Insurance process version dynamic shunting method and system based on user grouping

Abstract

The invention relates to the technical field of insurance businesses, in particular to an insurance flow version dynamic shunting method and system based on user grouping, and overcomes the defects of long experimental period and response delay of a traditional static A / B test by collecting conversion rate data in real time and dynamically adjusting the user shunting proportion. The high-conversion-rate version flow can be amplified in time, and the experiment efficiency and the insurance success rate are remarkably improved; meanwhile, flow distribution is performed by combining user grouping and a dynamic distribution proportion, so that personalized recommendation based on user attributes and real-time intentions is realized, dynamic adaptability is enhanced, and a technical basis is provided for deep fusion of insurance service rules through a grouping mechanism, so that the efficiency is improved, and the user experience is improved. And the accuracy and compliance of business operation are ensured.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of insurance business, in particular to a user group-based insurance process version dynamic shunting method and system. BACKGROUND

[0002] In the insurance technology field, improving the conversion rate of the insurance process version is one of the core technical goals, which lies in accurately identifying user needs and dynamically optimizing the page jump path. The current mainstream technical solutions mainly rely on two categories: one is the static A / B test framework, which allocates user traffic to different process versions according to a pre-set fixed ratio, and evaluates the effect after the experimental period ends; the other is an engine based on pre-defined IF-THEN rules, which recommends a jump path according to user static attributes or historical behavior data.

[0003] However, these existing technical solutions have obvious defects. First, the experimental efficiency is low: static A / B testing must wait for the complete experimental period (usually several days or even weeks) to draw a conclusion, which cannot respond in real time to the process version showing high conversion rate during the experiment, resulting in missed best promotion opportunities and potential loss of single loss. Second, poor dynamic adaptability: for the long decision chain scenario unique to insurance products, the solution based on static rules is difficult to capture the real-time changes in user behavior intentions, resulting in a serious mismatch between the recommended jump path and the user's dynamic needs. Finally, weak business targeting: the general technical solution fails to deeply integrate the particularity of the insurance business, for example, it does not consider the differentiated value of different insurance products, and lacks a verification mechanism for strict regional compliance requirements in the insurance industry, which poses a risk of insufficient business adaptation and compliance.

[0004] Therefore, the existing technology has three core bottlenecks: experimental response delay, dynamic strategy rigidity, and insufficient business adaptation, and there is an urgent need for an insurance process version shunting method that can deeply integrate insurance business rules and support real-time feedback and dynamic adjustment, to achieve the precision, compliance and efficiency of the optimization process. SUMMARY

[0005] The purpose of the present application is to provide a user group-based insurance process version dynamic shunting method and system to solve the problem of low experimental efficiency and response delay of static A / B testing in the optimization of insurance process versions.

[0006] To achieve the above purpose, the technical solution adopted by the present application is as follows: According to one aspect of the embodiments of this application, a method for dynamically allocating insurance application process versions based on user segmentation is provided, comprising: responding to a user's insurance application request, acquiring user attribute data and real-time behavior data; classifying users into predefined user segments according to the user attribute data and real-time behavior data; assigning corresponding insurance application process versions to users based on the user segment and the currently effective user allocation ratio; collecting and statistically analyzing conversion rate data for each insurance application process version in real time, and determining whether preset dynamic adjustment conditions are met; if the dynamic adjustment conditions are met, calculating and updating the user allocation ratio based on the conversion rate data, and allocating insurance application process versions to subsequent users according to the updated user allocation ratio.

[0007] Based on the aforementioned technical means, by collecting conversion rate data in real time and dynamically adjusting the user diversion ratio, the inherent defects of traditional static A / B testing, such as long experimental cycles and response delays, are effectively overcome. This allows for timely amplification of traffic from high-conversion-rate versions, thereby significantly improving experimental efficiency and overall conversion rates. Simultaneously, by combining user segmentation mechanisms with dynamic diversion strategies, personalized process recommendations based on user attributes and real-time behavioral intentions are achieved, enhancing the system's adaptability to dynamic user needs. Furthermore, this provides a technical foundation for deep integration with insurance business rules (such as regional compliance verification and differentiated weighting of insurance types), thus ensuring the accuracy and compliance of business processes while maximizing conversion efficiency.

[0008] Furthermore, based on user attribute data and real-time behavior data, users are divided into predefined user groups, including: obtaining the geographic location attribute from the user attribute data and matching the geographic location attribute with a pre-defined list of high-risk areas, where the list of high-risk areas is pre-configured according to the strictness of insurance regulatory policies in each region; if the match is successful, the user is determined to be in a pre-defined high-risk area, and the first grouping logic is executed: obtaining the form completion progress from the real-time behavior data, where the form completion progress is obtained by tracking the ratio of the number of required form fields completed by the user in the insurance application process version to the total number of required form fields; determining whether the form completion progress is greater than a first pre-defined threshold; if the form completion progress is determined to be greater than the first pre-defined threshold, the user is grouped into high-risk groups. The system first segments users by intention. If the form completion progress is less than or equal to a first preset threshold, the user is assigned to the high-risk user group. If no match is found, the user is determined not to be in a high-risk area, and the second segmentation logic is executed: The system retrieves page dwell time and form completion progress from real-time behavioral data; it determines whether the page dwell time is greater than a second preset threshold and whether the form completion progress is greater than a third preset threshold. If the page dwell time is greater than the second preset threshold and the form completion progress is greater than the third preset threshold, the user is assigned to the high-intent user group. If the page dwell time is less than or equal to the second preset threshold and / or the form completion progress is less than or equal to the third preset threshold, the user is assigned to the ordinary-intent user group.

[0009] Based on the aforementioned technical means, a refined real-time segmentation model is constructed by deeply integrating static user attributes such as geographical location with dynamic behavioral intentions such as form completion progress and page dwell time. This model can automatically identify and distinguish user groups with different characteristics, such as "high compliance requirements" and "high conversion potential." On the one hand, this mechanism ensures that users in high-risk areas are accurately guided to the insurance process version that includes mandatory compliance steps, thus avoiding regulatory risks from the source. On the other hand, it prioritizes high-intent users to the faster channel with a better experience, effectively improving the conversion rate. Thus, while ensuring business compliance, it achieves simultaneous optimization of traffic allocation efficiency and accuracy.

[0010] Further, determining whether the preset dynamic adjustment conditions are met includes: obtaining the current system time and retrieving the preset adjustment period, wherein the adjustment period defines the time interval for performing dynamic adjustments; calculating the time difference between the current time and the time point of the last successful dynamic adjustment; determining whether the time difference reaches or exceeds the adjustment period; if the time difference reaches or exceeds the adjustment period, it is determined that the dynamic adjustment conditions are met.

[0011] Based on the aforementioned technical means, by introducing an automated triggering mechanism based on a fixed time period, dynamic adjustment is transformed from an event-driven mode that relies on manual intervention or data fluctuations into a systematic time-driven mode, realizing the periodic and automated self-optimization of the traffic diversion strategy. This not only completely avoids the response delay problem of traditional A / B testing, which requires waiting for the entire experimental cycle to end before drawing conclusions, but also enables the system to respond quickly to changes in conversion rates at a preset rhythm and promptly amplify high-potential version traffic, thereby significantly improving experimental efficiency and resource utilization. At the same time, it ensures the controllability, predictability, and stability of the system's adjustment behavior, avoiding frequent strategy oscillations caused by instantaneous fluctuations in real-time data.

[0012] Furthermore, the user diversion ratio is calculated and updated based on conversion rate data, including: obtaining the real-time conversion rate of each insurance application process version within the current statistical window, where the real-time conversion rate is the ratio of the number of users who successfully completed the insurance application process version to the total number of users accessing the insurance application process version; determining the insurance product weight based on the type of insurance product carried by each insurance application process version; determining the weighted conversion rate corresponding to each insurance application process version based on the real-time conversion rate and insurance product weight of each insurance application process version through the weighted conversion rate calculation formula; and determining the user diversion ratio corresponding to each insurance application process version through the diversion ratio formula based on the weighted conversion rate and the sum of the weighted conversion rates of all insurance application process versions.

[0013] Based on the aforementioned technical means, by introducing insurance type weight coefficients and weighting their real-time conversion rates, a deeply integrated business value-oriented diversion ratio decision model was constructed. This model enables the diversion strategy to not only respond to the conversion efficiency of each process version but also accurately reflect the differentiated business value of different insurance products. The mechanism automatically maps the weighted conversion rate to the diversion ratio through a normalization algorithm, achieving the optimal allocation of traffic resources to the "high conversion + high value" combination, thereby ensuring the mathematical rationality and business adaptability of the diversion decision.

[0014] Furthermore, the insurance product weights are determined based on the insurance product types carried by each insurance application process version. This includes: obtaining the basic weight corresponding to the insurance product type according to a predefined insurance product weight configuration table; obtaining the current system time and determining whether the current time is within a preset target period, which includes holidays, large-scale promotional events, and high-value time slots during the day; if the current time is determined to be within the preset target period, the final effective insurance product weight is determined based on the current time and a preset time slot-weight mapping table, where the time slot-weight mapping table stores the mapping relationship between a specific time slot and the corresponding insurance product weight in the form of key-value pairs; if the current time is determined not to be within the preset target period, the basic weight is used as the final effective insurance product weight.

[0015] Based on the aforementioned technical means, by introducing a dynamic coupling mechanism between the time dimension and business strategy, the static basic weight of insurance products is intelligently associated with preset time factors such as high-value periods and promotional activity periods, realizing adaptive optimization of the diversion strategy in different time contexts. This mechanism can automatically identify key business periods and increase the weight priority of high-value insurance products, ensuring that traffic resources are tilted towards high-yield products during prime time. Thus, without manual intervention in the system, it significantly improves the overall input-output ratio and resource utilization of marketing activities, while enhancing the system's dynamic response capability to changes in the market environment and the accuracy of strategy execution.

[0016] Furthermore, based on user segmentation and the currently effective user traffic allocation ratio, a corresponding insurance application process version is assigned to each user. This includes: determining one or more candidate insurance application process versions based on the segmentation tags corresponding to the user segmentation; obtaining the currently effective user traffic allocation ratio, where the user traffic allocation ratio defines the proportion of user traffic that each candidate version should be allocated at the current moment; and using a consistent hashing algorithm to bucket user identifiers based on the currently effective user traffic allocation ratio, thereby assigning a unique target insurance application process version to the current user from the candidate insurance application process versions.

[0017] Based on the aforementioned technical means, by combining business-oriented user segmentation rules with data-driven traffic allocation strategies, while ensuring that users with high compliance requirements are accurately guided to secure processes, the uniformity, stability, and consistency of user sessions are achieved through consistent hashing algorithms. This mechanism not only ensures that the traffic allocation strategy strictly adheres to business compliance constraints and avoids regulatory risks caused by version misallocation, but also ensures the accuracy of experimental data and system load balancing in A / B testing through predictable and low-conflict hash bucketing technology. Thus, it maintains high performance and high reliability in traffic allocation decisions even under complex business rules.

[0018] Furthermore, before allocating insurance process versions to subsequent users based on the updated user diversion ratio, the method also includes: after calculating a preliminary user diversion ratio adjustment plan based on conversion rate data, identifying one or more target insurance process versions for which the diversion ratio is planned to be increased from the plan; for each target insurance process version to be increased, executing a real-time compliance verification process, specifically including: obtaining the user ID of the user to be diverted, and querying the corresponding user attribute data based on the user ID, wherein the user attribute data includes at least geographical location and age; using the user's geographical location, age, and the identifier of the target insurance process version as input, calling the compliance verification service, and verifying by querying the pre-set compliance rule library to determine whether the target insurance process version meets all mandatory compliance requirements for the corresponding user's location, insurance type, and user attributes; if the verification passes, it is determined that the target insurance process version meets the compliance requirements, and the diversion ratio increase operation is allowed; if the verification fails, it is determined that the target insurance process version does not meet the compliance requirements, and the diversion ratio increase for the target insurance process version is canceled.

[0019] Based on the aforementioned technical means, a business rule-driven security protection mechanism was constructed by embedding a real-time compliance verification step before the execution of the diversion strategy. This mechanism can automatically identify and block diversion operations that violate regional regulatory policies or specific user restrictions based on user location, age, and target process characteristics. This mechanism upgrades compliance verification from offline checks to real-time blocking in advance, effectively avoiding the risk of regulatory penalties caused by misallocation of process versions. It also ensures agile optimization of the diversion strategy within the business compliance framework through automated system judgment, achieving a balance between business security and experimental efficiency.

[0020] According to another aspect of the embodiments of this application, a dynamic diversion system for insurance application process versions based on user segmentation is also provided, comprising: a data acquisition module, used to acquire user attribute data and real-time behavior data in response to a user's insurance application request; a user segmentation module, used to divide users into predefined user segments according to the user attribute data and real-time behavior data; a version allocation module, used to allocate a corresponding insurance application process version to a user based on the user segment and the currently effective user diversion ratio; a data collection and statistics module, used to collect and statistically analyze the conversion rate data of each insurance application process version in real time; and a strategy decision module, used to determine whether preset dynamic adjustment conditions are met; when it is determined that the dynamic adjustment conditions are met, the user diversion ratio is calculated and updated according to the conversion rate data; wherein, the version allocation module is also used to allocate insurance application process versions to subsequent users according to the updated user diversion ratio.

[0021] According to another aspect of the embodiments of this application, an electronic device is also provided, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; wherein the memory is used to store a computer program; and the processor is used to execute the steps of the dynamic diversion method for the insurance application process version based on user grouping in any of the above embodiments by running the computer program stored in the memory.

[0022] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, wherein the storage medium stores a computer program, wherein the computer program is configured to execute the steps of the dynamic diversion method for the insurance application process based on user segmentation in any of the above embodiments when running.

[0023] The beneficial effects of this application are: This application overcomes the shortcomings of traditional static A / B testing, such as long testing cycles and response delays, by collecting conversion rate data in real time and dynamically adjusting user traffic allocation ratios. It can promptly amplify traffic from high-conversion-rate versions, significantly improving testing efficiency and insurance success rates. At the same time, by combining user segmentation with dynamic traffic allocation ratios for process distribution, it not only achieves personalized recommendations based on user attributes and real-time intent, enhancing dynamic adaptability, but also provides a technical foundation for deep integration with insurance business rules through the segmentation mechanism. Thus, while improving efficiency, it ensures the accuracy and compliance of business operations. Attached Figure Description

[0024] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0025] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a schematic diagram of the hardware environment for an optional user-segment-based insurance application process version dynamic diversion method provided in an embodiment of this application; Figure 2 This is a flowchart illustrating an optional dynamic diversion method for the insurance application process based on user segmentation, provided in an embodiment of this application. Figure 3 This is a structural block diagram of an optional user-segment-based insurance application process dynamic diversion system provided in an embodiment of this application; Figure 4This is a structural block diagram of an optional electronic device provided in an embodiment of this application. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] According to one aspect of the embodiments of this application, a method for dynamic routing of insurance application process versions based on user segmentation is provided. Optionally, in this embodiment, the above-mentioned method for dynamic routing of insurance application process versions based on user segmentation can be applied to a hardware environment consisting of a terminal and a server. The server is connected to the terminal via a network and can be used to provide services to the terminal or clients installed on the terminal. A database can be set up on the server or independently of the server to provide data storage services to the server.

[0030] The aforementioned network may include, but is not limited to, at least one of the following: wired network, wireless network. The aforementioned wired network may include, but is not limited to, at least one of the following: wide area network, metropolitan area network, local area network. The aforementioned wireless network may include, but is not limited to, at least one of the following: Wi-Fi (Wireless Fidelity), Bluetooth. The terminal may not be limited to PC, mobile phone, tablet computer, etc.

[0031] The user-segment-based dynamic traffic allocation method for the insurance application process in this application can be executed by a server, a terminal, or both. Specifically, the execution of this user-segment-based dynamic traffic allocation method for the insurance application process in this application can also be performed by a client installed on the terminal.

[0032] Taking the dynamic traffic diversion method for the insurance application process based on user segmentation in this embodiment, executed by the server, as an example, please refer to [link to relevant documentation]. Figure 1 , Figure 1 This is a schematic diagram of the hardware environment for an optional user-segment-based dynamic traffic diversion method for the insurance application process, as provided in an embodiment of this application. Figure 1 As shown, the hardware environment of this user-segment-based dynamic allocation method for insurance application process versions includes: a terminal 102 and a server 104 connected to the terminal 102 via a network. The server 104 is used to deploy and execute the relevant logic of the user-segment-based dynamic allocation method for insurance application process versions, including functions of modules such as data acquisition, user segmentation, version allocation, data collection and statistics, and strategy decision-making, to achieve dynamic optimization and allocation of insurance application process versions. The terminal 102 is used to send user insurance application requests to the server and display the corresponding insurance application process version page allocated by the server, while simultaneously collecting and feeding back real-time user behavior data.

[0033] The user-segment-based dynamic routing method for insurance application process versions in this embodiment can be applied to scenarios such as insurance application process version optimization in the field of insurance technology, for example: online insurance sales platforms, mobile insurance applications, and insurance company official website application process version management. This embodiment uses the optimization of the application process version of an online insurance sales platform as an example to illustrate the above-mentioned user-segment-based dynamic routing method for insurance application process versions.

[0034] Existing technologies have fundamental problems in optimizing insurance application processes, mainly manifested in low experimental efficiency and response delays. This is primarily because traditional static A / B testing requires waiting for the entire experimental cycle to draw conclusions, making it impossible to scale up traffic to high-conversion versions in real time, leading to missed promotion opportunities and potential sales losses. Simultaneously, existing technologies have poor dynamic adaptability, making it difficult to capture real-time changes in user behavior and intent, resulting in a mismatch between recommended paths and user needs. Furthermore, they lack business targeting, failing to deeply integrate with the characteristics of insurance business and lacking verification mechanisms for regional compliance requirements and differentiated insurance product value, resulting in insufficient business adaptation and compliance risks.

[0035] To address the aforementioned issues, this embodiment provides a dynamic traffic splitting method for the insurance application process based on user segmentation, running on the aforementioned server. Please refer to [link to relevant documentation]. Figure 2 , Figure 2 This is a flowchart illustrating an optional user-segment-based dynamic traffic diversion method for the insurance application process, as provided in an embodiment of this application. Figure 2 As shown in the figure, the dynamic diversion method for the insurance application process based on user segmentation in this application embodiment specifically includes the following steps: Step S201: In response to the user's insurance application request, obtain user attribute data and real-time behavior data; Step S202: Based on user attribute data and real-time behavior data, users are divided into predefined user groups; Step S203: Based on the user group and the current effective user diversion ratio, assign the corresponding insurance application process version to the user; Step S204: Collect and statistically analyze the conversion rate data of each insurance application process version in real time, and determine whether the preset dynamic adjustment conditions are met. Step S205: If the dynamic adjustment conditions are met, calculate and update the user diversion ratio based on the conversion rate data, and allocate the insurance process version to subsequent users according to the updated user diversion ratio.

[0036] Through steps S201 to S205, by collecting conversion rate data in real time and dynamically adjusting the user diversion ratio, the inherent defects of traditional static A / B testing, such as long experimental cycles and response delays, are effectively overcome. This allows for timely amplification of traffic from high-conversion-rate versions, thereby significantly improving experimental efficiency and overall conversion rate. Simultaneously, by combining user segmentation mechanisms with dynamic diversion strategies, personalized process recommendations based on user attributes and real-time behavioral intent are achieved, enhancing the system's adaptability to dynamic user needs. Furthermore, this provides a technical foundation for deep integration with insurance business rules (such as regional compliance verification and differentiated weighting of insurance types), thus ensuring the accuracy and compliance of business processes while maximizing conversion efficiency.

[0037] The following is combined with Figure 2 The method for dynamic diversion of insurance application process versions based on user segmentation in the embodiments of this application will be explained.

[0038] In the technical solution of step S201, in response to the user's insurance application request, user attribute data and real-time behavior data are obtained, specifically including: when the system detects that the user triggers a specific insurance application entry interaction event on the insurance product details page, the system generates an insurance application request and establishes an independent user session; the system obtains the basic attribute information of the logged-in user by calling the user center service interface, and simultaneously captures the user's interaction behavior sequence in the current session in real time through front-end tracking technology; the system associates the obtained structured attribute data and unstructured behavior event data with the same session ID, encapsulates them into a unified format context data packet, and transmits it to the downstream cluster decision module.

[0039] In this embodiment, a user's insurance application request refers to an action command initiated by the user expressing their intention to purchase an insurance product. This typically manifests as the user clicking the "Apply Now" button, submitting an application form, or issuing an application command via a voice assistant. This request is a structured event containing metadata such as a timestamp, user ID, device fingerprint, and insurance type ID. For example, if a user selects a "Three-Year Car Insurance Plan" in a mobile app and clicks the "Next" button, the system will generate an insurance application request containing information such as {user_id: "U12345", product_id: "P001", timestamp: "2024-06-15T10:30:00Z"}, send it to the server, and establish an independent session.

[0040] User attribute data refers to static or long-term stable data related to a user's identity, preferences, and risk characteristics, typically accumulated during user registration or historical interactions. This includes, but is not limited to, geographic location, age, gender, occupation, historical insurance records, and credit score. For example: Geographic location: The user's registered "permanent residence" is "City A, District B"; Age: The ID information associated with the user's account shows "35 years old"; Historical insurance records: The system database stores records of the user's purchases of "travel accident insurance" and "home insurance" over the past two years.

[0041] Real-time behavioral data refers to dynamic operational data generated by users in the current insurance application process, reflecting their real-time intentions and interaction status. This includes page browsing path, dwell time, form completion progress, and button click frequency. For example: Page dwell time: The user spent 2 minutes and 30 seconds on the "Health Declaration" page; Form completion progress: The user has completed the "Vehicle Owner Information" and "Vehicle Information" modules in the car insurance application form, but has not yet completed the "Policyholder Signature"; Click actions: The user repeatedly clicked the "Premium Calculation" button 3 times before finally clicking the "Submit" button.

[0042] As an optional implementation, the specific implementation process for obtaining user attribute data and real-time behavior data in response to a user's insurance application request is as follows: When a user accesses the insurance sales platform and initiates an insurance purchase, the system captures the user's insurance purchase request through the front-end interface.

[0043] At this time, the server-side data acquisition module starts synchronously. First, it extracts pre-stored user attribute data (such as geographical location, age, historical insurance records, etc.) from the request header or user session. At the same time, it collects user behavior data in the insurance process version in real time through the tracking technology (such as page dwell time, form filling progress, click operations, etc.).

[0044] For example, if a user visits the car insurance application page in region A, the system will record their geographical location as "A" and track their real-time progress in filling out the vehicle information form (e.g., 50% of the required fields have been completed).

[0045] These data are encrypted before being transmitted to the server, providing a foundation for subsequent user segmentation and dynamic traffic allocation. It should be noted that, in this embodiment, the specific steps for collecting user behavior data during the insurance application process using data tracking technology, as well as the specific steps for encrypting the acquired data, are not specifically limited; please refer to relevant content in existing technologies.

[0046] By employing the aforementioned technical means and multi-source data fusion technology, static attributes are combined with dynamic intent signals to provide a panoramic user profile for subsequent grouping decisions, effectively solving the problem of insufficient accuracy in path recommendation caused by the single data dimension in traditional solutions.

[0047] In the technical solution of step S202, users are divided into predefined user groups based on user attribute data and real-time behavior data.

[0048] In this embodiment, user segmentation is achieved by combining user attribute data and real-time behavioral data to divide users into predefined groups with similar characteristics or needs, thereby enabling precise process allocation and dynamic adaptation. Through multi-dimensional data fusion analysis, refined classification of user groups is achieved, providing a foundation for differentiated services. Based on geographical location risk level and real-time user intent strength, the system divides users into four core groups: high-risk / high-intent, high-risk, high-intent, and moderate-intent.

[0049] For example: High-risk area user segmentation: If a user's geographical location matches a preset list of high-risk areas (such as areas with strict insurance regulations), they are further divided into two categories based on their form completion progress: High-risk with high intent (completion progress > first preset threshold) and High-risk (completion progress ≤ first preset threshold). Ordinary area user segmentation: If a user is not in a high-risk area, they are divided into two categories based on page dwell time and form completion progress: High intent (dwell time > second preset threshold and completion progress > third preset threshold) and Ordinary intent (dwell time ≤ second preset threshold or completion progress ≤ third preset threshold).

[0050] As an optional implementation, users are divided into predefined user groups based on user attribute data and real-time behavior data. This includes: obtaining the geographic location attribute from the user attribute data and matching the geographic location attribute with a preset high-risk area list, wherein the high-risk area list is pre-configured based on the strictness of insurance regulatory policies in each region; if the match is successful, the user is determined to be in a preset high-risk area, and the first grouping logic is executed: obtaining the form completion progress from the real-time behavior data, wherein the form completion progress is obtained by tracking the ratio of the number of required form fields completed by the user in the insurance application process version to the total number of required form fields; determining whether the form completion progress is greater than a first preset threshold; if the form completion progress is determined to be greater than the first preset threshold, the user is grouped into a tag. Users are grouped into high-risk, high-intent user groups. If the form completion progress is less than or equal to the first preset threshold, the user is grouped into the high-risk user group. If no match is found, it is determined that the user is not in a high-risk area, and the second grouping logic is executed: obtain the page dwell time and form completion progress from real-time behavioral data; determine whether the page dwell time is greater than the second preset threshold, and at the same time determine whether the form completion progress is greater than the third preset threshold; if the page dwell time is greater than the second preset threshold and the form completion progress is greater than the third preset threshold, the user is grouped into the high-intent user group; if the page dwell time is less than or equal to the second preset threshold and / or the form completion progress is less than or equal to the third preset threshold, the user is grouped into the ordinary-intent user group.

[0051] In this embodiment, the high-risk area list is a pre-defined set of high-risk area codes based on regulatory requirements. It is determined according to regional risk ratings issued by insurance regulatory agencies and updated periodically, such as monthly.

[0052] Tags, also known as segmentation tags, are identifiers used to characterize the segment type to which users belong. Specific tag descriptions: High-risk, high-intention segment: Refers to users located in high-risk areas who show a strong intention to purchase insurance. High-risk segment: Refers to users located in high-risk areas whose intention to purchase insurance is not yet clear. High-intention segment: Refers to users in non-high-risk areas who show a clear intention to purchase insurance. Moderate-intention segment: Refers to users in non-high-risk areas with a moderate level of intention.

[0053] The form completion progress is the percentage of required fields that a user has completed out of the total number of required fields in the current insurance application process version. It can be calculated using the following formula: Form completion progress = (Number of completed required fields / Total number of required fields) × 100%.

[0054] Through the aforementioned segmentation mechanism, the flexible configuration of threshold parameters ensures both business compliance and real-time response to changes in user intent, effectively addressing the issues of simplistic user profiles and rigid strategies inherent in traditional solutions. The precise matching of each segmentation tag with subsequent process versions provides a scientific basis for dynamic traffic allocation.

[0055] In this embodiment, the grouping logic is a rule that divides users into specific groups based on a combination of user attributes and real-time behavior. This grouping logic includes two types: high-risk area grouping logic and ordinary area grouping logic.

[0056] High-risk area grouping logic: If the high-risk area list is successfully matched, the first grouping logic is executed, that is, if the form filling progress is greater than the first preset threshold, it is classified as high-risk and high-intention; otherwise, it is classified as high-risk.

[0057] Normal region segmentation logic: If matching the high-risk region list fails, execute the second segmentation logic, that is, if the page dwell time is greater than the second preset threshold and the form filling progress is greater than the third preset threshold, it is classified as high intention; otherwise, it is classified as normal intention.

[0058] Understandably, the first preset threshold is used to judge the form completion rate of users in high-risk areas. Optionally, the first preset threshold can be 50%. The second preset threshold is used to judge the page dwell time. Optionally, the second preset threshold can be 60 seconds. The third preset threshold is used to judge the form completion rate of users in non-high-risk areas. Optionally, the third preset threshold can be the same as the first preset threshold or different from the first preset threshold. For example, the third preset threshold is 30%.

[0059] Based on the aforementioned technical means, a refined real-time segmentation model is constructed by deeply integrating static user attributes such as geographical location with dynamic behavioral intentions such as form completion progress and page dwell time. This model can automatically identify and distinguish user groups with different characteristics, such as "high compliance requirements" and "high conversion potential." On the one hand, this mechanism ensures that users in high-risk areas are accurately guided to the insurance application process version that includes mandatory compliance steps, thus avoiding regulatory risks from the source. On the other hand, it prioritizes high-intent users to the faster channel with a better experience, effectively improving the success rate of the insurance application process. Thus, while ensuring business compliance, it achieves simultaneous optimization of traffic allocation efficiency and accuracy.

[0060] In the technical solution of step S203, based on the user group and the current effective user diversion ratio, a corresponding insurance process version is assigned to the user.

[0061] In this embodiment, the currently effective user traffic allocation ratio refers to the real-time user traffic proportion allocated by the system to each insurance application process version at a specific point in time. It is adjusted periodically based on real-time conversion rate data, such as updating every 5 minutes. Each ratio value corresponds to a specific process version identifier, and the sum of the traffic allocation ratios for all versions is constant at 100%.

[0062] The insurance application process refers to a set of ordered steps designed for users to complete the insurance purchase. For the same insurance product, there may be one or more different implementation schemes for the application process; that is, different versions of the application process exist for the same insurance product. Specifically, each version differs in interface design, number of steps, and interaction methods. Based on practical experience, one or more application process versions are pre-assigned to each user group.

[0063] For example: The simplified quick version (version A) includes 3 core steps, collapsed optional fields, and default recommended options, making it suitable for high-intent users and repeat customers. The standard guided version (version B) includes 5 standard steps, a complete form display, and appropriate guidance prompts, making it suitable for ordinary intent users and new users. The complete and compliant version (version C) includes 7 complete steps, a dual recording process, and mandatory reading time, making it suitable for users in high-risk areas and areas with strict regulatory requirements.

[0064] As an optional implementation, based on user segmentation and the currently effective user traffic splitting ratio, a corresponding insurance application process version is assigned to the user, including: determining one or more candidate insurance application process versions according to the segmentation tags corresponding to the user segmentation; obtaining the currently effective user traffic splitting ratio, wherein the user traffic splitting ratio defines the proportion of user traffic that each candidate version should be allocated at the current moment; and using a consistent hashing algorithm to bucket user identifiers according to the currently effective user traffic splitting ratio, thereby assigning a unique target insurance application process version to the current user from the candidate insurance application process versions.

[0065] In this embodiment, the group label is used as an identifier to represent the group type to which a user belongs. Specific label descriptions: High-risk, high-intention group: Refers to a user group located in a high-risk area who shows a strong intention to purchase insurance. High-risk group: Refers to a user group located in a high-risk area but whose intention to purchase insurance is not yet clear. High-intention group: Refers to a user group located in a non-high-risk area who shows a clear intention to purchase insurance. Moderate-intention group: Refers to a user group located in a non-high-risk area with a moderate level of intention.

[0066] Candidate insurance application process versions refer to a set of insurance application process versions that are pre-selected based on user segmentation tags and that comply with business rules and are allowed to be displayed to users in that segment.

[0067] The candidate application process version selection logic may include: the candidate application process version corresponding to high-risk and high-intention / high-risk subgroups is the full compliant version; the candidate application process version for high-intention subgroups is the simplified quick version and the standard guided version; the candidate application process version for ordinary intention subgroups is the standard guided version.

[0068] The target insurance application process version refers to the specific application process version ultimately selected from the candidate version set using a consistent hashing algorithm and actually displayed to the user. It involves precise traffic allocation based on the candidate version set and real-time traffic distribution ratios. For example, if the candidate set for a highly interested user is {version A, version B}, version A will be ultimately selected after hash bucketing.

[0069] It should be noted that for users in high-risk areas, due to business restrictions, only compliant versions of the candidate insurance application process are included, therefore the target version is mandatory as the compliant version. For ordinary users, the candidate insurance application process versions include multiple versions, and the target insurance application process version is dynamically determined by an algorithm. The specific steps for determining the candidate insurance application process version using consistent hashing bucketing in this embodiment are not specifically limited; please refer to the relevant content in the prior art.

[0070] Based on the aforementioned technical means, by combining business-oriented user segmentation rules with data-driven traffic allocation strategies, while ensuring that users with high compliance requirements are accurately guided to secure processes, the uniformity, stability, and consistency of user sessions are achieved through consistent hashing algorithms. This mechanism not only ensures that the traffic allocation strategy strictly adheres to business compliance constraints and avoids regulatory risks caused by version misallocation, but also ensures the accuracy of experimental data and system load balancing in A / B testing through predictable and low-conflict hash bucketing technology. Thus, it maintains high performance and high reliability in traffic allocation decisions even under complex business rules.

[0071] In the technical solution of step S204, the conversion rate data of each insurance application process version is collected and statistically analyzed in real time, and it is determined whether the preset dynamic adjustment conditions are met.

[0072] In this embodiment, conversion rate data refers to a set of quantitative indicators that measure the conversion effectiveness of each insurance application process version. Specifically, conversion rate data includes a multi-dimensional set of indicators: basic statistical indicators such as the number of users accessing the application process version (the number of unique users accessing the application process version within the statistical window, for example, 1,200 people), the number of successful applications (the number of users who completed the application and successfully paid, for example, 102 people), and the original conversion rate (calculated as the number of successful applications / the number of users accessing the application process × 100%, for example, 8.5%); time-dimensional data includes the statistical window identifier (such as the start and end time of a 1-hour scrolling window) and the data generation timestamp; in addition, it also includes version-related metadata (such as the application process version ID and insurance type). This structured data is generated in real time through a stream processing engine, providing comprehensive and reliable data support for dynamic traffic allocation decisions.

[0073] Dynamic adjustment conditions refer to the preset criteria for recalculating the traffic splitting ratio. Specifically, it refers to the time difference between the current time and the time of the last successful dynamic adjustment, and the duration during which the preset adjustment period is reached or exceeded.

[0074] As an optional embodiment, determining whether the preset dynamic adjustment conditions are met includes: obtaining the current system time and retrieving a preset adjustment period, wherein the adjustment period defines the time interval for performing dynamic adjustments; calculating the time difference between the current time and the time point of the last successful dynamic adjustment; determining whether the time difference reaches or exceeds the adjustment period; if the time difference reaches or exceeds the adjustment period, it is determined that the dynamic adjustment conditions are met.

[0075] In this embodiment, the adjustment period refers to a fixed time interval preset by the system to trigger the recalculation of the dynamic traffic splitting ratio. Its setting is the result of a trade-off between business needs, data science, and system performance. If the period is too short (e.g., 1 minute), the strategy may be adjusted based on unstable random fluctuations; if the period is too long (e.g., 1 hour), the effect of "real-time" optimization cannot be achieved. In insurance application scenarios, it typically takes several minutes for a user to complete the payment process from entering the process. A suitable time window helps to obtain more stable conversion rate data. Moreover, frequent full calculations can put pressure on data processing and decision engines, so an interval that is friendly to system resources needs to be set. In a preferred embodiment, the adjustment period is set to 5 minutes. Within this period, it is possible to capture significant increases in conversion rates due to marketing activities, time-of-day changes, or the attractiveness of new versions, thereby quickly amplifying traffic from high-value versions; it is also possible to accumulate a sufficient number of user visits and order samples, so that the calculated conversion rate has certain statistical significance, avoiding frequent strategy fluctuations due to the randomness of individual orders.

[0076] Based on the aforementioned technical means, by introducing an automated triggering mechanism based on a fixed time period, dynamic adjustment is transformed from an event-driven mode that relies on manual intervention or data fluctuations into a systematic time-driven mode, realizing the periodic and automated self-optimization of the traffic diversion strategy. This not only completely avoids the response delay problem of traditional A / B testing, which requires waiting for the entire experimental cycle to end before drawing conclusions, but also enables the system to respond quickly to changes in conversion rates at a preset rhythm and promptly amplify high-potential version traffic, thereby significantly improving experimental efficiency and resource utilization. At the same time, it ensures the controllability, predictability, and stability of the system's adjustment behavior, avoiding frequent strategy oscillations caused by instantaneous fluctuations in real-time data.

[0077] In the technical solution of step S205, if it is determined that the dynamic adjustment conditions are met, the user diversion ratio is calculated and updated based on the conversion rate data, and the insurance process version is assigned to subsequent users according to the updated user diversion ratio.

[0078] As an optional implementation, the user diversion ratio is calculated and updated based on conversion rate data, including: obtaining the real-time conversion rate of each insurance application process version within the current statistical window, wherein the real-time conversion rate is the ratio of the number of users who successfully completed the insurance application process version to the total number of users accessing the insurance application process version; determining the insurance type weight based on the type of insurance product carried by each insurance application process version; determining the weighted conversion rate corresponding to each insurance application process version through a weighted conversion rate calculation formula based on the real-time conversion rate and insurance type weight of each insurance application process version; and determining the user diversion ratio corresponding to each insurance application process version through a diversion ratio formula based on the weighted conversion rate and the sum of the weighted conversion rates of all insurance application process versions.

[0079] In this embodiment, the real-time conversion rate refers to the instantaneous conversion effect metric of a single insurance application process version within a specific statistical window. In practice, the real-time conversion rate is calculated based on a rolling time window (e.g., the past hour) and the real-time conversion rate formula. For the i-th insurance application process version, the real-time conversion rate formula is: CRi = (Ni / Sumi) × 100%, where Ni is the number of users who successfully completed the insurance application, Sumi is the total number of users who accessed the service, and CRi is the real-time conversion rate.

[0080] For example, if version A has a total of 1,200 users visiting between 10:00 and 11:00, and 102 users successfully purchase insurance, then version A's real-time conversion rate is 8.5%.

[0081] Insurance type weights are business value coefficients set based on the type of insurance product, used to adjust the importance of different insurance types in the triage decision. In practice, each insurance product is pre-configured with a basic insurance type weight. For example, the default weight for accident insurance is 1.2; the default weight for comprehensive medical insurance is 1.0; and the default weight for life insurance is 1.5. Insurance type weights can also be dynamically adjusted according to actual conditions. For example, during high-value periods (such as evening), the accident insurance weight automatically increases to 1.5.

[0082] The weighted conversion rate is a comprehensive evaluation indicator that adjusts the real-time conversion rate according to the insurance type weight. For the i-th insurance application process version, the weighted conversion rate calculation formula is: WCi = CRi × Wi, where WCi is the weighted conversion rate, CRi is the real-time conversion rate, and Wi is the insurance type weight. For example, if version A has a real-time conversion rate of 8.5% and its insurance type weight is 1.2, then its weighted conversion rate calculated using the above formula is 10.2%.

[0083] The split ratio formula is the core algorithm used to normalize the weighted conversion rate into a probability distribution.

[0084] For the i-th insurance application process version, the diversion ratio calculation formula is: Pi = WCi / SumWC, where WCi is the weighted conversion rate and SumWC is the sum of the weighted conversion rates of all versions.

[0085] In practice, for the i-th insurance application process version, its weighted conversion rate is calculated according to the weighted conversion rate formula WCi=CRi×Wi, where WCi is the weighted conversion rate, CRi is the real-time conversion rate, and Wi is the insurance type weight; the sum of the weighted conversion rates of all insurance application process versions is calculated, i.e., SumWC=Σ(WCi); for the i-th insurance application process version, its target diversion ratio is calculated according to the formula Pi=WCi / SumWC.

[0086] Based on the aforementioned technical means, by introducing insurance type weight coefficients and weighting their real-time conversion rates, a deeply integrated business value-oriented diversion ratio decision model was constructed. This model enables the diversion strategy to not only respond to the conversion efficiency of each process version but also accurately reflect the differentiated business value of different insurance products. The mechanism automatically maps the weighted conversion rate to the diversion ratio through a normalization algorithm, achieving the optimal allocation of traffic resources to the "high conversion + high value" combination, thereby ensuring the mathematical rationality and business adaptability of the diversion decision.

[0087] In the technical solution of step S205, if it is determined that the dynamic adjustment conditions are not met, the currently effective user diversion ratio will continue to be used to allocate the insurance process version to subsequent users.

[0088] In practice, all calculated target traffic splitting ratios are subject to constraint adjustments: it is determined whether the target traffic splitting ratio of each version is lower than the minimum guaranteed ratio of 5%; if there is a version with a target traffic splitting ratio lower than 5%, its traffic splitting ratio is increased to 5%, and the traffic splitting ratios of the remaining versions are reduced accordingly according to the original calculated ratios to ensure that the sum of all traffic splitting ratios is 100%; the final adjusted traffic splitting ratio is updated to the currently effective user traffic splitting ratio.

[0089] As an optional implementation, the insurance product weight is determined based on the insurance product type carried by each insurance application process version. This includes: obtaining the basic weight corresponding to the insurance product type according to a predefined insurance product weight configuration table; obtaining the current system time and determining whether the current time is within a preset target period, where the target period includes holidays, large-scale promotional events, and high-value time slots during the day; if the current time is determined to be within the preset target period, then the final effective insurance product weight is determined based on the current time and a preset time slot-weight mapping table, where the time slot-weight mapping table stores the mapping relationship between a specific time slot and the corresponding insurance product weight in the form of key-value pairs; if the current time is determined not to be within the preset target period, then the basic weight is used as the final effective insurance product weight.

[0090] In this embodiment, the insurance product weight configuration table is a predefined static parameter table that stores the benchmark weight coefficients corresponding to different insurance product types in a structured form. Its data comes from a comprehensive evaluation of product profit margin, strategic priority, and long-term value. For example, accident insurance is configured as 1.2 and life insurance is configured as 1.5, providing an initial basis for weight calculation.

[0091] The base weight refers to the fixed weight value used by the insurance product under normal circumstances. It serves as a benchmark for dynamic adjustment and is not affected by time factors. For example, accident insurance always uses a base weight of 1.2, which ensures the stability of the weight system under default scenarios.

[0092] The target period is a pre-defined set of special time periods, including statutory holidays, large-scale marketing campaigns, and daily high-value periods (such as 20:00-22:00). During these periods, a weight adjustment mechanism is triggered due to user activity or business strategy needs.

[0093] The time period-weight mapping table uses a key-value pair structure to store the mapping relationship between time conditions and weight coefficients. For example, the key "20:00-22:00" corresponds to the value {"Accident Insurance":1.5}, which enables the system to quickly obtain dynamically adjusted parameters through time matching.

[0094] The final effective insurance type weight is the actual usage weight after time dimension calibration. For example, the weight of accident insurance is increased from 1.2 to 1.8 during the evening period, and it will eventually participate in the diversion ratio calculation to achieve accurate adaptation of business strategy and time environment.

[0095] Based on the aforementioned technical means, by introducing a dynamic coupling mechanism between the time dimension and business strategy, the static basic weight of insurance products is intelligently associated with preset time factors such as high-value periods and promotional activity periods, realizing adaptive optimization of the diversion strategy in different time contexts. This mechanism can automatically identify key business periods and increase the weight priority of high-value insurance products, ensuring that traffic resources are tilted towards high-yield products during prime time. Thus, without manual intervention in the system, it significantly improves the overall input-output ratio and resource utilization of marketing activities, while enhancing the system's dynamic response capability to changes in the market environment and the accuracy of strategy execution.

[0096] As an optional embodiment, before allocating insurance process versions to subsequent users based on the updated user diversion ratio, the method further includes: after calculating a preliminary user diversion ratio adjustment plan based on conversion rate data, identifying one or more target insurance process versions for which the diversion ratio is planned to be increased from the plan; for each target insurance process version to be increased, executing a real-time compliance verification process, specifically including: obtaining the user ID of the user to be diverted, and querying the corresponding user attribute data based on the user ID, wherein the user attribute data includes at least geographical location and age; using the user's geographical location, age, and the identifier of the target insurance process version as input, calling the compliance verification service, and verifying by querying a pre-set compliance rule library to determine whether the target insurance process version meets all mandatory compliance requirements for the corresponding user's location, insurance type, and user attributes; if the verification passes, it is determined that the target insurance process version meets the compliance requirements, and the diversion ratio increase operation for it is allowed; if the verification fails, it is determined that the target insurance process version does not meet the compliance requirements, and the diversion ratio increase for the target insurance process version is canceled.

[0097] In this embodiment, compliance verification refers to verifying whether the target insurance application process version meets the mandatory compliance requirements of the user's location, insurance type, and attributes before traffic diversion. For example, if user C (17 years old) is assigned to the high-intent group, and the system verification finds that their age does not meet the underwriting requirements of a certain children's insurance, then the traffic diversion increase for that process will be canceled. User D (located in a high-risk area) is recommended to the simplified insurance application process version, but compliance rules require facial recognition to be completed in that area, so the system blocks this recommendation.

[0098] In this embodiment, the rule base stores at least the following mandatory compliance rules: Regional rules: Define the mandatory requirements for the insurance application process in different administrative regions, such as requiring designated regions to include the "dual recording" function; Insurance Product Rules: Defines the display and operational requirements for specific types of insurance products. For example, long-term life insurance requires a mandatory reading time for the full text of the terms and conditions. User rules: Define restrictive conditions based on user attributes, such as prohibiting the recommendation of high-risk investment-linked insurance products to users over the age of 60.

[0099] Based on the aforementioned technical means, a business rule-driven security protection mechanism was constructed by embedding a real-time compliance verification step before the execution of the diversion strategy. This mechanism can automatically identify and block diversion operations that violate regional regulatory policies or specific user restrictions based on user location, age, and target process characteristics. This mechanism upgrades compliance verification from offline checks to real-time blocking in advance, effectively avoiding the risk of regulatory penalties caused by misallocation of process versions. It also ensures agile optimization of the diversion strategy within the business compliance framework through automated system judgment, achieving a balance between business security and experimental efficiency.

[0100] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0101] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they 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 this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM (Read-Only Memory) / RAM (Random Access Memory), magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0102] According to another aspect of the embodiments of this application, a user-segment-based dynamic traffic allocation system for implementing the above-described user-segment-based dynamic traffic allocation method for insurance application process versions is also provided. Please refer to [link to relevant documentation]. Figure 3 , Figure 3This is a structural block diagram of an optional user-segment-based insurance application process dynamic diversion system provided in an embodiment of this application, such as... Figure 3 As shown, the system 300 may include: The data acquisition module 301 is used to acquire user attribute data and real-time behavior data in response to the user's insurance application request; User segmentation module 302 is used to divide users into predefined user segments based on user attribute data and real-time behavior data; Version allocation module 303 is used to allocate the corresponding insurance process version to users based on user grouping and the current effective user diversion ratio; The data acquisition and statistics module 304 is used to collect and statistically analyze the conversion rate data of each insurance application process version in real time. The strategy decision module 305 is used to determine whether the preset dynamic adjustment conditions are met; when it is determined that the dynamic adjustment conditions are met, the user diversion ratio is calculated and updated based on the conversion rate data.

[0103] It should be noted that, in this embodiment, the data acquisition module 301 can be used to execute the above-mentioned step S201, the user segmentation module 302 can be used to execute the above-mentioned step S202, the version allocation module 303 can be used to execute the above-mentioned step S203, the data collection and statistics module 304 can be used to execute the above-mentioned step S204, and the strategy decision-making module 305 can be used to execute the above-mentioned step S205. The version allocation module 303 is also used to allocate insurance process versions to subsequent users based on the updated user diversion ratio.

[0104] Regarding the user-segment-based insurance application process version dynamic diversion system in this embodiment, the specific manner in which the data acquisition module 301, user segmentation module 302, version allocation module 303, data collection and statistics module 304, and strategy decision-making module 305 execute the above-mentioned user-segment-based insurance application process version dynamic diversion method has been described in detail in the embodiments related to the user-segment-based insurance application process version dynamic diversion method, and will not be elaborated here.

[0105] It is understood that the technical solution provided in this embodiment, in the dynamic diversion system for the insurance application process based on user segmentation, effectively overcomes the inherent defects of traditional static A / B testing, such as long experimental cycles and response delays, by collecting conversion rate data in real time and dynamically adjusting the user diversion ratio. It can promptly amplify the traffic of high-conversion-rate versions, thereby significantly improving experimental efficiency and overall conversion rate. At the same time, by combining the user segmentation mechanism with the dynamic diversion strategy, it not only realizes personalized process recommendations based on user attributes and real-time behavioral intentions, enhancing the system's adaptability to dynamic user needs, but also provides a technical foundation for the deep integration of insurance business rules (such as regional compliance verification and differentiated weighting of insurance types), thus ensuring the accuracy and compliance of the business process while pursuing maximum conversion efficiency.

[0106] In addition to the modules described above, the apparatus in this embodiment may also include a module that executes any method in any of the embodiments of the dynamic diversion method for the insurance application process version based on user segmentation.

[0107] It should be noted that the examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the device, can operate in ways such as... Figure 1 The method shown can be implemented in either software or hardware within a hardware environment, where the hardware environment includes a network environment.

[0108] According to another aspect of the embodiments of this application, an electronic device is also provided for implementing the above-described dynamic diversion method for the insurance application process based on user segmentation. The electronic device may be a server, a terminal, or a combination thereof.

[0109] According to another embodiment of this application, an electronic device is also provided; please refer to [link to relevant documentation]. Figure 4 , Figure 4 This is a structural block diagram of an optional electronic device provided in an embodiment of this application, such as... Figure 4 As shown, the electronic device may include: a processor 1501, a communication interface 1502, a memory 1503, and a communication bus 1504, wherein the processor 1501, the communication interface 1502, and the memory 1503 communicate with each other through the communication bus 1504.

[0110] Memory 1503 is used to store computer programs; When processor 1501 executes the program stored in memory 1503, it performs the following steps: Step S201: In response to the user's insurance application request, obtain user attribute data and real-time behavior data; Step S202: Based on user attribute data and real-time behavior data, users are divided into predefined user groups; Step S203: Based on the user group and the current effective user diversion ratio, assign the corresponding insurance application process version to the user; Step S204: Collect and statistically analyze the conversion rate data of each insurance application process version in real time, and determine whether the preset dynamic adjustment conditions are met. Step S205: If the dynamic adjustment conditions are met, calculate and update the user diversion ratio based on the conversion rate data, and allocate the insurance process version to subsequent users according to the updated user diversion ratio.

[0111] It is understood that the technical solution provided in this embodiment, where the processor of the electronic device collects conversion rate data in real time and dynamically adjusts the user diversion ratio, effectively overcomes the inherent defects of traditional static A / B testing, such as long test cycles and response delays. It can promptly amplify the traffic of high-conversion-rate versions, thereby significantly improving test efficiency and overall conversion rate. At the same time, by combining user segmentation mechanisms with dynamic diversion strategies, it not only realizes personalized process recommendations based on user attributes and real-time behavioral intentions, enhancing the system's adaptability to dynamic user needs, but also provides a technical foundation for deep integration of insurance business rules (such as regional compliance verification and differentiated weighting of insurance types). Thus, while pursuing maximum conversion efficiency, it ensures the accuracy and compliance of business processes.

[0112] Optionally, in this embodiment, the communication bus can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. This communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is used to represent it in the figure, but this does not mean that there is only one bus or one type of bus. The communication interface is used for communication between the aforementioned electronic device and other devices.

[0113] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0114] The processor mentioned above can be a general-purpose processor, including but not limited to: CPU (Central Processing Unit), NP (Network Processor), etc.; it can also be DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0115] This application also provides a computer-readable storage medium, which includes a stored program, wherein the program executes the method steps of the above method embodiments when it runs.

[0116] Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, ROMs, RAMs, portable hard drives, magnetic disks, or optical disks.

[0117] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0118] If the integrated units in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in the aforementioned computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause one or more computer devices (which may be personal computers, servers, or network devices, etc.) to execute all or part of the steps of the methods of the various embodiments of this application.

[0119] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0120] In the several embodiments provided in this application, it should be understood that the disclosed client can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or the indirect coupling or communication connection of units or modules may be electrical or other forms.

[0121] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the solution provided in this embodiment, depending on actual needs.

[0122] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0123] The above are merely preferred embodiments of this application. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for dynamic traffic diversion in the insurance application process based on user segmentation, characterized in that, include: In response to a user's insurance application request, obtain user attribute data and real-time behavioral data; Based on the user attribute data and the real-time behavior data, the users are divided into predefined user groups; Based on the user grouping and the currently effective user diversion ratio, assign the corresponding insurance application process version to the user; Real-time collection and statistics of conversion rate data for each insurance application process version, and determination of whether the preset dynamic adjustment conditions are met; If the dynamic adjustment conditions are met, the user diversion ratio is calculated and updated based on the conversion rate data, and the insurance process version is assigned to subsequent users according to the updated user diversion ratio.

2. The method for dynamic diversion of insurance application process versions based on user segmentation according to claim 1, characterized in that, Based on the user attribute data and the real-time behavior data, the users are divided into predefined user groups, including: Obtain the geographic location attribute from the user attribute data, and match the geographic location attribute with a preset list of high-risk areas, wherein the list of high-risk areas is pre-configured based on the strictness of insurance regulatory policies in each region; If a match is successful, the user is determined to be in a preset high-risk area, and the first grouping logic is executed: obtain the form filling progress in the real-time behavior data, wherein the form filling progress is obtained by tracking the ratio of the number of required form fields completed by the user in the insurance process version to the total number of required form fields; determine whether the form filling progress is greater than a first preset threshold. If it is determined that the form completion progress is greater than the first preset threshold, then the user will be classified into a user group labeled as high-risk and high-intention. If it is determined that the form completion progress is less than or equal to the first preset threshold, then the user will be classified into a user group labeled as high-risk. If the matching fails, it is determined that the user is not in a high-risk area, and the second grouping logic is executed: obtain the page dwell time and form filling progress from the real-time behavior data; determine whether the page dwell time is greater than a second preset threshold, and at the same time determine whether the form filling progress is greater than a third preset threshold; If it is determined that the duration of the page stay is greater than the second preset threshold and the form filling progress is greater than the third preset threshold, then the user will be classified into a user group tagged as high intent. If it is determined that the duration of the page stay is less than or equal to the second preset threshold and / or the form completion progress is less than or equal to the third preset threshold, then the user will be classified into a user group labeled as having ordinary intentions.

3. The method for dynamic diversion of insurance application process versions based on user segmentation according to claim 1, characterized in that, Determine whether the preset dynamic adjustment conditions are met, including: Obtain the current system time and retrieve the preset adjustment period, wherein the adjustment period defines the time interval for dynamic adjustment; Calculate the time difference between the current time and the time when the dynamic adjustment was last successfully executed; Determine whether the time difference has reached or exceeded the adjustment period; If the time difference reaches or exceeds the adjustment period, it is determined that the dynamic adjustment conditions are met.

4. The method for dynamic diversion of insurance application process versions based on user segmentation according to claim 1, characterized in that, Calculate and update the user traffic splitting ratio based on the conversion rate data, including: Obtain the real-time conversion rate of each insurance application process version within the current statistics window, wherein the real-time conversion rate is the ratio of the number of users who successfully completed the insurance application process version to the total number of users accessing the insurance application process version; The weight of insurance products is determined based on the types of insurance products covered by each version of the insurance application process. Based on the real-time conversion rate and the insurance type weight of each insurance application process version, the weighted conversion rate corresponding to each insurance application process version is determined by the weighted conversion rate calculation formula. Based on the weighted conversion rate and the sum of the weighted conversion rates of all insurance application process versions, the user diversion ratio corresponding to each insurance application process version is determined by the diversion ratio formula.

5. The method for dynamic diversion of insurance application process versions based on user segmentation according to claim 4, characterized in that, The weight of insurance products is determined based on the types of insurance products covered by each version of the application process, including: Based on the insurance product type, the basic weight corresponding to the insurance product type is obtained according to the predefined insurance product weight configuration table; Obtain the current system time and determine whether the current time is within a preset target period, wherein the target period includes holidays, large-scale promotional events, and high-value time periods of the day; If it is determined that the current time is within the preset target period, the final effective insurance type weight is determined based on the current time and the preset time period-weight mapping table. The time period-weight mapping table stores the mapping relationship between a specific time period and the corresponding insurance type weight in the form of key-value pairs. If it is determined that the current time is not within the preset target period, then the basic weight will be used as the final effective insurance type weight.

6. The method for dynamic diversion of insurance application process versions based on user segmentation according to claim 1, characterized in that, Based on the user segmentation and the currently effective user diversion ratio, a corresponding insurance application process version is assigned to the user, including: Based on the segmentation tags corresponding to the user segments, determine one or more candidate insurance application process versions; Obtain the currently effective user traffic splitting ratio, wherein the user traffic splitting ratio defines the proportion of user traffic that should be allocated to each candidate version at the current moment; Based on the currently effective user diversion ratio, a consistent hashing algorithm is used to bucket user identifiers, thereby assigning a unique target insurance process version to the current user from the candidate insurance process versions.

7. The method for dynamic diversion of insurance application process versions based on user segmentation according to claim 1, characterized in that, Before allocating insurance process versions to subsequent users based on the updated user diversion ratio, the method further includes: After calculating a preliminary user diversion ratio adjustment plan based on the conversion rate data, one or more target insurance process versions with a planned increase in the diversion ratio are identified from the plan. For each of the target underwriting process versions proposed for improvement, a real-time compliance verification process will be executed, specifically including: Obtain the user ID of the user currently to be diverted, and query the corresponding user attribute data based on the user ID, wherein the user attribute data includes at least geographical location and age; Based on the user's geographical location, age, and the identifier of the target insurance application process version as input, the compliance verification service is invoked. By querying the pre-set compliance rule library, the verification is performed to determine whether the target insurance application process version meets all mandatory compliance requirements for the corresponding user's location, insurance type, and user attributes. If the verification passes, it is determined that the target insurance application process version meets the compliance requirements, and the operation to increase its diversion ratio is allowed. If the verification fails, it is determined that the target insurance application process version does not meet the compliance requirements, and the current increase in the diversion ratio for the target insurance application process version is cancelled.

8. A dynamic diversion system for the insurance application process based on user segmentation, characterized in that, include: The data acquisition module is used to respond to users' insurance application requests and acquire user attribute data and real-time behavior data; The user segmentation module is used to divide the user into predefined user segments based on the user attribute data and the real-time behavior data. The version allocation module is used to allocate the corresponding insurance process version to the user based on the user group and the current effective user diversion ratio; The data collection and statistics module is used to collect and statistically analyze the conversion rate data of each insurance application process version in real time. The strategy decision module is used to determine whether preset dynamic adjustment conditions are met; when it is determined that the dynamic adjustment conditions are met, the user diversion ratio is calculated and updated based on the conversion rate data. The version allocation module is also used to allocate insurance process versions to subsequent users based on the updated user diversion ratio.

9. An electronic device comprising a processor, a communication interface, a memory, and a communication bus, wherein, The processor, the communication interface, and the memory communicate with each other via the communication bus, characterized in that... The memory is used to store computer programs; The processor is configured to execute the dynamic triage method for the insurance application process based on user segmentation as described in any one of claims 1 to 7 by running the computer program stored in the memory.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, wherein the computer program is configured to execute the dynamic diversion method for the insurance application process based on user segmentation as described in any one of claims 1 to 7 when it is run.

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