Data processing method and device, electronic equipment and computer readable storage medium

Abstract

The application relates to the technical field of data processing, and is applied to a financial insurance business scene, and provides a data processing method and device, an electronic device and a computer readable storage medium, the method comprises the following steps: acquiring multidimensional data; based on a multi-dimensional weight model, commission allocation analysis and processing are performed on agent data, customer data and rule data to obtain dynamic commission ratio information; based on a pre-trained logistic regression model, loss risk level prediction processing is performed on the agent data and external associated data to obtain loss risk prediction information; and personnel management strategy information is constructed according to the dynamic commission ratio information, income accounting information, agent incentive strategy information and loss risk prediction information. Through the technical scheme, a more reasonable and accurate incentive effect can be achieved.

Description

Technical Field

[0001] The embodiments of this application relate to, but are not limited to, the field of data processing technology, and in particular to a data processing method, apparatus, electronic device, and computer-readable storage medium. Background Technology

[0002] With the continuous development of society and economy and the advancement of science and technology, people's living standards have been continuously improved. To provide better life security, the insurance industry has also seen significant promotion and development. For example, in the medical industry, health insurance can be used to protect personal health; in the automotive sector, vehicle insurance can protect drivers, passengers, and vehicles. Currently, insurance business is generally promoted and sold through agents. To better incentivize agents, commission distribution mechanisms are typically used. However, existing commission distribution mechanisms often employ fixed percentages or tiered commission structures, failing to fully consider the actual contributions of agents and customer satisfaction, resulting in limited incentive effects. Summary of the Invention

[0003] The following is an overview of the subject matter described in detail herein. This overview is not intended to limit the scope of the claims.

[0004] To address the problems mentioned in the background section, embodiments of this application provide a data processing method, apparatus, electronic device, and computer-readable storage medium that can fully consider the actual contributions of agents and customer satisfaction during personnel assessment and management, thereby achieving a more reasonable and accurate incentive effect.

[0005] In a first aspect, embodiments of this application provide a data processing method, the data processing method comprising: Acquire multidimensional data, wherein the multidimensional data includes agent data, customer data, rule data, and external related data; Based on a pre-trained multi-dimensional weight model, commission allocation analysis is performed on the agent data, customer data, and rule data to obtain dynamic commission ratio information; and revenue calculation is performed based on the agent data and customer data to obtain revenue calculation information. Based on the agent data and the external related data, label segmentation processing is performed to obtain agent incentive strategy information; and based on the pre-trained logistic regression model, churn risk level prediction processing is performed on the agent data and the external related data to obtain churn risk prediction information. Based on the dynamic commission ratio information, the revenue calculation information, the agent incentive strategy information, and the churn risk prediction information, personnel management strategy information is constructed.

[0006] Secondly, embodiments of this application also provide a data processing apparatus, the data processing apparatus comprising: The acquisition unit is used to acquire multidimensional data, wherein the multidimensional data includes agent data, customer data, rule data, and external related data; The analysis unit is used to perform commission allocation analysis and processing on the agent data, customer data and rule data based on a pre-trained multi-dimensional weight model to obtain dynamic commission ratio information; and to perform revenue calculation based on the agent data and customer data to obtain revenue calculation information. The prediction unit is used to perform label segmentation processing based on the agent data and the external related data to obtain agent incentive strategy information; and to perform churn risk level prediction processing on the agent data and the external related data based on a pre-trained logistic regression model to obtain churn risk prediction information. The construction unit is used to construct personnel management strategy information based on the dynamic commission ratio information, the revenue calculation information, the agent incentive strategy information, and the churn risk prediction information.

[0007] Thirdly, embodiments of this application also provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the data processing method described in the first aspect above.

[0008] Fourthly, embodiments of this application also provide a computer-readable storage medium storing computer-executable instructions for performing the data processing method described in the first aspect above.

[0009] The data processing method according to the embodiments provided in this application has at least the following beneficial effects: In the process of personnel performance evaluation and management, firstly, multi-dimensional data is acquired, including agent data, customer data, rule data, and external related data; then, based on a pre-trained multi-dimensional weight model, commission allocation analysis is performed on the agent data, customer data, and rule data to obtain dynamic commission ratio information; and then, revenue calculation is performed based on the agent data and customer data to obtain revenue calculation information; next, labeling is performed based on the agent data and external related data to obtain agent incentive strategy information; and then, based on a pre-trained logistic regression model, churn risk level prediction is performed on the agent data and external related data to obtain churn risk prediction information; finally, personnel management strategy information is constructed based on the dynamic commission ratio information, revenue calculation information, agent incentive strategy information, and churn risk prediction information. Through the above technical solution, the actual contribution of agents and customer satisfaction can be fully considered in the process of personnel performance evaluation and management, thereby achieving a more reasonable and accurate incentive effect. Attached Figure Description

[0010] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0011] Figure 1 This is a schematic flowchart of a data processing method provided in one embodiment of this application; Figure 2 yes Figure 1 A schematic diagram of a specific implementation method of step S200; Figure 3 yes Figure 1 A schematic diagram of a specific implementation of step S300; Figure 4 yes Figure 1 A flowchart illustrating another specific implementation of step S300; Figure 5 yes Figure 1 A schematic diagram of a specific implementation of step S400; Figure 6 yes Figure 1 A flowchart illustrating another specific implementation of step S400; Figure 7 Is it completed? Figure 1 A flowchart illustrating a specific implementation method following step S400; Figure 8 This is a schematic diagram of a data processing apparatus provided in one embodiment of this application; Figure 9This is a schematic diagram of an electronic device provided in one embodiment of this application. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0013] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0014] It should be noted that, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0015] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0016] AI is a new technical science that studies and develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence. Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce new intelligent machines that can react in a way similar to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing, and expert systems. Artificial intelligence can simulate the information processes of human consciousness and thought. Furthermore, artificial intelligence utilizes digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceiving the environment, acquiring knowledge, and using that knowledge to achieve optimal results—the theories, methods, technologies, and application systems available for use.

[0017] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0018] Artificial intelligence, or AI, is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0019] The servers involved in artificial intelligence technology can be standalone servers or cloud servers that provide basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0020] This application provides a data processing method, apparatus, electronic device, and computer-readable storage medium. In the process of personnel performance evaluation and management, multidimensional data is first acquired, including agent data, customer data, rule data, and external correlation data. Next, based on a pre-trained multidimensional weight model, commission allocation analysis is performed on the agent data, customer data, and rule data to obtain dynamic commission ratio information. Then, revenue calculation is performed based on the agent data and customer data to obtain revenue calculation information. Next, labeling is performed on the agent data and external correlation data to obtain agent incentive strategy information. Finally, based on a pre-trained logistic regression model, churn risk level prediction is performed on the agent data and external correlation data to obtain churn risk prediction information. Finally, personnel management strategy information is constructed based on the dynamic commission ratio information, revenue calculation information, agent incentive strategy information, and churn risk prediction information. Through the above technical solution, the actual contribution of agents and customer satisfaction can be fully considered in the personnel performance evaluation and management process, thereby achieving a more reasonable and accurate incentive effect.

[0021] The data processing method provided in this application relates to the field of data processing technology. The data processing method provided in this application can be applied to a terminal or a server, and can also be software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms.

[0022] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0023] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0024] The embodiments of this application will be further described below with reference to the accompanying drawings.

[0025] like Figure 1 As shown, Figure 1 This is a flowchart illustrating a data processing method provided in one embodiment of this application. The data processing method includes the following steps: Step S100: Obtain multidimensional data, which includes agent data, customer data, rule data, and external related data.

[0026] The data processing method provided in this application, during personnel performance evaluation and management, first acquires multi-dimensional data, including agent data, customer data, rule data, and externally related data. Agent data may include sales performance, behavioral data, professional data, and risk data; sales performance includes the number of completed policies and premium income; behavioral data includes customer follow-up frequency and training participation; professional data includes promotion intention information and satisfaction scores; and risk data includes performance fluctuation information and turnover intention questionnaire results. Customer data may include basic information, value data, and feedback data; basic information includes customer profile information and policy term information; value data includes renewal records and repurchase rates; and feedback data includes satisfaction scores and complaint ticket information. Rule data may include preset commission allocation rules, long-term incentive conditions, and industry competitor policies. Externally related data may include agent salary level information and competitor recruitment information.

[0027] It is worth noting that acquiring multidimensional data, including agent data, customer data, rule data, and external related data, can provide a data foundation for subsequent personnel performance evaluation and management.

[0028] It is worth noting that in the process of acquiring multidimensional data, when it involves processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, the user's permission or consent is always obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when this application embodiment needs to obtain sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirects to confirmation pages. Only after explicitly obtaining the user's separate permission or consent is the necessary user-related data for the normal operation of this application embodiment acquired.

[0029] Step S200: Based on the pre-trained multi-dimensional weight model, perform commission allocation analysis and processing on agent data, customer data, and rule data to obtain dynamic commission ratio information; and, based on agent data and customer data, perform revenue calculation to obtain revenue calculation information.

[0030] The data processing method provided in this application embodiment, during the personnel assessment and management process, after acquiring multidimensional data, since multidimensional data includes agent data, customer data, rule data, and external related data, can perform commission allocation analysis and processing on agent data, customer data, and rule data based on a pre-trained multidimensional weight model to obtain dynamic commission ratio information; and, based on agent data and customer data, can perform revenue calculation to obtain revenue calculation information, thus preparing for subsequent personnel assessment and management.

[0031] It is worth noting that, based on a pre-trained multi-dimensional weight model, the process of analyzing agent data, customer data, and rule data to obtain dynamic commission ratio information involves first performing attribute analysis on sales performance in agent data, customer satisfaction in customer data, and long-term retention value in rule data to obtain attribute analysis information. Next, a weighting scheme is determined based on the attribute analysis information and a pre-set weighting ratio. Finally, dynamic commission allocation is performed on agent data, customer data, and rule data according to the multi-dimensional weight model and weighting scheme to obtain dynamic commission ratio information. Based on this technical solution, the determination of dynamic commission ratio information considers sales performance in agent data, customer satisfaction in customer data, and long-term retention value in rule data, making the determination of dynamic commission ratio information more reasonable and accurate. Long-term retention value includes long-term retention and long-term value; long-term retention refers to whether users / customers continue to use the product or continue to pay after activation for a considerable period; long-term value refers to the revenue generated for the enterprise during the retention lifecycle.

[0032] like Figure 2 As shown, based on a pre-trained multi-dimensional weight model, commission allocation analysis is performed on agent data, customer data, and rule data to obtain dynamic commission ratio information. This process may include the following steps: Step S210: Perform attribute analysis on the sales performance in the agent data, the customer satisfaction in the customer data, and the long-term retention value in the rule data to obtain attribute analysis information. Step S220: Determine the weighting scheme based on the attribute analysis information and the preset weighting ratio; Step S230: Based on the multi-dimensional weight model and weight division scheme, perform dynamic commission allocation processing on agent data, customer data and rule data to obtain dynamic commission ratio information.

[0033] For steps S210 to S230, in the process of obtaining dynamic commission ratio information by analyzing and processing commission allocation from agent data, customer data, and rule data based on a pre-trained multi-dimensional weight model, firstly, attribute analysis is performed on sales performance in agent data, customer satisfaction in customer data, and long-term retention value in rule data to obtain attribute analysis information; then, a weighting scheme is determined based on the attribute analysis information and a pre-set weighting ratio; finally, dynamic commission allocation is performed on agent data, customer data, and rule data according to the multi-dimensional weight model and the weighting scheme to obtain dynamic commission ratio information. Based on the above technical solution, the determination of dynamic commission ratio information takes into account sales performance in agent data, customer satisfaction in customer data, and long-term retention value in rule data, making the determination of dynamic commission ratio information more reasonable and accurate.

[0034] It is worth noting that attribute analysis can be performed on sales performance in agent data, customer satisfaction in customer data, and long-term retention value in rule data to obtain attribute analysis information. Then, a weighting scheme can be determined based on the attribute analysis information and the pre-set weighting ratio. For example, the weight of sales performance in agent data can be set to 40%, the weight of customer satisfaction in customer data can be set to 25%, and the weight of long-term retention value in rule data can be set to 35%. Subsequently, dynamic commission allocation can be performed based on the weights set above to obtain dynamic commission ratio information.

[0035] like Figure 3 As shown, revenue calculation based on agent data and customer data to obtain revenue calculation information may include the following steps: Step S240: Determine the number of completed orders from the agent data, and determine the complaint rate and customer renewal milestones from the customer data; Step S250: Calculate revenue based on order volume, complaint rate, and customer renewal milestones to obtain revenue accounting information.

[0036] For steps S240 to S250, in the process of obtaining revenue accounting information based on agent data and customer data, the number of completed orders is first determined from the agent data, and the complaint rate and customer renewal milestones are determined from the customer data. Then, revenue calculation can be performed based on the number of completed orders, complaint rate and customer renewal milestones to obtain the corresponding revenue accounting information, which is used to prepare for subsequent personnel assessment and management.

[0037] It's worth noting that the number of completed transactions can be determined from agent data, while the complaint rate and customer renewal milestones can be determined from customer data. Subsequently, revenue can be calculated based on these factors to obtain revenue accounting information. This technical solution allows for the determination of revenue accounting information based on both agent and customer data, making the determination more accurate and reasonable, and further improving the accuracy of personnel performance management. For example, agents who have met the transaction volume target for three consecutive months and have a complaint rate of less than 0.5% are selected. Then, the corresponding revenue score is calculated based on the customer renewal milestone, thus obtaining the revenue accounting information. The customer renewal milestone can be either the first year of renewal or renewal for three consecutive years.

[0038] Step S300: Perform label segmentation processing based on agent data and external related data to obtain agent incentive strategy information; and, based on a pre-trained logistic regression model, perform churn risk level prediction processing on agent data and external related data to obtain churn risk prediction information.

[0039] The data processing method provided in this application, during the personnel performance evaluation and management process, after acquiring multidimensional data (including agent data, customer data, rule data, and external related data), allows for the labeling and segmentation of agent data and external related data to obtain agent incentive strategy information. Furthermore, based on a pre-trained logistic regression model, churn risk level prediction processing is performed on agent data and external related data to obtain churn risk prediction information. Through these technical solutions, the determination of agent incentive strategy information and churn risk prediction information becomes more accurate, leading to more accurate subsequent personnel performance evaluation and management.

[0040] It is worth noting that in the process of obtaining agent incentive strategy information through tagging based on agent data and external related data, the agent type information is obtained by first classifying the agent data into tags; then, the agent type information is matched with a pre-defined incentive strategy library to obtain the agent incentive strategy information. This technical solution enables more accurate determination of agent record strategy information, thereby improving the rationality and accuracy of personnel performance management.

[0041] It is worth noting that in the process of using a pre-trained logistic regression model to predict churn risk levels from agent data and external related data, the following steps are taken: First, the performance decline rate and turnover intention score are determined from the agent data. Then, competitor contact records are determined from the external related data. Next, the performance decline rate, turnover intention score, and competitor contact records are quantified. Finally, based on the logistic regression model, the churn risk prediction is performed on the quantified feature values ​​of performance decline, turnover intention, and competitor contact. This results in churn risk prediction information, making subsequent personnel performance management more reasonable and achieving better incentive effects.

[0042] like Figure 4 As shown, the process of labeling agent data and external related data to obtain agent incentive strategy information can include the following steps: Step S310: Perform tag classification processing based on agent data to obtain agent type information; Step S320: Match agent type information with a preset incentive strategy library to obtain agent incentive strategy information.

[0043] For steps S310 to S320, in the process of obtaining agent incentive strategy information through labeling based on agent data and external related data, agent type information is first obtained by labeling the agent data; then, agent incentive strategy information is obtained by matching the agent type information with a pre-set incentive strategy library. This technical solution enables more accurate determination of agent recording strategy information, thereby improving the rationality and accuracy of personnel performance management.

[0044] For example, agent type information can be obtained by classifying agent data based on individual tags. For instance, agents can be categorized into new agents, experienced agents, and stable agents. Then, the new agents, experienced agents, and stable agents can be matched with a pre-set incentive strategy library to obtain the corresponding agent incentive strategy information, making subsequent agent incentives more reasonable and effective.

[0045] like Figure 5 As shown, based on a pre-trained logistic regression model, churn risk level prediction processing is performed on agent data and the external related data to obtain churn risk prediction information, which may include the following steps: Step S330: Determine the performance decline rate and turnover intention score from the agent data; and determine competitor contact record information from external related data; Step S340: Quantify the performance decline rate, turnover intention score and competitor contact record information to obtain the performance decline quantitative feature value corresponding to the performance decline rate, the turnover intention quantitative feature value corresponding to the turnover intention score and the competitor contact record information. Step S350: Based on the logistic regression model, the quantitative characteristic values ​​of performance decline, turnover intention, and competitor contact are processed to predict the risk of employee turnover, thereby obtaining the risk prediction information.

[0046] For steps S330 to S350, in the process of predicting the churn risk level of agent data and external related data based on the pre-trained logistic regression model to obtain churn risk prediction information, firstly, the performance decline rate and turnover intention score are determined from the agent data; then, competitor contact record information is determined from the external related data; next, the performance decline rate, turnover intention score, and competitor contact record information are quantified to obtain the performance decline quantitative feature value corresponding to the performance decline rate, the turnover intention quantitative feature value corresponding to the turnover intention score, and the competitor contact quantitative feature value corresponding to the competitor contact record information; finally, the churn risk prediction process can be performed on the performance decline quantitative feature value, turnover intention quantitative feature value, and competitor contact quantitative feature value based on the logistic regression model to obtain churn risk prediction information, making subsequent personnel assessment and management more reasonable and achieving better incentive effects.

[0047] It is worth noting that competitor contact records are a ledger that insurance companies create internally to keep track of their market research, channel visits, bidding, or regulatory reporting. Competitors refer to companies that offer similar insurance products in the same market. Contact records refer to any "information exchange" that has occurred between the company's employees (sales, actuarial, product, legal, reinsurance, risk control, etc.) and these competitors.

[0048] Step S400: Construct personnel management strategy information based on dynamic commission ratio information, revenue calculation information, agent incentive strategy information, and churn risk prediction information.

[0049] The data processing method provided in this application embodiment, during the personnel assessment and management process, performs commission allocation analysis on agent data, customer data, and rule data based on a pre-trained multi-dimensional weight model to obtain dynamic commission ratio information; performs revenue calculation based on agent data and customer data to obtain revenue calculation information; performs label segmentation processing based on agent data and external related data to obtain agent incentive strategy information; and performs churn risk level prediction processing on agent data and external related data based on a pre-trained logistic regression model to obtain churn risk prediction information. Then, personnel management strategy information can be constructed based on the dynamic commission ratio information, revenue calculation information, agent incentive strategy information, and churn risk prediction information, providing a more reasonable and accurate strategy for personnel management.

[0050] It is worth noting that in the process of constructing personnel management strategy information, it is necessary to base it on agent data, customer data, rule data, and external related data. This setting makes the construction of personnel management strategy information more reasonable and can better motivate agents.

[0051] like Figure 6 As shown, constructing personnel management strategy information based on dynamic commission ratio information, revenue calculation information, agent incentive strategy information, and churn risk prediction information may include the following steps: Step S410: Determine commission management strategy information based on dynamic commission ratio information and preset commission allocation rules; Step S420: Determine the equity redemption strategy information based on the revenue calculation information and the preset incentive execution strategy set; Step S430: Determine risk intervention strategy information based on churn risk prediction information and a pre-set set of risk intervention strategies; Step S440: Determine personnel management strategy information based on commission management strategy information, equity redemption strategy information, agent incentive strategy information, and risk intervention strategy information.

[0052] For steps S410 to S440, in the process of constructing personnel management strategy information based on dynamic commission ratio information, revenue calculation information, agent incentive strategy information, and churn risk prediction information, firstly, commission management strategy information is determined based on dynamic commission ratio information and pre-set commission allocation rules; then, equity redemption strategy information is determined based on revenue calculation information and a pre-set set of incentive execution strategies; next, risk intervention strategy information is determined based on churn risk prediction information and a pre-set set of risk intervention strategies; finally, personnel management strategy information can be constructed based on commission management strategy information, equity redemption strategy information, agent incentive strategy information, and risk intervention strategy information, making the confirmation of personnel management strategy information more reasonable and accurate.

[0053] It is worth noting that, based on dynamic commission ratio information and pre-set commission allocation rules, commission management strategy information can be determined, making commission allocation more reasonable; based on revenue calculation information and a set of preset incentive execution strategies, equity redemption strategy information can be determined, making equity redemption more reasonable; based on agent incentive strategy information, agents can be better incentivized; and based on risk intervention strategy information, agent churn can be better predicted and handled, thus enabling better agent management.

[0054] For example, personnel management strategy information may include the following: displaying commission calculation processes (including data sources, formulas, and results) to agents through a tiered permission system, automatically completing commission settlement and pushing details; updating agent earnings points based on customer renewal data, supporting the exchange of earnings points for cash or value-added benefits; issuing matching incentive programs (such as new agent training and promotion channels for senior agents) to agents with different tags; and implementing measures such as "customized training + commission subsidies," "adjusting commission structure + customer resource support," and "career planning + team building incentives" for high / medium / low-risk agents respectively.

[0055] like Figure 7 As shown, after constructing personnel management strategy information based on dynamic commission ratio information, revenue calculation information, agent incentive strategy information, and churn risk prediction information, the following steps may also be included: Step S500: Obtain execution effect information from the personnel management strategy information; Step S600: Optimize and adjust the rule data, multi-dimensional weight model, and logistic regression model based on the execution effect information.

[0056] The data processing method provided in this application, after constructing personnel management strategy information based on dynamic commission ratio information, revenue calculation information, agent incentive strategy information, and churn risk prediction information, can then perform execution processing based on the personnel management strategy information to obtain execution effect information, thereby better managing and evaluating agents. After obtaining the execution effect information through execution processing based on the personnel management strategy information, the method can then optimize and adjust the rule data, multi-dimensional weight model, and logistic regression model based on the execution effect information, so that the subsequently generated personnel management strategy information can be more accurate and reasonable, thereby further improving the rationality of the subsequently generated personnel management strategy information.

[0057] In addition, such as Figure 8 As shown, one embodiment of this application also provides a data processing apparatus 10, which includes: The acquisition unit 100 is used to acquire multidimensional data, which includes agent data, customer data, rule data, and external related data. Analysis unit 200 is used to perform commission allocation analysis and processing on agent data, customer data and rule data based on a pre-trained multi-dimensional weight model to obtain dynamic commission ratio information; and to perform revenue calculation based on agent data and customer data to obtain revenue calculation information. The prediction unit 300 is used to perform label segmentation processing based on agent data and external related data to obtain agent incentive strategy information; and, based on a pre-trained logistic regression model, to perform churn risk level prediction processing on agent data and external related data to obtain churn risk prediction information. Building unit 400 is used to build personnel management strategy information based on dynamic commission ratio information, revenue calculation information, agent incentive strategy information, and churn risk prediction information.

[0058] The specific implementation of the data processing device 10 is basically the same as the specific embodiment of the data processing method described above, and will not be repeated here.

[0059] In addition, such as Figure 9 As shown, one embodiment of this application also provides an electronic device 700, which includes: a memory 720, a processor 710, and a computer program stored on the memory 720 and executable on the processor 710.

[0060] The processor 710 and memory 720 can be connected via a bus or other means.

[0061] The non-transient software program and instructions required to implement the data processing method of the above embodiments are stored in the memory 720. When executed by the processor 710, the data processing method of each of the above embodiments is executed.

[0062] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0063] Furthermore, one embodiment of this application provides a computer-readable storage medium storing computer-executable instructions that are executed by a processor 710 or a controller, for example, by a processor 710 in the above-described device embodiment, causing the processor 710 to perform the data processing method in the above-described embodiment.

[0064] The above embodiments can be used in combination, and modules with the same name in different embodiments may be the same or different.

[0065] The foregoing has described specific embodiments of this application; other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily have to follow the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0066] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and computer-readable storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0067] The apparatus, device, computer-readable storage medium and method provided in the embodiments of this application are corresponding. Therefore, the apparatus, device and non-volatile computer storage medium also have similar beneficial technical effects as the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the corresponding apparatus, device and computer storage medium will not be described again here.

[0068] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many methodological improvements today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that a methodological improvement cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program a digital system themselves to "integrate" it onto a PLD, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software, which is similar to the software compiler used when writing program development code. The original code before compilation must also be written in a specific programming language, which is called a Hardware Description Language (HDL). There is not just one HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also understand that by simply performing some logic programming on the method flow using the aforementioned hardware description languages ​​and programming it into an integrated circuit, the hardware circuit that implements the logic method flow can be easily obtained.

[0069] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, ASICs, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0070] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0071] For ease of description, the above apparatus is described by dividing it into various functional units. Of course, in implementing the embodiments of this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0072] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, embodiments of this application can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of this application can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0073] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0074] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0075] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0076] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0077] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash memory (FlashRAM). Memory is an example of computer-readable media.

[0078] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0079] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0080] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, A and B simultaneously, or B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0081] The embodiments of this application can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. The embodiments of this application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can reside in local and remote computer storage media, including storage devices.

[0082] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0083] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of this application should be included within the scope of the claims of this application.

Claims

1. A data processing method, characterized in that, The data processing method includes: Acquire multidimensional data, wherein the multidimensional data includes agent data, customer data, rule data, and external related data; Based on a pre-trained multi-dimensional weight model, commission allocation analysis is performed on the agent data, customer data, and rule data to obtain dynamic commission ratio information; and revenue calculation is performed based on the agent data and customer data to obtain revenue calculation information. Based on the agent data and the external related data, label segmentation processing is performed to obtain agent incentive strategy information; and based on the pre-trained logistic regression model, churn risk level prediction processing is performed on the agent data and the external related data to obtain churn risk prediction information. Based on the dynamic commission ratio information, the revenue calculation information, the agent incentive strategy information, and the churn risk prediction information, personnel management strategy information is constructed.

2. The data processing method according to claim 1, characterized in that, The pre-trained multi-dimensional weight model performs commission allocation analysis on the agent data, customer data, and rule data to obtain dynamic commission ratio information, including: Attribute analysis is performed on the sales performance in the agent data, the customer satisfaction in the customer data, and the long-term retention value in the rule data to obtain attribute analysis information; Based on the attribute analysis information and the preset weighting ratio, a weighting scheme is determined; Based on the multi-dimensional weight model and the weight division scheme, the agent data, customer data and rule data are dynamically allocated to obtain the dynamic commission ratio information.

3. The data processing method according to claim 1, characterized in that, The revenue calculation based on the agent data and the customer data, to obtain revenue calculation information, includes: The number of completed orders is determined from the agent data, and the complaint rate and customer renewal milestones are determined from the customer data; Revenue is calculated based on the number of completed orders, the complaint rate, and the customer renewal milestones to obtain the revenue accounting information.

4. The data processing method according to claim 1, characterized in that, The step of performing label segmentation based on the agent data and the externally related data to obtain agent incentive strategy information includes: Based on the agent data, tag classification processing is performed to obtain agent type information; The agent incentive strategy information is obtained by matching the agent type information with a preset incentive strategy library.

5. The data processing method according to claim 1, characterized in that, The pre-trained logistic regression model performs churn risk level prediction processing on the agent data and the external correlation data to obtain churn risk prediction information, including: The agent data is used to determine the extent of performance decline and turnover intention scores; and the external correlation data is used to determine competitor contact records. The performance decline rate, the turnover intention score, and the competitor contact record information are quantified to obtain the performance decline quantitative feature value corresponding to the performance decline rate, the turnover intention quantitative feature value corresponding to the turnover intention score, and the competitor contact record information quantitative feature value. Based on the logistic regression model, the quantitative characteristic values ​​of performance decline, turnover intention, and competitor contact are processed to predict the risk of employee turnover, thereby obtaining the predicted information of employee turnover risk.

6. The data processing method according to claim 1, characterized in that, The process of constructing personnel management strategy information based on the dynamic commission ratio information, the revenue calculation information, the agent incentive strategy information, and the churn risk prediction information includes: Based on the dynamic commission ratio information and the preset commission allocation rules, determine the commission management strategy information; Based on the aforementioned revenue calculation information and the preset set of incentive execution strategies, determine the equity redemption strategy information; Based on the churn risk prediction information and the preset set of risk intervention strategies, determine the risk intervention strategy information; The personnel management strategy information is determined based on the commission management strategy information, the equity redemption strategy information, the agent incentive strategy information, and the risk intervention strategy information.

7. The data processing method according to claim 1, characterized in that, After constructing personnel management strategy information based on the dynamic commission ratio information, the revenue calculation information, the agent incentive strategy information, and the churn risk prediction information, the method further includes: Obtain the execution effect information obtained by implementing the personnel management strategy; The rule data, the multi-dimensional weight model, and the logistic regression model are optimized and adjusted based on the execution effect information.

8. A data processing apparatus, characterized in that, The data processing device includes: The acquisition unit is used to acquire multidimensional data, wherein the multidimensional data includes agent data, customer data, rule data, and external related data; The analysis unit is used to perform commission allocation analysis and processing on the agent data, customer data and rule data based on a pre-trained multi-dimensional weight model to obtain dynamic commission ratio information; and to perform revenue calculation based on the agent data and customer data to obtain revenue calculation information. The prediction unit is used to perform label segmentation processing based on the agent data and the external related data to obtain agent incentive strategy information; and to perform churn risk level prediction processing on the agent data and the external related data based on a pre-trained logistic regression model to obtain churn risk prediction information. The construction unit is used to construct personnel management strategy information based on the dynamic commission ratio information, the revenue calculation information, the agent incentive strategy information, and the churn risk prediction information.

9. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements the data processing method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions are used to execute the data processing method according to any one of claims 1 to 7.

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