operation decision results, which can be used for model training and strategy optimization; if the operation decision result is to complete the operation, the system performs a secondary verification of the user identity. After the verification is passed, the surrender operation is officially executed, and a prompt information is pushed to the user terminal, and the complete user behavior path, operation nodes, environmental data and final decision results are recorded for subsequent analysis and model optimization.

[0067] In the embodiment of the present application, by classifying and processing the user's operation decision results after the decision buffer period, clearing the task queue and pushing prompt information when the operation is canceled, and performing identity authentication and executing the operation when the operation is completed, a closed-loop intelligent response mechanism is constructed, which effectively improves the system response efficiency and user retention ability.

[0068] Further, as Figure 1 The specific implementation of the method, the embodiment of the present application provides a user behavior response device, such as Figure 3 As shown, the device includes: a data collection module 301, a buffer period determination module 302 and a strategy pushing module 303.

[0069] The data collection module 301 is used to generate a user operation identifier in response to a user's operation behavior for a target service, and collect user behavior feature data corresponding to the user operation identifier, wherein the user behavior feature data includes operation trigger data, operation behavior data, and operation environment data;

[0070] A buffer period determination module 302 is configured to determine a decision buffer period based on user behavior feature data, wherein the decision buffer period includes multiple behavior response stages divided based on time sequence;

[0071] The policy push module 303 is used to obtain the behavior response policy corresponding to the behavior response stage, push multiple behavior response policies to the user terminal in sequence during the decision buffer period, and obtain the user's operation decision result for the target service after the decision buffer period ends.

[0072] In a specific application scenario, the data collection module 301 can be specifically used to respond to an operation request submitted by a user for a target business, generate a user operation identifier and display an information filling page, and collect operation trigger data corresponding to the user operation identifier; respond to a filling operation on the information filling page triggered by a user, collect operation behavior data corresponding to the user operation identifier; respond to a filling information confirmation operation triggered by a user, collect operation environment data corresponding to the target operation identifier.

[0073] In a specific application scenario, the buffer period determination module 302 can be specifically used to obtain a preset basic buffer period; determine a user portrait factor based on a user operation identifier, and adjust the basic buffer period according to the user portrait factor to obtain a first dynamic buffer period; determine a behavior data factor based on operation trigger data and operation behavior data, and adjust the basic buffer period according to the behavior data factor to obtain a second dynamic buffer period; determine an external environment factor based on operation environment data, and adjust the basic buffer period according to the external environment factor to obtain a third dynamic buffer period; determine a decision buffer period based on the first dynamic buffer period, and / or the second dynamic buffer period, and / or the third dynamic buffer period.

[0074] In a specific application scenario, the user operation identifier includes user identity information, and the decision buffer period includes a recognition stage, an intervention stage, and a guidance stage divided in chronological order; the policy push module 303 can be specifically used to determine, in the recognition stage, the rights and interests issuance information that the user has enjoyed in the target business based on the user identity information, and push the rights and interests issuance information to the user terminal; in the intervention stage, identify the business preferential information that matches the user in the target business based on the user identity information, push the business preferential information to the user terminal, and establish a service access entrance for the target business; in the guidance stage, identify the business adjustment information that matches the user in the target business based on the user identity information, push the business adjustment information to the user terminal, and establish a business adjustment entrance for the target business, wherein the business adjustment entrance is used to adjust the target business according to the business adjustment information.

[0075] In a specific application scenario, the strategy push module 303 can be used to push decision-making assistance information to the user terminal before the decision buffer period begins, where the decision-making assistance information includes rights issuance information and operation behavior prompt information for the target business; within the preset time before the end of the decision buffer period, the operation behavior prompt information is pushed to the user terminal and a business adjustment entry is established.

[0076] In a specific application scenario, the policy push module 303 can be used to respond to the user's information viewing operation for the target business during the decision buffer period, generate a target behavior response strategy corresponding to the user behavior feature data based on preset policy rules, and push the target behavior response strategy to the user terminal.

[0077] In a specific application scenario, the operation decision results include canceling the operation behavior and completing the operation behavior; the policy push module 303 can be specifically used to terminate the user's operation behavior for the target business when the operation decision result is to cancel the operation behavior, clear the task queue associated with the user operation identifier, push the operation cancellation prompt information to the user terminal, and record the user behavior feature data and the operation decision result; when the operation decision result is to complete the operation behavior, verify the user identity and execute the user's operation behavior for the target business, push the operation completion prompt information to the user terminal, and record the user behavior feature data and the operation decision result.

[0078] It should be noted that for other corresponding descriptions of the functional units involved in the user behavior response device provided in this embodiment, please refer to Figure 1 and Figure 2 The corresponding description in will not be repeated here.

[0079] Based on the above Figure 1The method shown, accordingly, this embodiment also provides a storage medium, which stores a computer program, and when the program is executed by a processor, it implements the above-mentioned method for responding to user behavior.

[0080] Based on this understanding, the technical solution of the present application can be embodied in the form of a software product. The software product to be identified can be stored in a non-volatile storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute a response method for user behavior in each implementation scenario of the present application.

[0081] Based on the above Figure 1 and Figure 2 The method shown, and Figure 3 The embodiment of the device for responding to user behavior shown in FIG. Figure 4 As shown, this embodiment also provides a physical device for responding to user behavior. The device includes a communication bus, a processor, a memory, and a communication interface. It may also include an input / output interface and a display device. The various functional units can communicate with each other via the bus. The memory stores a computer program, and the processor is configured to execute the program stored in the memory and perform the method for responding to user behavior in the above embodiment.

[0082] Optionally, the physical device may further include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a Wi-Fi module, etc. The user interface may include a display, an input unit such as a keyboard, etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a Wi-Fi interface), etc.

[0083] Those skilled in the art will understand that the structure of a user behavior responsive physical device provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or a combination of certain components, or different component arrangements.

[0084] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the physical device hardware and the software resources to be identified, supporting the execution of the information processing program and other software and / or programs to be identified. The network communication module is used to enable communication between components within the storage medium and with other hardware and software in the physical information processing device.

[0085] Through the description of the above implementation methods, those skilled in the art can clearly understand that this application can be implemented with the help of software plus the necessary general hardware platform, or it can be implemented through hardware. By applying the technical solution of this application, the ability to identify user behavior is improved. Specifically, by generating user operation identifiers, the tracking and binding of user key operations are achieved, and three types of user behavior feature data are collected to achieve early perception of user intentions, effectively focusing on the motivation and process of user behavior; and constructing a dynamic intervention mechanism to determine the target decision buffer period based on user behavior feature data, rather than a fixed waiting time, and determine corresponding different behavior response strategies at each behavior response stage, support multiple strategy combinations, achieve phased intervention, and improve intervention effects; and set a decision buffer period after the user performs a key operation to avoid the operation taking effect immediately, and continuously push intervention strategies during the decision buffer period, giving users the opportunity to think and adjust again, significantly reducing irrational cancellation behavior caused by emotional fluctuations and insufficient information cognition. The above method collects user behavior feature data and constructs a decision buffer period, combined with the intelligent push of phased response strategies, to achieve accurate identification and timely intervention of user behavior intentions before users perform key operations. It effectively solves the problems of difficult user churn control, single intervention methods, and lack of buffer mechanisms in high-interaction frequency business scenarios such as insurance, significantly improves user retention rate and customer service satisfaction, and ensures the stable operation of the enterprise.

[0086] Those skilled in the art will understand that the accompanying drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily required to implement the present application. Those skilled in the art will understand that the modules in the devices in the implementation scenario can be distributed in the devices of the implementation scenario according to the implementation scenario description, or can be changed accordingly and located in one or more devices different from the implementation scenario. The modules of the above-mentioned implementation scenario can be combined into one module, or can be further split into multiple sub-modules.

[0087] The serial numbers of the above application are for descriptive purposes only and do not represent the advantages or disadvantages of the implementation scenarios. The above disclosure only discloses several specific implementation scenarios of the present application, but the present application is not limited thereto. Any changes that can be conceived by those skilled in the art should fall within the scope of protection of the present application.

Claims

1. A method for responding to user behavior, characterized in that: The method comprises: In response to a user's operation behavior for a target service, a user operation identifier is generated, and user behavior feature data corresponding to the user operation identifier is collected, wherein the user behavior feature data includes operation trigger data, operation behavior data, and operation environment data; Determining a decision buffer period based on the user behavior characteristic data, wherein the decision buffer period includes a plurality of behavior response stages divided based on a time sequence; Obtain the behavior response strategy corresponding to the behavior response stage, push multiple behavior response strategies to the user terminal in sequence during the decision buffer period, and obtain the user's operation decision result for the target business after the decision buffer period ends.

2. The method according to claim 1, characterized in that The step of generating a user operation identifier in response to a user's operation behavior for a target service and collecting user behavior feature data corresponding to the user operation identifier includes: In response to an operation request for the target service submitted by a user, a user operation identifier is generated, an information filling page is displayed, and operation trigger data corresponding to the user operation identifier is collected; In response to a filling operation on the information filling page triggered by a user, collecting operation behavior data corresponding to the user operation identifier; In response to a user-triggered information filling confirmation operation, operating environment data corresponding to the target operation identifier is collected.

3. The method according to claim 1, characterized in that The determining of the decision buffer period based on the user behavior feature data includes: Get the preset basic buffer period; Determining a user portrait factor based on the user operation identifier, and adjusting the basic buffer period according to the user portrait factor to obtain a first dynamic buffer period; determining a behavior data factor based on the operation trigger data and the operation behavior data, and adjusting the basic buffer period according to the behavior data factor to obtain a second dynamic buffer period; determining an external environmental factor based on the operating environment data, and adjusting the basic buffer period according to the external environmental factor to obtain a third dynamic buffer period; A decision buffer period is determined according to the first dynamic buffer period, and / or the second dynamic buffer period, and / or the third dynamic buffer period.

4. The method according to claim 1, wherein The user operation identifier includes user identity information, the decision buffer period includes a cognitive stage, an intervention stage, and a guidance stage divided in chronological order; obtaining a behavioral response strategy corresponding to the behavioral response stage, and sequentially pushing multiple behavioral response strategies to the user terminal within the decision buffer period, include: In the recognition stage, determining the rights and interests that the user has enjoyed in the target business based on the user identity information, and pushing the rights and interests information to the user terminal; During the intervention phase, identifying service preferential information matching the user in the target service based on the user identity information, pushing the service preferential information to the user terminal, and establishing a service access portal for the target service; During the boot phase, the business adjustment information matching the user in the target business is identified based on the user identity information, the business adjustment information is pushed to the user terminal, and a business adjustment entry for the target business is established, wherein the business adjustment entry is used to adjust the target business according to the business adjustment information.

5. The method according to claim 4, characterized in that The method further comprises: Before the decision buffer period begins, push decision-making assistance information to the user terminal, wherein the decision-making assistance information includes the rights issuance information and operation behavior prompt information for the target business; Within a preset time before the end of the decision buffer period, the operation behavior prompt information is pushed to the user terminal, and the service adjustment entry is established.

6. The method according to claim 1, characterized in that The pushing of the plurality of behavior response strategies to the user terminal in sequence during the decision buffer period includes: In response to the user's information viewing operation for the target service during the decision buffer period, a target behavior response strategy corresponding to the user behavior feature data is generated based on preset strategy rules, and the target behavior response strategy is pushed to the user terminal.

7. The method according to claim 1, characterized in that The operation decision results include canceling the operation behavior and completing the operation behavior; The obtaining of the user's operation decision result for the target service after the decision buffer period ends includes: When the operation decision result is the cancellation operation behavior, terminate the user's operation behavior for the target service, clear the task queue associated with the user operation identifier, push the operation cancellation prompt information to the user terminal, and record the user behavior feature data and the operation decision result; When the operation decision result is the completion of the operation behavior, the user identity is verified and the user's operation behavior for the target business is executed, an operation completion prompt message is pushed to the user terminal, and the user behavior feature data and the operation decision result are recorded.

8. A device for responding to user behavior, characterized in that: The device comprises: A data collection module, configured to generate a user operation identifier in response to a user's operation behavior for a target service, and collect user behavior feature data corresponding to the user operation identifier, wherein the user behavior feature data includes operation trigger data, operation behavior data, and operation environment data; a buffer period determination module, configured to determine a decision buffer period based on the user behavior characteristic data, wherein the decision buffer period includes a plurality of behavior response stages divided based on a time sequence; The strategy push module is used to obtain the behavior response strategy corresponding to the behavior response stage, push multiple behavior response strategies to the user terminal in sequence during the decision buffer period, and obtain the user's operation decision result for the target business after the decision buffer period ends.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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