Insurance process evaluation method and device, electronic equipment and storage medium

Abstract

The invention provides an insurance process evaluation method and device, electronic equipment and a storage medium, and the method comprises the steps: analyzing data indexes of different process stages in an insurance process, and determining monitoring data indexes corresponding to the different process stages; performing point burying processing in different process stages, and acquiring monitoring data generated by the target user in the insurance process by using process burying points; monitoring data generated by the target user in the insurance buying process are sorted and summarized, target monitoring data are generated, data analysis is carried out on the target monitoring data, and the insurance buying experience of the target user is evaluated, so that the insurance buying process is optimized. Compared with the prior art, the embodiment of the invention has the advantages that each stage can be accurately monitored; according to the method, point burying processing is carried out in different process stages, the monitoring data generated by the target user in the insurance process is obtained by utilizing the process burying points, and based on the data, the insurance experience of the target user can be objectively evaluated, and data support is provided for optimizing the insurance process.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a method and device, electronic equipment and storage medium for evaluating an insurance process. Background Art

[0002] In the past, data analysis of the plan preparation and insurance application process was obviously insufficient. On the one hand, only individual data indicators were collected, and the overall core process of the plan was not fully monitored and data collected; on the other hand, no full-process data analysis mechanism was established for this scenario, resulting in the inability to accurately identify the popularity of life insurance products, target population characteristics, various behavioral characteristics and conversion conditions within the user behavior cycle, etc. This lack of data has seriously restricted insurance companies' in-depth understanding of the plan preparation and user insurance application process, and also hindered the ability to convert process data into more potential customers, insured customers, and renewal customers.

[0003] Specifically, the traditional data analysis methods have the following main problems: First, data collection is not comprehensive, resulting in the inability to form a complete data chain and fully reflect the real behavior of users in the plan preparation and insurance process; second, data analysis is not in-depth, lacking refined analysis for specific scenarios and business processes, and unable to accurately identify pain points and optimization space in the process; third, data application is insufficient, and the data analysis results are not closely integrated with business operations and product optimization, resulting in the inability to effectively transform data analysis results into actual productivity. How to optimize the plan preparation and user insurance process and improve business operations and product experience has become an urgent problem to be solved. Summary of the invention

[0004] The present disclosure provides a method and device, electronic device and storage medium for evaluating an insurance process, the main purpose of which is to solve the problem of inaccurate evaluation of user insurance experience caused by incomplete data collection.

[0005] According to a first aspect of the present disclosure, a method for evaluating an insurance process is provided, comprising:

[0006] Analyze the data indicators of different process stages in the insurance process and determine the monitoring data indicators corresponding to the different process stages;

[0007] Perform tracking processing at different stages of the process, and use the process tracking to obtain monitoring data generated by the target user in the insurance process;

[0008] The monitoring data generated by the target user in the insurance process is sorted and summarized to generate target monitoring data, and data analysis is performed on the target monitoring data to evaluate the insurance experience of the target user in order to optimize the insurance process.

[0009] In some embodiments, the method further comprises:

[0010] The insurance process of the target product is analyzed, and the insurance process stages are divided into different process stages, wherein the process stages include: motivation stage, action stage and feedback stage.

[0011] In some embodiments, analyzing the data indicators of different process stages in the insurance application process to determine the monitoring data indicators corresponding to the different process stages includes:

[0012] Obtain historical user behavior data from different databases, analyze the historical user behavior data, and determine data indicators in the historical behavior data that are related to the insurance process;

[0013] The correlation between the data indicator and the insurance application process is calculated, and the monitoring data indicator in the historical behavior data is determined based on the correlation.

[0014] In some embodiments, the tracking processing is performed at different stages of the process, and the monitoring data generated by the target user in the insurance process is obtained by using the process tracking, including:

[0015] According to the target position of the front-end page where the monitoring data indicator is located, performing tracking processing at the target position of the front-end page to obtain the process tracking point;

[0016] Based on the process embedding point and the user identifier of the target user, the process data generated by the target user in the insurance process is extracted, and the process data is cleansed to obtain the monitoring data.

[0017] In some embodiments, the monitoring data generated by the target user in the insurance application process is collated and summarized to generate target monitoring data, and data analysis is performed on the target monitoring data to evaluate the insurance application experience of the target user so as to optimize the insurance application process, including:

[0018] Aggregating the monitoring data according to the target user and the process stage to generate the target monitoring data;

[0019] The target monitoring data is input into an evaluation model to evaluate the insurance process of the target customer.

[0020] In some embodiments, the method further comprises:

[0021] The historical user behavior data is processed to generate training data, and the evaluation model is trained based on the training data to obtain a trained evaluation model.

[0022] According to a second aspect of the present disclosure, a device for evaluating an insurance process is provided, comprising:

[0023] An analysis unit, used to analyze data indicators of different process stages in the insurance process, and determine monitoring data indicators corresponding to different process stages;

[0024] An acquisition unit, used to perform tracking processing at different stages of the process, and use the process tracking to acquire monitoring data generated by the target user in the insurance application process;

[0025] The evaluation unit is used to organize and summarize the monitoring data generated by the target user in the insurance process, generate target monitoring data, and perform data analysis on the target monitoring data to evaluate the insurance experience of the target user so as to optimize the insurance process.

[0026] In some embodiments, the apparatus further comprises:

[0027] The division unit is used to analyze the insurance process of the target product and divide the insurance process into different process stages, wherein the process stages include: motivation stage, action stage and feedback stage.

[0028] In some embodiments, the analysis unit comprises:

[0029] An analysis module, used to obtain historical user behavior data from different databases, analyze the historical user behavior data, and determine data indicators in the historical behavior data that are related to the insurance process;

[0030] A calculation module is used to calculate the correlation between the data indicator and the insurance process, and determine the monitoring data indicator in the historical behavior data based on the correlation.

[0031] In some embodiments, the acquisition unit includes:

[0032] A tracking unit, configured to perform tracking processing at a target position of the front-end page according to a target position of the front-end page where the monitoring data indicator is located, to obtain the process tracking point;

[0033] An extraction unit is used to extract the process data generated by the target user in the insurance process based on the process tracking point and the user identifier of the target user, and to clean the process data to obtain the monitoring data.

[0034] In some embodiments, the evaluation unit comprises:

[0035] A summary module, used to summarize the monitoring data according to the target user and the process stage to generate the target monitoring data;

[0036] The evaluation module is used to input the target monitoring data into the evaluation model to evaluate the insurance process of the target customer.

[0037] In some embodiments, the apparatus further comprises:

[0038] The training unit is used to process the historical user behavior data to generate training data, and train the evaluation model based on the training data to obtain a trained evaluation model.

[0039] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0040] at least one processor; and

[0041] a memory communicatively connected to the at least one processor; wherein,

[0042] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect.

[0043] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the method described in the first aspect.

[0044] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method as described in the first aspect above.

[0045] The present disclosure provides a method and device, electronic device and storage medium for evaluating the insurance process, which analyzes the data indicators of different process stages in the insurance process, determines the monitoring data indicators corresponding to the different process stages; performs point-of-sale processing at different process stages, and uses process point-of-sale to obtain the monitoring data generated by the target user in the insurance process; organizes and summarizes the monitoring data generated by the target user in the insurance process, generates target monitoring data, and performs data analysis on the target monitoring data to evaluate the insurance experience of the target user, so as to optimize the insurance process. Compared with the related art, the embodiment of the present disclosure can realize accurate monitoring of each stage by analyzing different process stages in the insurance process and determining the corresponding monitoring data indicators; such accurate monitoring helps to accurately capture the key nodes and potential problems in the insurance process; performs point-of-sale processing at different process stages, and uses process point-of-sale to obtain the monitoring data generated by the target user in the insurance process, based on which, the insurance experience of the target user can be objectively and comprehensively evaluated, providing data support for optimizing the insurance process.

[0046] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0048] Figure 1 A flowchart of a method for evaluating an insurance process provided by an embodiment of the present disclosure;

[0049] Figure 2 A flowchart of another method for evaluating an insurance process provided by an embodiment of the present disclosure;

[0050] Figure 3 A schematic diagram of the structure of a device for evaluating an insurance process provided by an embodiment of the present disclosure;

[0051] Figure 4 A schematic diagram of the structure of another insurance process evaluation device provided by an embodiment of the present disclosure;

[0052] Figure 5 A schematic block diagram of an exemplary electronic device provided for an embodiment of the present disclosure. DETAILED DESCRIPTION

[0053] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0054] The following describes the method and device, electronic device and storage medium for insurance process evaluation according to the embodiments of the present disclosure with reference to the accompanying drawings.

[0055] Figure 1 A flowchart of a method for evaluating an insurance process provided in an embodiment of the present disclosure.

[0056] like Figure 1 As shown, the method comprises the following steps:

[0057] Step 101, analyzing data indicators of different process stages in the insurance process, and determining monitoring data indicators corresponding to different process stages.

[0058] In the embodiments of the present disclosure, when analyzing each stage of the insurance application process, it is first necessary to subdivide the entire insurance application process to identify the specific performance and characteristics of each stage. Through a detailed analysis of each process stage, the corresponding monitoring data indicators of different process stages can be determined. Corresponding monitoring data indicators are specified for each stage, which can effectively reflect the customer's behavior and experience during the insurance application process.

[0059] For example, in the filling and making stage of insurance plan, key data indicators may include task completion time, insurance trial calculation time (if applicable), insurance transfer rate, operation error fields and frequency (pay attention to when formally insuring), gender / age / region / occupation distribution of insured persons (depending on specific needs), role / quantity distribution, product name / insurance type distribution, etc. These indicators can fully reflect the user's operation efficiency, preferences and possible problems at this stage.

[0060] After entering the formal insurance purchase process, monitoring data indicators may focus more on the user's willingness to pay and actual operations, such as page dwell time, function utilization, step conversion rate, payment success rate, etc. These indicators help to gain insight into the user's psychological and behavioral changes during the insurance decision-making process, as well as the factors that may hinder user conversion in the process.

[0061] When determining these monitoring data indicators, it is necessary to comprehensively consider business needs, user behavior characteristics, and the feasibility of data analysis. By setting monitoring indicators scientifically and reasonably, a solid foundation can be provided for subsequent data collection, analysis, and optimization, thereby improving the efficiency of the insurance process and user experience.

[0062] Step 102, performing tracking processing at different stages of the process, and using the process tracking to obtain monitoring data generated by the target user in the insurance process.

[0063] In the embodiments of the present disclosure, in order to systematically obtain the monitoring data generated by the target user in the process at different stages of the insurance process, point tracking is performed at different stages of the insurance process to obtain monitoring data. Point tracking, as a data collection technology, can accurately capture the user's behavior information at a specific link.

[0064] Specifically, a tracking strategy will be implemented at each key stage of the insurance process, such as the input of insurance information, the selection of insurance plans, the calculation of costs, payment confirmation, and feedback on successful insurance. These tracking points will be cleverly embedded in various interaction points of the user interface to ensure that every click, input, selection or jump behavior of the user can be fully and accurately captured.

[0065] By using these process tracking points, we can collect various monitoring data generated by target users during the insurance application process in real time. These data not only include basic information such as the time users stay at different stages, the frequency of visits, and the order of operations, but also may cover deeper information such as the user's preferences when facing different options, behavioral characteristics in the payment process, and emotional changes throughout the entire process.

[0066] By analyzing these monitoring data, we can gain a deeper understanding of the user's real experience in the insurance application process and identify bottlenecks and potential problems in the process. For example, if it is found that users frequently exit or show obvious confusion in a certain link, it can be inferred that there may be design deficiencies or unclear information communication in this link.

[0067] Step 103, collating and summarizing the monitoring data generated by the target user in the insurance process to generate target monitoring data, and performing data analysis on the target monitoring data to evaluate the insurance experience of the target user so as to optimize the insurance process.

[0068] In the embodiment of the present disclosure, the monitoring data obtained by the target user through point-of-care processing in the insurance application process is comprehensively and carefully sorted and summarized. This step aims to transform the massive and scattered raw data into target monitoring data with a clear structure and easy to analyze. Subsequently, the target monitoring data is deeply mined and carefully analyzed using data analysis techniques and tools to reveal the hidden laws, trends and potential problems therein.

[0069] Through data analysis, the target user's insurance experience can be evaluated from multiple dimensions, including but not limited to key indicators such as task completion time, operation error fields and frequency, function utilization, step conversion rate, etc. These evaluation results can provide valuable insights to help accurately identify bottlenecks in the insurance process, nodes with poor user experience, and potential optimization space.

[0070] Based on the above evaluation results, targeted optimization strategies for the insurance process can be formulated to improve user experience, increase conversion rates and promote business growth. This optimization process may involve adjustments to process design, improvements to interface interactions and enhancements to system functions, aiming to create a smoother, more efficient and enjoyable insurance experience for users.

[0071] The present disclosure provides a method for evaluating the insurance process, analyzing the data indicators of different process stages in the insurance process, determining the monitoring data indicators corresponding to the different process stages; performing point-of-sale processing at different process stages, and using process point-of-sale to obtain the monitoring data generated by the target user in the insurance process; collating and summarizing the monitoring data generated by the target user in the insurance process, generating target monitoring data, and performing data analysis on the target monitoring data to evaluate the insurance experience of the target user, so as to optimize the insurance process. Compared with the related art, the embodiment of the present disclosure can realize accurate monitoring of each stage by analyzing the different process stages in the insurance process and determining the corresponding monitoring data indicators; such accurate monitoring helps to accurately capture the key nodes and potential problems in the insurance process; performing point-of-sale processing at different process stages, and using process point-of-sale to obtain the monitoring data generated by the target user in the insurance process, based on these data, the insurance experience of the target user can be objectively and comprehensively evaluated, providing data support for optimizing the insurance process.

[0072] In order to clearly illustrate the embodiment of the present disclosure, this embodiment provides a flowchart of another method for evaluating an insurance process.

[0073] like Figure 2 As shown, the method comprises the following steps:

[0074] Step 201, analyzing the insurance process of the target product, and dividing the insurance process into different process stages, wherein the process stages include: motivation stage, action stage and feedback stage.

[0075] Specifically, in step 201, when conducting in-depth analysis and optimization on the insurance process of the target product, the entire insurance process is carefully divided into several key process stages, so as to more accurately capture and evaluate the user experience of each stage. Specifically, these process stages are divided into motivation stage, action stage and feedback stage, and each stage carries different user behaviors and business goals.

[0076] The motivation stage is the initial stage when users generate insurance demand. At this stage, users mainly gain a preliminary understanding and evaluation of insurance products through behaviors such as product details viewing, plan preparation, plan review, and premium calculation by salespersons. In order to fully and deeply understand user behavior at this stage, a series of key tracking monitoring parameters are set. These parameters include task completion time, which is used to evaluate the time required for users to complete a task from the beginning to the end; distribution of insured information to reveal the characteristics and preferences of different user groups; product information distribution to help understand which products are more popular or have potential market opportunities; function utilization rate, which is used to monitor the use of each function to identify which functions are more popular or need further optimization; and step conversion rate, which is used to evaluate the proportion of users who successfully enter the next step from the current step to reveal potential bottlenecks or obstacles in the process.

[0077] The action phase is the stage where users determine their insurance needs and start the actual insurance operation. In this stage, users need to fill in the insured person information, policyholder information, and select an insurance plan. In order to ensure the smooth progress of this process, a series of key tracking monitoring parameters are set. These parameters include total / subtask completion time, which is used to evaluate the time required for users to complete the entire task or a part of the task; operation error field / reason / frequency, which is used to identify common errors or problems in the user filling process for subsequent optimization; first task completion success rate, which is used to evaluate the proportion of users who can successfully complete the information when they first try to fill in the information; task abort and restart rate, which reveals the confusion that users may encounter during the filling process or the frequency of needing to return to check the information; insured person information distribution, legal / designated beneficiary selection ratio, payment method selection ratio, etc., are used to gain a deep understanding of users' specific needs and preferences; and step conversion rate, which is again used to evaluate the smoothness and user satisfaction in the process.

[0078] The feedback stage is the final stage where users read the terms, confirm the information and pay the premium to complete the insurance. At this stage, the user experience is also crucial. Therefore, a series of key tracking parameters are set to evaluate user behavior at this stage. These parameters include the reading time of the terms, which is used to evaluate the user's attention and understanding of the terms; function utilization rate, which is used to monitor the use of payment methods, electronic insurance policies, customer service and other functions to understand the user's satisfaction and dependence on these functions; step conversion rate, which is again used to evaluate the proportion of users who successfully enter the next step from the current step; product distribution, which is used to reveal which products are more popular or have potential market opportunities at this stage; task success rate, which is used to evaluate the proportion of users who finally successfully complete the insurance task; and drainage rate, which is used to evaluate the clicks and drainage of other products when the users who have completed the transaction are pushed to reveal potential market expansion opportunities.

[0079] Through the detailed division of these key process stages and the setting of key monitoring parameters, we can more accurately capture and evaluate the user's behavior and experience during the insurance process, and provide strong data support and decision-making basis for subsequent business operations and product optimization.

[0080] Step 202, obtaining historical user behavior data from different databases, and analyzing the historical user behavior data to determine data indicators in the historical behavior data that are related to the insurance process.

[0081] Specifically, in step 202, in the optimization of the insurance process and the analysis of user experience, a crucial link is to obtain historical user behavior data from different databases and conduct in-depth analysis and mining of these valuable data. This process not only requires the integration of resources from multiple databases to achieve comprehensive data collection, but also requires strong data processing and analysis capabilities to accurately identify data indicators closely related to the insurance process.

[0082] Specifically, it is necessary to extract historical data related to the insurance process from multiple data sources such as the company's user behavior database, transaction database, product database, etc. This data may cover every key step of the user's initial contact with insurance products, browsing product details, making insurance plans, and finally completing insurance.

[0083] After obtaining these historical user behavior data, we will use advanced data analysis technologies, such as data mining and machine learning, to conduct a detailed analysis of these data. Through this step, we can determine which data indicators can truly reflect the user's behavioral characteristics, preferences, and possible pain points in the insurance process. These data indicators may include but are not limited to the user's stay time at different stages, click-through rate, conversion rate, task completion rate, etc., as well as basic information such as the user's age, gender, and geographical distribution.

[0084] Through in-depth analysis of these data indicators related to the insurance process, we can more accurately understand the needs and pain points of users in the insurance process, thereby providing strong data support for subsequent process optimization and user experience improvement.

[0085] Step 203, calculate the correlation between the data indicator and the insurance process, and determine the monitoring data indicator in the historical behavior data based on the correlation.

[0086] Specifically, in step 203, in the analysis and optimization of the insurance process, the data processing steps need to be further refined to ensure that the data indicators closely related to the insurance process can be accurately identified. Specifically, this process involves calculating the correlation between the data indicators and the insurance process, and based on this correlation, accurately determining the monitoring data indicators that should be focused on in the historical behavior data.

[0087] To achieve this goal, we use statistical analysis methods to deeply process and mine the collected historical user behavior data. By calculating the correlation between each data indicator and each stage of the insurance process, we can clearly understand which data indicators have a significant impact on the insurance process. This step not only helps us exclude data indicators with low correlation with the insurance process, but also enables us to focus more on those data indicators that play a key role in optimizing the insurance process and improving user experience.

[0088] Based on the calculation results of the correlation, we will further select monitoring data indicators that are highly relevant to the insurance application process as the focus of subsequent analysis and optimization. These monitoring data indicators will cover multiple dimensions such as user behavior characteristics at different stages, conversion status, and the degree of utilization of various functions, providing strong data support for our comprehensive and in-depth understanding of the user experience in the insurance application process.

[0089] Step 204, according to the target position of the front-end page where the monitoring data indicator is located, a tracking point processing is performed at the target position of the front-end page to obtain the process tracking point.

[0090] Specifically, in step 204, in the in-depth analysis and optimization practice of the user experience of the insurance process, the tracking strategy is carefully planned and implemented based on the monitoring data indicators determined in the early stage. This process includes accurately identifying the target positions of the front-end page corresponding to each monitoring data indicator, which are usually the key nodes for users to interact with the page. Subsequently, tracking is scientifically performed at these target locations. This step requires a high degree of technical accuracy and a deep understanding of the user experience to ensure that the tracking can effectively capture user behavior data without interfering with the user's normal operating process. Through this refined tracking processing, the process tracking closely related to the insurance process was successfully obtained, providing a solid foundation for subsequent data analysis and process optimization.

[0091] Step 205, based on the process tracking point and the user identifier of the target user, extract the process data generated by the target user in the insurance process, and clean the process data to obtain the monitoring data.

[0092] Specifically, in step 205, in the process of quantitative evaluation and optimization of the user experience of the insurance process, the process tracking points set in the early stage are fully utilized. These tracking points can accurately capture each key behavior of the target user in the insurance process, and at the same time, combined with the user's unique identifier (i.e., user identifier), it can ensure that the collected process data is accurately attributed to each user. Next, in order to improve the accuracy and efficiency of data analysis, data cleaning work is performed on these original process data.

[0093] The data cleaning process covers many aspects, including removing duplicate data, correcting erroneous data, handling missing values, and unifying data formats. Using data processing technology and tools, the process data is comprehensively checked and corrected to ensure that each piece of data can truly and completely reflect the actual behavior of users in the insurance process.

[0094] Through this series of steps, we can finally obtain the cleaned and organized monitoring data. These data not only provide us with a comprehensive behavioral portrait of users in the insurance process, but also provide reliable data support for our subsequent data analysis and process optimization.

[0095] Step 206: Aggregate the monitoring data according to the target user and the process stage to generate the target monitoring data.

[0096] Specifically, in step 206, in the quantitative evaluation of comprehensively optimizing the user experience of the insurance process, the cleaned and sorted monitoring data are carefully summarized based on the identity information of the target users and the different stages they are in the insurance process. This step allows us to examine and analyze the specific performance and behavioral characteristics of each target user in each process stage such as plan preparation, information filling, clause reading, payment confirmation, etc. from a more macro and systematic perspective. By scientifically classifying and summarizing these monitoring data, we can eventually generate target monitoring data, which will provide a solid data foundation and decision-making basis for our subsequent user experience analysis and process optimization work.

[0097] Step 207: Input the target monitoring data into an evaluation model to evaluate the insurance application process of the target customer.

[0098] Specifically, in step 207, in the quantitative evaluation of user experience in the insurance process, the target monitoring data that has been summarized and sorted is imported into the pre-built evaluation model as key input information. This evaluation model is designed based on a multi-dimensional user experience quantitative evaluation system, which can conduct a comprehensive and in-depth evaluation of the insurance process of target customers. Through the calculation and analysis of the model, potential problems and bottlenecks in the insurance process can be accurately identified, such as low conversion rates in certain steps, too long user stay time, or frequent operation errors. This information is of great guiding significance for the subsequent optimization of the insurance process and improvement of user experience.

[0099] As an achievable method of an embodiment of the present disclosure, before using the evaluation model to evaluate and analyze the target monitoring data, the evaluation model needs to be trained. This can be done by, but is not limited to, the following methods: performing data processing on historical user behavior data to generate training data, and training the evaluation model based on the training data to obtain a trained evaluation model.

[0100] Specifically, before using the evaluation model to conduct an in-depth and detailed evaluation and analysis of the target monitoring data, the evaluation model is scientifically trained to ensure that it can accurately reflect the real situation of the user experience. Extensive collection and in-depth analysis of historical user behavior data, which contains detailed behavioral records of users in various links such as plan preparation and insurance application process, is the basis for learning the evaluation model. A series of sophisticated data processing operations are performed on these raw data, such as data cleaning, format conversion, feature extraction, etc., to generate high-quality training data. These training data not only accurately reflect the characteristics of user behavior, but also have sufficient diversity and representativeness to provide a solid foundation for learning the evaluation model. Based on these carefully prepared training data, the evaluation model is systematically trained. During the training process, the model will continue to learn and optimize, and gradually master the ability to extract key information from user behavior data and evaluate the quality of user experience. In the end, a fully trained evaluation model will have strong analytical capabilities and accuracy.

[0101] It should be noted that the embodiments of the present disclosure may include multiple steps. For the convenience of description, these steps are numbered, but these numbers do not limit the execution time slots or execution order between the steps; these steps can be implemented in any order, and the embodiments of the present disclosure do not limit this.

[0102] Corresponding to the above-mentioned insurance process evaluation method, the present invention also proposes an insurance process evaluation device. Since the device embodiment of the present invention corresponds to the above-mentioned method embodiment, the details not disclosed in the device embodiment can be referred to the above-mentioned method embodiment, and will not be repeated in the present invention.

[0103] Figure 3 A schematic diagram of the structure of a device for evaluating an insurance process provided by an embodiment of the present disclosure, such as Figure 3 As shown, including:

[0104] An analysis unit 31 is used to analyze data indicators of different process stages in the insurance process and determine monitoring data indicators corresponding to different process stages;

[0105] The acquisition unit 32 is used to perform tracking processing at different stages of the process, and use the process tracking to acquire the monitoring data generated by the target user in the insurance application process;

[0106] The evaluation unit 33 is used to organize and summarize the monitoring data generated by the target user in the insurance process, generate target monitoring data, and perform data analysis on the target monitoring data to evaluate the insurance experience of the target user so as to optimize the insurance process.

[0107] The present disclosure provides a device for evaluating the insurance process, which analyzes the data indicators of different process stages in the insurance process, determines the monitoring data indicators corresponding to the different process stages; performs point-of-sale processing at different process stages, and uses process point-of-sale to obtain the monitoring data generated by the target user in the insurance process; organizes and summarizes the monitoring data generated by the target user in the insurance process to generate target monitoring data, and performs data analysis on the target monitoring data to evaluate the insurance experience of the target user, so as to optimize the insurance process. Compared with the related art, the embodiment of the present disclosure can realize accurate monitoring of each stage by analyzing the different process stages in the insurance process and determining the corresponding monitoring data indicators; this accurate monitoring helps to accurately capture the key nodes and potential problems in the insurance process; performs point-of-sale processing at different process stages, and uses process point-of-sale to obtain the monitoring data generated by the target user in the insurance process. Based on these data, the insurance experience of the target user can be objectively and comprehensively evaluated, providing data support for optimizing the insurance process.

[0108] Furthermore, in a possible implementation of this embodiment, as Figure 4 As shown, the device also includes:

[0109] The division unit 34 is used to analyze the insurance process of the target product and divide the insurance process into different process stages, wherein the process stages include: motivation stage, action stage and feedback stage.

[0110] Furthermore, in a possible implementation of this embodiment, as Figure 4 As shown, the analysis unit 31 includes:

[0111] The analysis module 311 is used to obtain historical user behavior data from different databases, analyze the historical user behavior data, and determine data indicators related to the insurance process in the historical behavior data;

[0112] The calculation module 312 is used to calculate the correlation between the data indicator and the insurance process, and determine the monitoring data indicator in the historical behavior data based on the correlation.

[0113] Furthermore, in a possible implementation of this embodiment, as Figure 4 As shown, the acquisition unit 32 includes:

[0114] A tracking point module 321 is used to perform tracking point processing at the target position of the front-end page according to the target position of the front-end page where the monitoring data indicator is located, so as to obtain the process tracking point;

[0115] The extraction module 322 is used to extract the process data generated by the target user in the insurance process based on the process tracking point and the user identifier of the target user, and clean the process data to obtain the monitoring data.

[0116] Furthermore, in a possible implementation of this embodiment, as Figure 4 As shown, the evaluation unit 33 includes:

[0117] A summarizing module 331 is used to summarize the monitoring data according to the target user and the process stage to generate the target monitoring data;

[0118] The evaluation module 332 is used to input the target monitoring data into the evaluation model to evaluate the insurance process of the target customer.

[0119] Furthermore, in a possible implementation of this embodiment, as Figure 4 As shown, the device also includes:

[0120] The training unit 35 is used to process the historical user behavior data to generate training data, and train the evaluation model based on the training data to obtain a trained evaluation model.

[0121] It should be noted that the above explanation of the method embodiment is also applicable to the device of this embodiment, and the principle is the same, which is not limited in this embodiment.

[0122] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0123] Figure 5 A schematic block diagram of an example electronic device 400 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0124] like Figure 5As shown, the device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a ROM (Read-Only Memory) 402 or a computer program loaded from a storage unit 408 to a RAM (Random Access Memory) 403. In the RAM 403, various programs and data required for the operation of the device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An I / O (Input / Output) interface 405 is also connected to the bus 404.

[0125] A number of components in the device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0126] The computing unit 401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a CPU (Central Processing Unit), a GPU (Graphic Processing Units), various dedicated AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, a DSP (Digital Signal Processor), and any appropriate processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as the method for evaluating the insurance process. For example, in some embodiments, the method for evaluating the insurance process may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to execute the aforementioned insurance process evaluation method in any other appropriate manner (eg, by means of firmware).

[0127] Various embodiments of the systems and techniques described above herein may be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application Specific Standard Products), SOCs (System On Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that may be executed and / or interpreted on a programmable system including at least one programmable processor that may be a special purpose or general purpose programmable processor that may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0128] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0129] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a RAM, a ROM, an EPROM (Electrically Programmable Read-Only-Memory) or a flash memory, an optical fiber, a CD-ROM (Compact Dis sc Read-Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0130] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball), through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0131] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: LAN (Local Area Network), WAN (Wide Area Network), the Internet, and blockchain networks.

[0132] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0133] It should be noted that artificial intelligence is a discipline that studies how computers can simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), and includes both hardware-level and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing; artificial intelligence software technologies mainly include computer vision technology, speech recognition technology, natural language processing technology, as well as machine learning / deep learning, big data processing technology, knowledge graph technology, and other major directions.

[0134] The various numerical numbers such as first and second involved in the present disclosure are only for the convenience of description and are not used to limit the scope of the embodiments of the present disclosure, but also indicate the order of precedence.

[0135] At least one in the present disclosure may also be described as one or more, and a plurality may be two, three, four or more, which is not limited in the present disclosure. In the embodiments of the present disclosure, for a technical feature, the technical features in the technical feature are distinguished by "first", "second", "third", "A", "B", "C" and "D", etc., and there is no order of precedence or size between the technical features described by the "first", "second", "third", "A", "B", "C" and "D".

[0136] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0137] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for evaluating an insurance process, characterized in that: include: Analyze the data indicators of different process stages in the insurance process and determine the monitoring data indicators corresponding to the different process stages; Perform tracking processing at different stages of the process, and use the process tracking to obtain monitoring data generated by the target user in the insurance process; The monitoring data generated by the target user in the insurance process is sorted and summarized to generate target monitoring data, and data analysis is performed on the target monitoring data to evaluate the insurance experience of the target user in order to optimize the insurance process.

2. The method according to claim 1, characterized in that The method further comprises: The insurance process of the target product is analyzed, and the insurance process stages are divided into different process stages, wherein the process stages include: motivation stage, action stage and feedback stage.

3. The method according to claim 1, characterized in that: The analysis of data indicators at different stages of the insurance process to determine monitoring data indicators corresponding to different stages of the process includes: Obtain historical user behavior data from different databases, analyze the historical user behavior data, and determine data indicators in the historical behavior data that are related to the insurance process; The correlation between the data indicator and the insurance application process is calculated, and the monitoring data indicator in the historical behavior data is determined based on the correlation.

4. The method according to claim 1, characterized in that The tracking point processing is performed at different stages of the process, and the monitoring data generated by the target user in the insurance process is obtained by using the process tracking point, including: According to the target position of the front-end page where the monitoring data indicator is located, performing tracking processing at the target position of the front-end page to obtain the process tracking point; Based on the process embedding point and the user identifier of the target user, the process data generated by the target user in the insurance process is extracted, and the process data is cleansed to obtain the monitoring data.

5. The method according to claim 1, characterized in that: The monitoring data generated by the target user in the insurance application process is collated and summarized to generate target monitoring data, and the target monitoring data is analyzed to evaluate the insurance application experience of the target user so as to optimize the insurance application process, including: Aggregating the monitoring data according to the target user and the process stage to generate the target monitoring data; The target monitoring data is input into an evaluation model to evaluate the insurance process of the target customer.

6. The method according to claim 5, characterized in that The method further comprises: The historical user behavior data is processed to generate training data, and the evaluation model is trained based on the training data to obtain a trained evaluation model.

7. A device for evaluating an insurance process, characterized in that: include: An analysis unit, used to analyze data indicators of different process stages in the insurance process, and determine monitoring data indicators corresponding to different process stages; An acquisition unit, used to perform tracking processing at different stages of the process, and use the process tracking to acquire monitoring data generated by the target user in the insurance application process; The evaluation unit is used to organize and summarize the monitoring data generated by the target user in the insurance process, generate target monitoring data, and perform data analysis on the target monitoring data to evaluate the insurance experience of the target user so as to optimize the insurance process.

8. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-6.

10. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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