A business interface optimization method, device and equipment based on multi-dimensional customer groups

Abstract

The embodiments of this specification relate to the field of computer technology, and in particular to a method, device and equipment for optimizing business interfaces based on multi-dimensional customer groups. The method comprises: obtaining behavioral data and basic information of multiple customers who conduct business through the business interface of the target business at multiple consecutive moments; dividing multiple customers into multiple predetermined customer groups according to the behavioral data and basic information at each moment, and obtaining the customer group corresponding to each moment; calculating the customer group conversion rate of each business interface according to the correspondence between the customer group and the business interface and the number of customers in the customer group corresponding to each moment; determining the business interface that needs to be optimized according to the customer group conversion rate as the business interface to be optimized; reading the feedback information of customers on the business interface to be optimized at each moment; and determining the optimization items of the business interface to be optimized according to the feedback information. The embodiments of this specification reduce the workload of optimization and ensure the privacy and security of customers.

Description

Technical Field

[0001] The embodiments of this specification relate to the field of computer technology, and in particular to a method, device, and equipment for optimizing a business interface based on multi-dimensional customer groups. Background Art

[0002] Currently, most customers conduct business through mobile banking apps or online banking interfaces. Therefore, the layout, color, and interface response time of the business interface will affect the customer's business experience. The current method for optimizing the business interface is mostly to release a beta version of the client. Some users use the test version to conduct business and submit improvements and optimization points for the business interface. Developers optimize the business interface based on the improvements and optimization points provided by users and release the official version. However, this method will bring a huge workload and may also require obtaining customer privacy information (such as customer identity information) to determine whether the customer meets the test version usage conditions before pushing the test version. Customer privacy security is not high.

[0003] How to ensure user privacy and reduce the workload of business interface optimization is a technical problem that needs to be solved urgently. Summary of the Invention

[0004] In order to solve the problems of large workload and poor customer privacy security in the optimization of business interfaces in the existing technology, the embodiments of this specification provide a business interface optimization method, device and equipment based on multi-dimensional customer groups, which obtain real-time behavior data of a large number of customers, and then analyze and process the real-time behavior data to determine the number of customers in the multi-dimensional customer groups corresponding to each interface of the business, calculate the group conversion rate based on the number of customers, and judge whether each interface needs to be optimized based on the group conversion rate. If optimization is required, obtain customer feedback information corresponding to the interface that needs to be optimized, analyze the customer feedback information, and determine the interface optimization points, so that developers can optimize the interface according to the interface optimization points.

[0005] The specific technical solutions of the embodiments of this specification are as follows:

[0006] On the one hand, the embodiments of this specification provide a method for optimizing a business interface based on a multi-dimensional customer base, including:

[0007] Obtaining behavioral data of multiple customers conducting business through the business interface of the target business at multiple consecutive moments and basic information of the multiple customers;

[0008] Dividing the plurality of customers into a plurality of predetermined customer groups according to the behavior data and basic information at each moment, to obtain a customer group corresponding to each moment;

[0009] Calculate the customer conversion rate of each business interface based on the correspondence between the customer group and the business interface and the number of customers of the customer group corresponding to each moment;

[0010] Determining the business interface that needs to be optimized according to the customer conversion rate as the business interface to be optimized;

[0011] Reading feedback information of at least one customer at each of the times for the business interface to be optimized;

[0012] Optimization items of the business interface to be optimized are determined according to the feedback information, and the optimization items are used to guide business personnel to optimize the business interface to be optimized.

[0013] Furthermore, according to the correspondence between the customer groups and the business interfaces and the number of customers of the customer groups corresponding to each moment, calculating the customer group conversion rate of each business interface further includes:

[0014] Sort each business interface according to the order of business processes;

[0015] For each business interface, the number of customers of the customer group corresponding to the previous business interface at the earlier of two consecutive moments is used as the target customer number of the business interface; the number of customers of the customer group corresponding to the business interface at the later of two consecutive moments is used as the converted customer number of the business interface;

[0016] The customer group conversion rate at each two adjacent moments of the business interface is calculated according to the number of target customers and the number of converted customers corresponding to each two adjacent moments of the business interface in the multiple consecutive moments.

[0017] Furthermore, calculating the customer conversion rate at each of two adjacent moments of the business interface according to the number of target customers and the number of converted customers corresponding to each of two adjacent moments in the plurality of consecutive moments further includes:

[0018] For each two adjacent moments of the business interface, the ratio of the number of converted customers to the number of target customers corresponding to the business interface at each two adjacent moments is calculated to obtain the customer conversion rate of the business interface at each two adjacent moments.

[0019] Furthermore, the business interface to be optimized is determined according to the customer conversion rate, and the business interface to be optimized further includes:

[0020] For each business interface, determine the number of two consecutive moments at which the customer conversion rate of the business interface does not exceed the first threshold;

[0021] It is determined whether the number exceeds a second threshold; if so, the business interface is the business interface to be optimized.

[0022] Furthermore, if the number of the plurality of consecutive moments is 2, determining the business interface to be optimized according to the customer conversion rate further includes:

[0023] For each business interface, it is determined whether the customer conversion rate of the business interface does not exceed the third threshold. If so, the business interface is the business interface to be optimized.

[0024] Furthermore, dividing the plurality of customers into a plurality of predetermined customer groups according to the behavior data and basic information at each moment, and obtaining the customer group corresponding to each moment further includes:

[0025] Matching the basic information and the behavioral data of the customer with predetermined rules corresponding to each customer group;

[0026] The customers are divided into the successfully matched customer groups.

[0027] Furthermore, matching the basic information and the behavior data of the customer with the predetermined rules corresponding to each customer group further includes:

[0028] The basic information and the behavior data of the customer are input into a decision tree, and matched with the predetermined rules corresponding to each of the multiple nodes in the decision tree to obtain the customer group matched with the customer.

[0029] Furthermore, the feedback information includes at least one predetermined interface defect point selected by the customer when exiting the service interface;

[0030] The method further comprises:

[0031] The interface defect points selected by the customer are stored in the first database table corresponding to the business interface according to the selection time, so that the interface defect points of the business interface to be optimized at each moment can be read from the first database table corresponding to the business interface to be optimized according to the time corresponding to each moment.

[0032] Furthermore, the interface defect points are anonymously selected by the customer when exiting the service interface.

[0033] Furthermore, determining the optimization items of the service interface to be optimized according to the feedback information further includes:

[0034] Determine the number of customer selections corresponding to each interface defect point among all interface defect points corresponding to the multiple consecutive moments;

[0035] The interface defect points whose number of customer selections exceeds a fourth threshold are used as the optimization items.

[0036] Furthermore, the feedback information also includes the exit reason entered by the customer when exiting the interface;

[0037] The method further comprises:

[0038] The exit reason entered by the customer is stored in the second database table corresponding to the business interface according to the input time, so that the exit reason of the business interface to be optimized at each moment can be read from the second database table corresponding to the business interface to be optimized according to the time corresponding to each moment.

[0039] Furthermore, determining the optimization items of the service interface to be optimized according to the feedback information further includes:

[0040] Performing text vectorization processing on each exit reason at the multiple consecutive moments to obtain multiple text vectors;

[0041] Each text vector is input into a pre-trained optimization recognition model for calculation to obtain the optimization item corresponding to each text vector, wherein the optimization recognition model is trained using the text vectors corresponding to the historical exit reasons and the annotated optimization items as a training data set.

[0042] On the other hand, the embodiments of this specification further provide a device for optimizing a business interface based on a multi-dimensional customer group, the device comprising:

[0043] An information acquisition unit, configured to acquire behavioral data of multiple customers handling business through a business interface of a target business at multiple consecutive moments and basic information of the multiple customers;

[0044] A customer group division unit, configured to divide the plurality of customers into a plurality of predetermined customer groups according to the behavior data and basic information at each moment, to obtain a customer group corresponding to each moment;

[0045] A customer group conversion rate calculation unit, configured to calculate the customer group conversion rate of each business interface based on the correspondence between the customer group and the business interface and the number of customers of the customer group corresponding to each moment;

[0046] a business interface to be optimized determining unit, configured to determine the business interface to be optimized according to the customer group conversion rate as the business interface to be optimized;

[0047] A feedback information reading unit, configured to read feedback information of at least one customer at each of the times for the service interface to be optimized;

[0048] An optimization item determination unit is used to determine the optimization items of the business interface to be optimized according to the feedback information, and the optimization items are used to guide business personnel to optimize the business interface to be optimized.

[0049] On the other hand, an embodiment of this specification further provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor implements the above method when executing the computer program.

[0050] On the other hand, an embodiment of this specification further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0051] Finally, an embodiment of this specification also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the above method is implemented.

[0052] Using the embodiments of this specification, first obtain the behavioral data and basic information of a large number of customers who conduct business through the business interface of the target business at multiple consecutive moments, and then divide the customers into multiple predetermined customer groups based on the behavioral data and basic information at each moment. It can be understood that the embodiments of this specification are to obtain the business processing data of multiple business interfaces of the target business in real time. The business processing data is the customer's behavioral data and basic information, and then divide the customers into corresponding customer groups in real time based on the obtained behavioral data and basic information. The correspondence between customer groups and business interfaces is predefined, so by dividing customers into customer groups, the corresponding business interface of the customer can be determined based on the correspondence between customer groups and business interfaces.

[0053] Because customers conduct business according to steps (processes), as time goes by, the number of customers corresponding to each business interface will also transfer in the order of steps (processes). Therefore, if the number of customers in the next business interface at the next moment is much smaller than the number of customers corresponding to the previous business interface at the previous moment, it means that a large number of customers have been lost in the next business interface, which means that the business interface does not conform to the customer's habits and needs to be optimized. Therefore, the embodiment of this specification calculates the customer group conversion rate of each business interface based on the correspondence between the customer group and the business interface and the number of customers corresponding to the customer group at each moment; and determines the business interface that needs to be optimized based on the customer group conversion rate as the business interface to be optimized.

[0054] Then, the customer feedback information of the business interface to be optimized at each moment is read. The feedback information can be submitted by the customer when the customer exits the interface to be optimized, indicating which parts of the business interface to be optimized the customer is dissatisfied with. Therefore, the embodiment of this specification determines the optimization items of the interface to be optimized based on the feedback information, so that business personnel can optimize the business interface to be optimized based on the optimization items.

[0055] Through the method of the embodiments of this specification, the interaction data between the customer and the business interface of the target business can be obtained in real time to determine the business interface that needs to be optimized, and the optimization items can be automatically determined for optimization based on the customer's feedback information. Developers do not need to release a test version of the target business, thereby reducing the optimization workload. In addition, there is no need to analyze whether the customer meets the usage conditions of the test version, thereby ensuring the privacy and security of the customer. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the embodiments of this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0057] Figure 1 The figure shows a schematic diagram of an implementation system of a business interface optimization method based on multi-dimensional customer groups according to an embodiment of this specification;

[0058] Figure 2 The figure shows a flow chart of a business interface optimization method based on multi-dimensional customer groups according to an embodiment of this specification;

[0059] Figure 3 The figure shows a flow chart of dividing the plurality of customers into a plurality of predetermined customer groups according to the behavior data and basic information at each moment in the embodiment of this specification, and obtaining the customer group corresponding to each moment;

[0060] Figure 4 The figure shows a flow chart of calculating the customer group conversion rate of each business interface according to the correspondence between the customer group and the business interface and the number of customers of the customer group corresponding to each moment in the embodiment of this specification;

[0061] Figure 5 The figure shows a process diagram of determining the business interface to be optimized according to the customer conversion rate in an embodiment of this specification, as a business interface to be optimized;

[0062] Figure 6 FIG2 is a schematic diagram of a first process of determining the optimization items of the service interface to be optimized according to the feedback information in an embodiment of this specification;

[0063] Figure 7 FIG2 is a schematic diagram of a second process of determining the optimization items of the service interface to be optimized according to the feedback information in an embodiment of this specification;

[0064] Figure 8 The figure shows a schematic diagram of the structure of a business interface optimization device based on multi-dimensional customer groups in an embodiment of this specification;

[0065] Figure 9 The figure shows a schematic diagram of the structure of a computer device in an embodiment of this specification.

[0066]

Description of the accompanying drawings

[0067] 101. Terminal;

[0068] 102. Server;

[0069] 801, information acquisition unit;

[0070] 802. Customer segmentation unit;

[0071] 803. Customer conversion rate calculation unit;

[0072] 804. A unit for determining a business interface to be optimized;

[0073] 805. Feedback information reading unit;

[0074] 806. Optimization item determination unit;

[0075] 902. Computer equipment;

[0076] 904. Processing equipment;

[0077] 906. Storage resources;

[0078] 908, drive system;

[0079] 910, input / output module;

[0080] 912. Input devices;

[0081] 914. Output device;

[0082] 916. Presentation equipment;

[0083] 918. Graphical User Interface;

[0084] 920, network interface;

[0085] 922, communication link;

[0086] 924. Communication bus. DETAILED DESCRIPTION

[0087] The following will be combined with the drawings in the embodiments of this specification to clearly and completely describe the technical solutions in the embodiments of this specification. Obviously, the embodiments described are only part of the embodiments of this specification, not all of them. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the embodiments of this specification.

[0088] It should be noted that the terms "first", "second", etc. in the description and claims of the embodiments of this specification and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the embodiments of this specification described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0089] It should be noted that the acquisition, storage, use, and processing of data in the technical solutions of the embodiments of this specification comply with the relevant provisions of national laws and regulations.

[0090] It should be noted that in the embodiments of this specification, certain software, components, models and other existing solutions in the industry may be mentioned. They should be regarded as exemplary and their purpose is only to illustrate the feasibility of implementing the technical solution of this application, but it does not mean that the applicant has or will necessarily use the solution.

[0091] like Figure 1 The diagram shows a system diagram for implementing a method for optimizing a business interface based on a multi-dimensional customer base in an embodiment of the present specification, including a terminal 101 and a server 102. The terminal 101 and the server 102 can communicate with each other via a network, which can include a local area network (LAN), a wide area network (WAN), the Internet, or a combination thereof, and is connected to a website, a user device (e.g., a computing device), and a back-end system.

[0092] Customers can conduct online business transactions through terminal 101. Server 102 can be a bank's business server. Server 102 displays the business interface to multiple customers through multiple terminals 101, and receives interaction data and feedback data between each customer and the business interface displayed on the corresponding terminal 101, and stores the feedback data in a database table. Server 102 analyzes the interaction data, determines the business interface to be optimized, and then reads the feedback data from the database table, analyzes the feedback data and determines the optimization items.

[0093] Alternatively, the server 102 may be a node of a cloud computing system (not shown), or each server may be a separate cloud computing system including multiple computers interconnected by a network and operating as a distributed processing system.

[0094] In addition, it should be noted that Figure 1 What is shown is only one application environment provided by the embodiment of this specification. In actual application, other application environments may also be included, and this specification does not limit them.

[0095] In response to the problems existing in the prior art, the embodiments of this specification provide a business interface optimization method based on multi-dimensional customer groups, which obtains real-time behavior data of a large number of customers, and then analyzes and processes the real-time behavior data to determine the number of customers in the multi-dimensional customer groups corresponding to each interface of the business, calculates the group conversion rate based on the number of customers, and determines whether each interface needs to be optimized based on the group conversion rate. If optimization is required, obtain customer feedback information corresponding to the interface that needs to be optimized, analyze the customer feedback information, and determine the interface optimization points, so that developers can optimize the interface according to the interface optimization points.

[0096] Figure 2 The figure shows a flowchart of a method for optimizing a business interface based on a multi-dimensional customer base. This figure describes the process of analyzing customer behavior data and basic information, determining interfaces to be optimized, and identifying optimization items. The order of steps listed in the embodiments is only one of many possible execution sequences and does not represent the only execution order. When implemented in a real system or device product, the methods shown in the embodiments or figures can be executed sequentially or in parallel.

[0097] Specific examples Figure 2 As shown, the method may include:

[0098] Step 201: Obtaining behavioral data of multiple customers who conduct business through the business interface of the target business at multiple consecutive moments and basic information of the multiple customers;

[0099] Step 202: Divide the plurality of customers into a plurality of predetermined customer groups according to the behavior data and basic information at each moment, and obtain a customer group corresponding to each moment;

[0100] Step 203: Calculate the customer conversion rate of each business interface based on the correspondence between the customer groups and the business interfaces and the number of customers of the customer groups corresponding to each moment;

[0101] Step 204: Determine the business interface that needs to be optimized based on the customer conversion rate as the business interface to be optimized;

[0102] Step 205: Reading feedback information of at least one customer at each time of the business interface to be optimized;

[0103] Step 206: Determine optimization items for the business interface to be optimized based on the feedback information, where the optimization items are used to guide business personnel to optimize the business interface to be optimized.

[0104] Using the embodiments of this specification, first obtain the behavioral data and basic information of a large number of customers who conduct business through the business interface of the target business at multiple consecutive moments, and then divide the customers into multiple predetermined customer groups based on the behavioral data and basic information at each moment. It can be understood that the embodiments of this specification are to obtain the business processing data of multiple business interfaces of the target business in real time. The business processing data is the customer's behavioral data and basic information, and then divide the customers into corresponding customer groups in real time based on the obtained behavioral data and basic information. The correspondence between customer groups and business interfaces is predefined, so by dividing customers into customer groups, the corresponding business interface of the customer can be determined based on the correspondence between customer groups and business interfaces.

[0105] Because customers conduct business according to steps (processes), as time goes by, the number of customers corresponding to each business interface will also transfer in the order of steps (processes). Therefore, if the number of customers in the next business interface at the next moment is much smaller than the number of customers corresponding to the previous business interface at the previous moment, it means that a large number of customers have been lost in the next business interface, which means that the business interface does not conform to the customer's habits and needs to be optimized. Therefore, the embodiment of this specification calculates the customer group conversion rate of each business interface based on the correspondence between the customer group and the business interface and the number of customers corresponding to the customer group at each moment; and determines the business interface that needs to be optimized based on the customer group conversion rate as the business interface to be optimized.

[0106] Then, the customer feedback information of the business interface to be optimized at each moment is read. The feedback information can be submitted by the customer when the customer exits the interface to be optimized, indicating which parts of the business interface to be optimized the customer is dissatisfied with. Therefore, the embodiment of this specification determines the optimization items of the interface to be optimized based on the feedback information, so that business personnel can optimize the business interface to be optimized based on the optimization items.

[0107] Through the method of the embodiments of this specification, the interaction data between the customer and the business interface of the target business can be obtained in real time to determine the business interface that needs to be optimized, and the optimization items can be automatically determined for optimization based on the customer's feedback information. Developers do not need to release a test version of the target business, thereby reducing the optimization workload. In addition, there is no need to analyze whether the customer meets the usage conditions of the test version, thereby ensuring the privacy and security of the customer.

[0108] In the embodiments of this specification, the analysis is not based on the customer dimension, that is, the embodiments of this specification do not analyze the business interface interacted by the same customer at each moment. If the business interface interacted by the same customer at each moment is analyzed, a large number of subtasks will be required to analyze each customer. This analysis method will greatly increase the amount of calculation, and it is also necessary to integrate the analysis results of each subtask after the analysis of each subtask is completed and further analyze them. Therefore, analyzing the business interface interacted by the same customer at each moment is only suitable for analyzing a small batch of customers.

[0109] Only when a large number of customers conduct business through the business interface can we reflect whether the business interface conforms to customer usage habits and whether it needs to be optimized. Therefore, the embodiments of this specification abandon the method of analyzing based on customer dimensions and instead group a large number of customers based on their behavioral data and basic customer information to obtain multiple customer groups. As can be seen, the embodiments of this specification divide a large number of customers into designated customer groups. It only needs to match multiple grouping rules with customer behavioral data and basic customer information, and can perform parallel analysis to improve computing power.

[0110] The correspondence between each customer segment and business interface is pre-defined. Therefore, the segmentation rules are derived from business personnel's analysis of the business characteristics of the business interfaces. These segmentation rules represent the characteristics of customer interaction data across each business interface. If a customer's interaction data (behavioral data and basic information) meets these characteristics, the customer can be identified as belonging to the appropriate customer segment, thereby determining which business interface the customer is interacting with.

[0111] For example, the target business includes registration, authentication, contract signing, expenditure, and retention, a total of five steps (layers). The customer needs to first register on the business interface corresponding to the registration step, then authenticate on the business interface corresponding to the authentication step, then sign on the business interface corresponding to the contract signing step, then make expenditures on the business interface corresponding to the expenditure step, and finally, complete the retention process on the business interface corresponding to the retention step. If the customer exits the business interface corresponding to a step, it means that the customer has not transitioned from the previous step to this step, and the customer has churned. Conversely, if the customer does not exit the business interface corresponding to a step, the customer has completed the step, indicating that the customer has not churned.

[0112] Therefore, the embodiment of this specification calculates the customer conversion rate of each business interface based on the correspondence between customer groups and business interfaces and the number of customers in the corresponding customer group at each time. A higher conversion rate indicates less customer churn, while a lower conversion rate indicates more customer churn. The business interface that needs to be optimized is thus determined based on the customer conversion rate.

[0113] When a customer exits a business interface, the embodiments of this specification can obtain customer feedback information regarding the exited business interface, for example, the customer points out which part or parts of the business interface have deficiencies that cause them to no longer want to continue processing the business, for example, the interface response time of the business interface is too long, the layout of the business interface is unreasonable, or there are too many functional modules in the business interface that cause operation difficulties, etc.

[0114] The embodiment of this specification stores customer feedback information in a database table corresponding to the business interface, so that after determining the business interface to be optimized, the feedback information of at least one customer of the business interface to be optimized at each time is read, and the optimization items of the business interface to be optimized are determined based on the feedback information. The optimization items are used to guide business personnel to optimize the business interface to be optimized.

[0115] In the embodiments of this specification, customer behavior data includes but is not limited to login behavior, transaction records, online activity traces, etc., to ensure that the customer's behavioral characteristics and preferences are fully captured.

[0116] The behavioral data can then be preprocessed and features extracted, for example:

[0117] Extract key indicators, such as authentication status, activity frequency within a time window (time-sensitive attributes in the policy definition are adjusted based on the time window, for example, "last 7 days," "last 30 days," etc.), and business touchpoints, and convert them into feature vectors useful for classification. Authentication status typically refers to the identity verification process for users or businesses using the platform, including personal real-name authentication and corporate authentication.

[0118] Data cleaning: remove outliers, delete feature columns with a missing feature rate higher than 90%, fill feature columns with a missing feature rate lower than 90% with the mean, and remove duplicates.

[0119] Then, customers are grouped. For example, a classification processor can be used for grouping, which is configured with multiple preset classification rules. Each rule corresponds to a specific customer group. The rules are based on five aspects: customer behavior characteristics (such as authentication status, business activity frequency, service usage preferences, etc.), time window definition (such as the last 7 days, the last 30 days, etc.), life cycle, potential value indicators, and state transitions. This forms a customer group classification framework covering the entire customer life cycle, including the following:

[0120] A. Behavioral characteristics include personal real-name authentication status, abnormal login behavior, post-authentication activities of enterprises, loan application behavior, browsing and use of service functions, customer activity indicators, etc.

[0121] B. The time window ensures the timeliness of the classification and can reflect the latest customer behavior patterns.

[0122] C. Divide the customer life cycle into different stages, such as new registration, active period, potential upgrade stage, churn warning, etc. Define corresponding classification standards in each stage based on the customer's behavior trajectory and the amount of activity within the time window. New registration: refers to the stage when the customer completes registration or authentication for the first time. Active period: refers to the stage when the customer frequently uses services, participates in activities or generates transactions within a period of time (such as the past 30 days). Potential upgrade stage: refers to the stage when the customer's behavior shows that he or she may be interested in higher-level services or products, but has not yet taken upgrade actions. Churn warning: refers to the stage when customer behavior shows that his or her activity level has decreased, such as reduced login frequency, decreased transaction volume, or begins to show signs of leaving the platform.

[0123] D. Potential value indicators: Combine customer behavioral data and potential business value, such as credit assessment results, predicted credit limit, funding needs, etc., to identify high-potential or high-risk customer groups.

[0124] E. Status Transition: Monitor changes in customer status, such as from unauthenticated to authenticated, from unused credit (e.g., loan limit) to spent, or from signed to nearing expiration, so you can implement timely marketing strategies or retention measures. Signing also refers to credit authorization, which authorizes the customer to make a loan, and expiration refers to the deadline for repayment.

[0125] Taking into account the above five classification scenarios, and finally combining with actual business needs, the operation command customer group is divided into 16 customer groups. The specific grouping rules are not the invention of the embodiments of this specification. Bank business personnel can set the grouping rules according to actual business conditions, so they will not be described here.

[0126] Customer group 1: There are highly active individual certified customers of the enterprise;

[0127] Customer group 2: Customers who log in at abnormal times;

[0128] Customer group 3: verified but not yet opened an account;

[0129] Customer group 4: Customers who have a credit limit but have not signed a contract;

[0130] Customer group 5: Customers who have loan quotas but have not signed contracts;

[0131] Customer group 6: Customers who have opened accounts and have funding needs;

[0132] Customer group 7: Customer group with missing four elements (the four elements include name, ID number, mobile phone number, and bank card number);

[0133] Customer group 8: Customers who have submitted an account opening application but have not yet opened a corporate account;

[0134] Customer group 9: those who browsed the service functions but did not sign up for the contract;

[0135] Customer group 10: Enterprise certified but without business card function activated;

[0136] Customer group 11: signed but unused customers;

[0137] Customer group 12: non-operating customers of contracted enterprises;

[0138] Customer group 13: Customer group that will sign contract and come online soon;

[0139] Customer group 14: Settle the customers who have not signed contracts and have funding needs;

[0140] Customer group 15: settled and highly active customer group;

[0141] Customer segment 16: Retained customers.

[0142] Then, based on the characteristics of each step of the target business, the corresponding relationships between these customer groups and business interfaces are divided. For example, customer groups 1-2 belong to the registration step and correspond to the registration step business interface; customer groups 3-10 belong to the authentication step and correspond to the authentication step business interface; customer groups 11-12 belong to the contract step and correspond to the contract step business interface; customer groups 13-15 belong to the payment step and correspond to the payment step business interface; customer group 16 belongs to the retention step and corresponds to the retention step business interface.

[0143] It should be noted that the above-mentioned specific behavioral data, basic information, steps of the target business, customer groups, and the correspondence between the customer groups and the steps of the target business are all exemplary. When actually implementing the methods of the embodiments of this specification, bank business personnel can determine the correspondence between behavioral data, basic information, steps of the target business, customer groups, and the steps of the target business based on actual business conditions. The embodiments of this specification do not impose any restrictions.

[0144] According to one embodiment of this specification, Figure 3 As shown, the plurality of customers are divided into a plurality of predetermined customer groups according to the behavior data and basic information at each moment, and the customer groups corresponding to each moment are obtained further including:

[0145] Step 301: Match the basic information and behavior data of the customer with the predetermined rules corresponding to each customer group;

[0146] Step 302: Divide the customer into the successfully matched customer group.

[0147] In the embodiments of this specification, the preset rules can be set by business personnel according to actual business conditions, and the embodiments of this specification do not limit this.

[0148] Preferably, matching the basic information and the behavior data of the customer with the predetermined rules corresponding to each customer group further includes:

[0149] The basic information and the behavior data of the customer are input into a decision tree, and matched with the predetermined rules corresponding to each of the multiple nodes in the decision tree to obtain the customer group matched with the customer.

[0150] For example, in a decision tree algorithm, the customer's behavior data and basic information are first converted into a form that the decision tree can understand and process. For example:

[0151] 1) Behavioral data and basic information can be encoded into usage indicators for specific services, with textual information represented in binary code. For example: customer authentication status, business activity level, and service usage preferences.

[0152] 2) Time windows can be used to calculate the number of activities within a given day. Considering the time sensitivity of customer behavior, rules will set a corresponding time range to define recent activity. For example, time windows such as "last seven days" or "last 30 days" are used to track dynamic changes over a recent period of time to reflect the customer's latest possible business needs or potential value.

[0153] 3) Divide the customer's entire life cycle into specific life cycle coding indicators, represented by binary codes, to identify the different stages of customers in the product or service life cycle, from registration 0001, certification 0010, contract 0011, expenditure 0100 to retention 0101, and define a series of business funnels to segment customer groups based on the completion of these conversion steps.

[0154] 4) Potential value indicators are divided into specific potential value indicators and represented by binary codes, such as credit assessment results, funding requirements, etc., to locate high-potential or high-risk customer groups.

[0155] 5) State transitions can be represented by binary codes using different state-specific codes.

[0156] In the stage of defining decision nodes: each preset classification rule corresponds to an internal node of the decision tree. For example: if the customer has undergone corporate certification in the past 7 days, then go to a certain branch. If the customer has financial needs in the past 7 days and has not signed a contract, then go to another branch. In building the decision tree, the C4.5 algorithm is used to build the tree structure (C4.5 is a classification algorithm that introduces weighted information gain to handle missing values ​​more fairly. It supports the processing of continuous attributes, can automatically select the best split point, and introduces a pruning strategy to reduce the risk of overfitting). Each internal node represents a test of a feature or feature combination, and the leaf node represents a type of customer group. At each split point, the algorithm will look for the optimal splitting criterion for information gain to maximize the distinction between categories. The accuracy rate is used to evaluate the final model effect, and the final accuracy rate reached 97%.

[0157] In some other embodiments of this specification, in addition to being used to determine the business interface to be optimized, the customer group can also be displayed to bank business personnel. For example, the number of customers in each region can be displayed in real time through a star map page, and the size of the star can be used to remind the region that business is being processed. It can be used for account managers to analyze the real-time business processing activity and divide regional business priorities. It can also allow bank business personnel to click on graphic elements to trigger detailed data viewing, exporting, and sending emails, and package the selected customer data and transmit it to a designated email address or local storage device in a secure manner. It can also generate a conversion rate report based on the grouping results to assist account managers in formulating personalized intervention measures.

[0158] It should be noted that strict rights management and data encryption should be implemented when implementing the methods of the embodiments of this specification. By encrypting the customer's personal key information, such as name and contact information, the security of sensitive information is ensured.

[0159] According to one embodiment of this specification, Figure 4As shown, according to the correspondence between the customer groups and the business interfaces and the number of customers of the customer groups corresponding to each moment, calculating the customer group conversion rate of each business interface further includes:

[0160] Step 401: sorting each business interface according to the order of business processes;

[0161] Step 402: For each business interface, the number of customers of the customer group corresponding to the previous business interface at the earlier of two adjacent moments is used as the target customer number of the business interface; the number of customers of the customer group corresponding to the business interface at the later of two adjacent moments is used as the converted customer number of the business interface;

[0162] Step 403: Calculate the customer conversion rate at each of two adjacent moments of the business interface according to the number of target customers and the number of converted customers corresponding to each of two adjacent moments in the multiple consecutive moments of the business interface.

[0163] In the embodiment of this specification, the customer needs to first handle the business corresponding to the business interface ranked first, and then enter the business interface ranked later to handle the corresponding business. If the customer feels that the business interface ranked later is very cumbersome or the experience is poor, then he or she may exit the business interface and may go to the counter offline to handle the business later. In this case, the customer has not converted from the steps corresponding to the business interface ranked first to the steps corresponding to the business interface ranked later, which means that the customer has been lost in the business interface ranked later, and the business interface ranked later may need to be optimized.

[0164] However, it should be noted that the embodiments of this specification do not analyze based on the customer dimension, that is, they do not determine whether each customer has converted from the previous business interface to the next business interface, but rather conduct an overall analysis of a large number of customers, that is, they analyze the trend of customer conversion, rather than determining whether each customer has completed the conversion. For example, if a customer handles business very quickly, then it is possible that at the moment before two adjacent moments, the customer belongs to the customer group corresponding to the registration step, but at the next moment, the customer may have completed the authentication step and the contract step and come to the payment step. At the next moment, the customer belongs to the customer group corresponding to the payment step. Although the customer has not actually churned, he is not among the number of customers in the next step after the registration step (i.e., the authentication step). However, this does not affect the calculation result of the customer group conversion rate, because the embodiments of this specification analyze the trend of customer conversion. If the business interface corresponding to a certain step has defects such as not conforming to the customer's usage habits or the interface waiting time is too long, then the business interface will definitely experience a large number of customer churn, and this can definitely be reflected in the conversion trend of a large number of customers.

[0165] In an embodiment of the present specification, for each business interface (for example, business interface B), the number of customers of the customer group corresponding to the previous business interface A of the business interface B at the previous moment 1 in two adjacent moments is used as the target customer number of the business interface; the number of customers of the customer group corresponding to the business interface B at the next moment 2 in the two adjacent moments is used as the converted customer number of the business interface; the customer group conversion rate at each two adjacent moments of the business interface B is calculated based on the target customer number and the converted customer number corresponding to each two adjacent moments of the business interface B in the multiple consecutive moments.

[0166] According to an embodiment of the present specification, calculating the customer conversion rate at each of two adjacent moments of the business interface based on the number of target customers and the number of converted customers corresponding to each of two adjacent moments of the business interface in the plurality of consecutive moments further includes:

[0167] For each two adjacent moments of the business interface, the ratio of the number of converted customers to the number of target customers corresponding to the business interface at each two adjacent moments is calculated to obtain the customer conversion rate of the business interface at each two adjacent moments.

[0168] For example, the number of target customers corresponding to business interface B at two adjacent moments (the previous moment 1 and the next moment 2) (that is, the total number of customers in the customer base determined by business interface A at the previous moment 1) is 1 million, and the number of converted customers (that is, the total number of customers in the customer base determined by business interface B at the next moment 2) is 500,000. Then the customer base conversion rate corresponding to business interface B at the two adjacent moments (the previous moment 1 and the next moment 2) is 50%, and half of the customers are lost at business interface B (that is, when the behavioral data and basic information are obtained at the next moment 2, the customers have already exited business interface B, so the behavioral data and basic information of the lost customers cannot be obtained at the next moment 2).

[0169] According to the above steps, calculate the customer conversion rate corresponding to the business interface B at other two adjacent moments (for example, the previous moment 2 and the next moment 3, the previous moment 3 and the next moment 4).

[0170] It should be noted that the time interval between each moment can be set by the business personnel according to the expected processing time of each step in the business, and the embodiments of this specification do not limit this.

[0171] According to one embodiment of this specification, Figure 5 As shown, the business interface to be optimized is determined based on the customer conversion rate, and the business interface to be optimized further includes:

[0172] Step 501: for each business interface, determine the number of two consecutive moments at which the customer conversion rate of the business interface does not exceed a first threshold;

[0173] Step 502: Determine whether the number exceeds a second threshold;

[0174] Step 503: If yes, the business interface is the business interface to be optimized.

[0175] In the embodiment of this specification, the first threshold may be an empirical value, and the second threshold may be set by a business person according to the total number of moments, which is not limited in the embodiment of this specification.

[0176] Preferably, the number of the multiple consecutive moments is 2. That is, in the embodiment of this specification, after obtaining the behavioral data and basic information at a moment, a customer conversion rate is calculated and the business interface to be optimized and the optimization items are determined. Therefore, when the number of the multiple consecutive moments is 2, the business interface to be optimized is determined based on the customer conversion rate, and the business interface to be optimized further includes:

[0177] For each business interface, it is determined whether the customer conversion rate of the business interface does not exceed the third threshold. If so, the business interface is the business interface to be optimized.

[0178] In an embodiment of the present specification, after obtaining the behavioral data and basic information at a certain moment, a customer conversion rate is calculated and it is determined whether the customer conversion rate exceeds a third threshold. If it exceeds, it means that the customer conversion rate meets the requirements. If it does not exceed, it means that the customer conversion rate does not meet the requirements, and the business interface is used as an interface to be optimized.

[0179] The third threshold value may be an empirical value, which is not limited in the embodiments of this specification.

[0180] According to one embodiment of the present specification, the feedback information includes at least one predetermined interface defect point selected by the customer when exiting the business interface; the interface defect point can be set by bank business personnel, such as layout defects, interface waiting time defects, etc. When the customer clicks to exit on the business interface, a pop-up window including multiple interface defect points can be popped up for the customer to select, and multiple selections are supported. After the customer selects the interface defect point, he clicks Submit.

[0181] The method further comprises:

[0182] The interface defect points selected by the customer are stored in the first database table corresponding to the business interface according to the selection time, so that the interface defect points of the business interface to be optimized at each moment can be read from the first database table corresponding to the business interface to be optimized according to the time corresponding to each moment.

[0183] In the embodiment of this specification, the first database table only needs to store the selected interface defect points and the selection time, without recording customer information. In other words, the first database table does not need to record which customer submitted the interface defect point, but only needs to record the time when the interface defect point was submitted. Therefore, the embodiment of this specification supports customers to submit interface defect points anonymously, thereby ensuring customer privacy. In this way, the interface defect points of the interface to be optimized at each time can be read from the first database table corresponding to the business interface to be optimized according to the time corresponding to each time.

[0184] According to one embodiment of this specification, Figure 6 As shown, determining the optimization items of the service interface to be optimized according to the feedback information further includes:

[0185] Step 601: Determine the number of customer selections corresponding to each interface defect point among all interface defect points corresponding to the plurality of consecutive moments;

[0186] Step 602: The interface defect points whose number selected by the customers exceeds a fourth threshold are used as the optimization items.

[0187] In an embodiment of the present specification, after obtaining all interface defect points of the interface to be optimized, the number of customer selections corresponding to each interface defect point is determined, that is, the number of data entries recorded in the first database table for each interface defect point, indicating how many customers selected the same interface defect point when exiting the interface to be optimized.

[0188] If the number of customer selections exceeds the fourth threshold, it means that a large number of customers have selected the corresponding interface defect point, so the interface defect point is selected as an item to be optimized.

[0189] According to some other embodiments of this specification, the feedback information also includes the exit reason entered by the customer when exiting the interface; when the customer clicks to exit on the business interface, a pop-up window including an exit reason input box may pop up for the customer to enter, and after the customer enters the exit reason, clicks Submit.

[0190] The method further comprises:

[0191] The exit reason entered by the customer is stored in the second database table corresponding to the business interface according to the input time, so that the exit reason of the business interface to be optimized at each moment can be read from the second database table corresponding to the business interface to be optimized according to the time corresponding to each moment.

[0192] In this embodiment of the present disclosure, the second database table only needs to store the exit reason and the time of entry, without recording customer information. In other words, the second database table does not need to record the customer who submitted the exit reason, but only the time when the exit reason was submitted. Therefore, this embodiment of the present disclosure supports anonymous submission of exit reasons by customers, thereby ensuring customer privacy. The exit reason for each time instant of the optimized interface can be read from the second database table corresponding to the optimized service interface according to the time instant.

[0193] According to one embodiment of this specification, Figure 7 As shown, determining the optimization items of the service interface to be optimized according to the feedback information further includes:

[0194] Step 701: Perform text vectorization processing on each exit reason at the multiple consecutive moments to obtain multiple text vectors;

[0195] Step 702: Input each text vector into a pre-trained optimization recognition model for calculation to obtain the optimization item corresponding to each text vector, wherein the optimization recognition model is trained using the text vectors corresponding to the historical exit reasons and the marked optimization items as a training data set.

[0196] In the embodiment of this specification, the optimization recognition model is used to determine the optimization item corresponding to the exit reason in text form of user feedback. Specifically, the optimization recognition model can be a neural network model, and its training process is common knowledge in this field and will not be repeated here.

[0197] It should be noted that when the customer exits the business interface, an interface defect point and exit reason input box can be popped up at the same time. The customer can select the interface defect point and enter the exit reason. The selected interface defect point and the entered exit reason are then stored in the first database table and the second database table corresponding to the business interface respectively. Then, according to Figure 6 and Figure 7 The method shown determines the optimization items, merges and removes duplicates, and obtains the final optimization items.

[0198] Business personnel can optimize the interface to be optimized based on the optimization items, thereby improving customer conversion rate and avoiding customer loss due to reasons such as the business interface not conforming to customer habits or long interface waiting time.

[0199] Based on the same inventive concept, the embodiment of this specification also provides a business interface optimization device based on multi-dimensional customer groups, such as Figure 8 As shown, the device includes:

[0200] The information acquisition unit 801 is used to acquire the behavior data of multiple customers who conduct business through the business interface of the target business at multiple consecutive moments and the basic information of the multiple customers;

[0201] A customer group division unit 802 is configured to divide the plurality of customers into a plurality of predetermined customer groups according to the behavior data and basic information at each moment, to obtain a customer group corresponding to each moment;

[0202] The customer group conversion rate calculation unit 803 is used to calculate the customer group conversion rate of each business interface based on the correspondence between the customer group and the business interface and the number of customers of the customer group corresponding to each moment;

[0203] A business interface to be optimized determining unit 804 is configured to determine the business interface to be optimized according to the customer group conversion rate as the business interface to be optimized;

[0204] A feedback information reading unit 805 is configured to read feedback information of at least one customer at each time instant of the service interface to be optimized;

[0205] The optimization item determination unit 806 is configured to determine the optimization items of the business interface to be optimized according to the feedback information, wherein the optimization items are used to guide business personnel to optimize the business interface to be optimized.

[0206] Furthermore, according to the correspondence between the customer groups and the business interfaces and the number of customers of the customer groups corresponding to each moment, calculating the customer group conversion rate of each business interface further includes:

[0207] Sort each business interface according to the order of business processes;

[0208] For each business interface, the number of customers of the customer group corresponding to the previous business interface at the earlier of two consecutive moments is used as the target customer number of the business interface; the number of customers of the customer group corresponding to the business interface at the later of two consecutive moments is used as the converted customer number of the business interface;

[0209] The customer group conversion rate at each two adjacent moments of the business interface is calculated according to the number of target customers and the number of converted customers corresponding to each two adjacent moments of the business interface in the multiple consecutive moments.

[0210] Furthermore, calculating the customer conversion rate at each of two adjacent moments of the business interface according to the number of target customers and the number of converted customers corresponding to each of two adjacent moments in the plurality of consecutive moments further includes:

[0211] For each two adjacent moments of the business interface, the ratio of the number of converted customers to the number of target customers corresponding to the business interface at each two adjacent moments is calculated to obtain the customer conversion rate of the business interface at each two adjacent moments.

[0212] Furthermore, the business interface to be optimized is determined according to the customer conversion rate, and the business interface to be optimized further includes:

[0213] For each business interface, determine the number of two consecutive moments at which the customer conversion rate of the business interface does not exceed the first threshold;

[0214] It is determined whether the number exceeds a second threshold; if so, the business interface is the business interface to be optimized.

[0215] Furthermore, if the number of the plurality of consecutive moments is 2, determining the business interface to be optimized according to the customer conversion rate further includes:

[0216] For each business interface, it is determined whether the customer conversion rate of the business interface does not exceed the third threshold. If so, the business interface is the business interface to be optimized.

[0217] Furthermore, the plurality of customers are divided into a plurality of predetermined customer groups according to the behavior data and basic information at each moment, and obtaining the customer group corresponding to each moment further includes:

[0218] Matching the basic information and the behavioral data of the customer with predetermined rules corresponding to each customer group;

[0219] The customers are divided into the successfully matched customer groups.

[0220] Furthermore, matching the basic information and the behavior data of the customer with the predetermined rules corresponding to each customer group further includes:

[0221] The basic information and the behavior data of the customer are input into a decision tree, and matched with the predetermined rules corresponding to each of the multiple nodes in the decision tree to obtain the customer group matched with the customer.

[0222] Furthermore, the feedback information includes at least one predetermined interface defect point selected by the customer when exiting the service interface;

[0223] The device also includes: a feedback information storage unit, which is used to store the interface defect points selected by the customer in the first database table corresponding to the business interface according to the selection time, so as to read the interface defect points of the business interface to be optimized at each moment from the first database table corresponding to the business interface to be optimized according to the time corresponding to each moment.

[0224] Furthermore, the interface defect points are anonymously selected by the customer when exiting the service interface.

[0225] Furthermore, determining the optimization items of the service interface to be optimized according to the feedback information further includes:

[0226] Determine the number of customer selections corresponding to each interface defect point among all interface defect points corresponding to the multiple consecutive moments;

[0227] The interface defect points whose number of customer selections exceeds a fourth threshold are used as the optimization items.

[0228] Furthermore, the feedback information also includes the exit reason entered by the customer when exiting the interface;

[0229] The feedback information storage unit is further used to: store the exit reason entered by the customer in the second database table corresponding to the business interface according to the input time, so as to read the exit reason of the business interface to be optimized at each moment from the second database table corresponding to the business interface to be optimized according to the time corresponding to each moment.

[0230] Furthermore, determining the optimization items of the service interface to be optimized according to the feedback information further includes:

[0231] Performing text vectorization processing on each exit reason at the multiple consecutive moments to obtain multiple text vectors;

[0232] Each text vector is input into a pre-trained optimization recognition model for calculation to obtain the optimization item corresponding to each text vector, wherein the optimization recognition model is trained using the text vectors corresponding to the historical exit reasons and the annotated optimization items as a training data set.

[0233] Since the principle of solving the problem by the above device is similar to that of the above method, the implementation of the above device can refer to the implementation of the above method, and the repeated parts will not be repeated.

[0234] like Figure 9 The diagram shows the structure of the computer device according to the embodiment of this specification. The apparatus according to the embodiment of this specification may be the computer device according to this embodiment, executing the method according to the embodiment of this specification.

[0235] Computer device 902 may include one or more processing devices 904, such as one or more central processing units (CPUs), each of which may implement one or more hardware threads. Computer device 902 may also include any storage resources 906 for storing any type of information, such as code, settings, data, and the like. For example, and without limitation, storage resources 906 may include any one or more combinations of the following: any type of RAM, any type of ROM, a flash memory device, a hard disk, an optical disk, and the like. More generally, any storage resource may use any technology to store information.

[0236] Further, any storage resource may provide volatile or non-volatile retention of information.

[0237] Furthermore, any storage resource may represent a fixed or removable component of the computer device 902. In one embodiment, when the processing device 904 executes the associated instructions stored in any storage resource or combination of storage resources, the computer device 902 may perform any operation of the associated instructions. The computer device 902 also includes one or more drive systems 908, such as a hard disk drive system, an optical disk drive system, etc., for interacting with any storage resource.

[0238] The computer device 902 may also include an input / output module 910 (I / O) for receiving various inputs (via input devices 912) and for providing various outputs (via output devices 914). A specific output mechanism may include a presentation device 916 and an associated graphical user interface (GUI) 918. In other embodiments, the input / output module 910 (I / O), input devices 912, and output devices 914 may not be included, and the computer device 902 may simply be a computer device in a network. The computer device 902 may also include one or more network interfaces 920 for exchanging data with other devices via one or more communication links 922. One or more communication buses 924 couple the components described above together.

[0239] The communication link 922 may be implemented in any manner, for example, via a local area network, a wide area network (e.g., the Internet), a point-to-point connection, etc., or any combination thereof. The communication link 922 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.

[0240] The embodiments of this specification also provide a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0241] The embodiments of this specification also provide a computer-readable instruction, wherein when a processor executes the instruction, the program therein causes the processor to execute the above method.

[0242] It should be understood that in the various embodiments of the present specification, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present specification.

[0243] It should also be understood that in the embodiments of this specification, the term "and / or" is merely a description of the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent three situations: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, in the embodiments of this specification, the character " / " generally indicates that the associated objects are in an "or" relationship.

[0244] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the embodiments of this specification can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of this specification.

[0245] Those skilled in the art will clearly understand that, for the sake of convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0246] In the several embodiments provided in the embodiments of this specification, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or can be electrical, mechanical or other forms of connection.

[0247] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of this specification.

[0248] In addition, the functional units in each embodiment of the present specification may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0249] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of this specification is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the embodiment of this specification. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0250] The embodiments of this specification use specific embodiments to illustrate the principles and implementation methods of the embodiments of this specification. The description of the above embodiments is only used to help understand the methods and core ideas of the embodiments of this specification. At the same time, for those skilled in the art, based on the ideas of the embodiments of this specification, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on the embodiments of this specification.

Claims

1. A business interface optimization method based on multi-dimensional customer groups, characterized in that: The method comprises: Obtaining behavioral data of multiple customers conducting business through the business interface of the target business at multiple consecutive moments and basic information of the multiple customers; Dividing the plurality of customers into a plurality of predetermined customer groups according to the behavior data and basic information at each moment, to obtain a customer group corresponding to each moment; Calculate the customer conversion rate of each business interface based on the correspondence between the customer group and the business interface and the number of customers of the customer group corresponding to each moment; Determining the business interface that needs to be optimized according to the customer conversion rate as the business interface to be optimized; Reading feedback information of at least one customer at each of the times for the business interface to be optimized; Determining optimization items for the business interface to be optimized based on the feedback information, wherein the optimization items are used to guide business personnel to optimize the business interface to be optimized; Calculating the customer conversion rate of each business interface according to the correspondence between the customer group and the business interface and the number of customers of the customer group corresponding to each moment further includes: Sort each business interface according to the order of business processes; For each business interface, the number of customers of the customer group corresponding to the previous business interface at the earlier of two consecutive moments is used as the target customer number of the business interface; the number of customers of the customer group corresponding to the business interface at the later of two consecutive moments is used as the converted customer number of the business interface; For each two adjacent moments of the business interface, the ratio of the number of converted customers to the number of target customers corresponding to the business interface at each two adjacent moments is calculated to obtain the customer conversion rate of the business interface at each two adjacent moments.

2. The method according to claim 1, characterized in that Determining the business interface to be optimized based on the customer conversion rate, as the business interface to be optimized further includes: For each business interface, determine the number of two consecutive moments at which the customer conversion rate of the business interface does not exceed the first threshold; It is determined whether the number exceeds a second threshold; if so, the business interface is the business interface to be optimized.

3. The method according to claim 1, characterized in that If the number of the plurality of consecutive moments is 2, determining the business interface to be optimized according to the customer conversion rate, as the business interface to be optimized further includes: For each business interface, it is determined whether the customer conversion rate of the business interface does not exceed the third threshold. If so, the business interface is the business interface to be optimized.

4. The method according to claim 1, wherein Dividing the plurality of customers into a plurality of predetermined customer groups according to the behavior data and basic information at each moment, and obtaining the customer group corresponding to each moment further includes: Matching the basic information and the behavioral data of the customer with predetermined rules corresponding to each customer group; The customers are divided into the successfully matched customer groups.

5. The method according to claim 4, characterized in that Matching the basic information and the behavior data of the customer with the predetermined rules corresponding to each customer group further includes: The basic information and the behavior data of the customer are input into a decision tree, and matched with the predetermined rules corresponding to each of the multiple nodes in the decision tree to obtain the customer group matched with the customer.

6. The method according to claim 1, characterized in that The feedback information includes at least one predetermined interface defect point selected by the customer when exiting the service interface; The method further comprises: The interface defect points selected by the customer are stored in the first database table corresponding to the business interface according to the selection time, so that the interface defect points of the business interface to be optimized at each moment can be read from the first database table corresponding to the business interface to be optimized according to the time corresponding to each moment.

7. The method according to claim 6, characterized in that The interface defect points are anonymously selected by the customer when exiting the service interface.

8. The method according to claim 6, characterized in that Determining the optimization items of the service interface to be optimized according to the feedback information further includes: Determine the number of customer selections corresponding to each interface defect point among all interface defect points corresponding to the multiple consecutive moments; The interface defect points whose number of customer selections exceeds a fourth threshold are used as the optimization items.

9. The method according to claim 1, characterized in that The feedback information also includes the exit reason entered by the customer when exiting the interface; The method further comprises: The exit reason entered by the customer is stored in the second database table corresponding to the business interface according to the input time, so that the exit reason of the business interface to be optimized at each moment can be read from the second database table corresponding to the business interface to be optimized according to the time corresponding to each moment.

10. The method according to claim 9, characterized in that Determining the optimization items of the service interface to be optimized according to the feedback information further includes: Performing text vectorization processing on each exit reason at the multiple consecutive moments to obtain multiple text vectors; Each text vector is input into a pre-trained optimization recognition model for calculation to obtain the optimization item corresponding to each text vector, wherein the optimization recognition model is trained using the text vectors corresponding to the historical exit reasons and the annotated optimization items as a training data set.

11. A business interface optimization device based on multi-dimensional customer groups, characterized in that: The device comprises: An information acquisition unit, configured to acquire behavioral data of multiple customers handling business through a business interface of a target business at multiple consecutive moments and basic information of the multiple customers; A customer group division unit, configured to divide the plurality of customers into a plurality of predetermined customer groups according to the behavior data and basic information at each moment, to obtain a customer group corresponding to each moment; A customer group conversion rate calculation unit, configured to calculate the customer group conversion rate of each business interface based on the correspondence between the customer group and the business interface and the number of customers of the customer group corresponding to each moment; a business interface to be optimized determining unit, configured to determine the business interface to be optimized according to the customer group conversion rate as the business interface to be optimized; A feedback information reading unit, configured to read feedback information of at least one customer at each of the times for the service interface to be optimized; an optimization item determination unit, configured to determine optimization items of the business interface to be optimized based on the feedback information, wherein the optimization items are used to guide business personnel to optimize the business interface to be optimized; Calculating the customer conversion rate of each business interface according to the correspondence between the customer group and the business interface and the number of customers of the customer group corresponding to each moment further includes: Sort each business interface according to the order of business processes; For each business interface, the number of customers of the customer group corresponding to the previous business interface at the earlier of two consecutive moments is used as the target customer number of the business interface; the number of customers of the customer group corresponding to the business interface at the later of two consecutive moments is used as the converted customer number of the business interface; For each two adjacent moments of the business interface, the ratio of the number of converted customers to the number of target customers corresponding to the business interface at each two adjacent moments is calculated to obtain the customer conversion rate of the business interface at each two adjacent moments.

12. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 10 is implemented.

13. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 10 is implemented.

14. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 10 is implemented.

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