Multi-path behavior event analysis method and device, electronic equipment and storage medium

Abstract

The invention provides a multi-path behavior event analysis method and device, electronic equipment and a storage medium, and the method comprises the steps: positioning at least one target customer group from a preset data tag cube model according to at least one target customer group tag; determining a plurality of target behavior paths formed by the at least one target process behavior event and the target endpoint behavior event; according to the behavior event sequence corresponding to each target user, screening out a directional user with a target behavior path in the time analysis interval from the plurality of target users; and according to the plurality of directional users corresponding to each target behavior path, generating a path flow diagram corresponding to the target endpoint behavior event. The multi-path information mining and to-be-analyzed are performed on the user behavior event in combination with the tag cube, so that a plurality of behavior paths corresponding to the target customer group can be quickly and comprehensively positioned, and the user can be helped to comprehensively understand the behavior mode of the target behavior event of the target customer group.

Description

Technical Field

[0001] The present application relates to the field of data analysis technology, and in particular to a multi-path behavior event analysis method, device, electronic device and storage medium. Background Art

[0002] In the existing analysis models related to refined customer operations, single-path analysis is often performed on behavioral events or businesses that need to be observed, which cannot help users understand the flow rules and behavior patterns of users in the product. For example, a webpage provides a "debit card application" service window. If the user's behavioral event "retail customer successfully applies for a debit card" is observed or mined, during the process of the user's "retail customer successfully applies for a debit card" behavior event, conventional analysis methods may often only focus on the user's click data related to the "debit card application" business window. However, in fact, the "retail customer successfully applies for a debit card" behavior event is not only determined by the user's click data, but may also have corresponding correlations with the user's page browsing or account opening behavior. If the analysis relies too much on click data, it cannot fully mine all the user's behaviors in the "retail customer successfully applies for a debit card" event, resulting in the final analysis result of the event being too one-sided and unable to fully understand the user's related behavior patterns in the "retail customer successfully applies for a debit card" behavior event. Summary of the invention

[0003] In view of this, the purpose of the present application is to at least provide a multi-path behavior event analysis method, device, electronic device and storage medium, which, by combining the label cube to perform multi-path information mining and analysis on user behavior events, can quickly and comprehensively locate multiple behavior paths corresponding to the target customer group, helping users to fully understand the behavior patterns of the target customer group when the target behavior events occur.

[0004] This application mainly includes the following aspects:

[0005] In a first aspect, an embodiment of the present application provides a multi-path behavior event analysis method, the method comprising: based on a multi-path behavior event analysis interface, determining analysis rule configuration data corresponding to a target behavior analysis project, the analysis rule configuration data comprising at least one target customer group label, at least one target process behavior event, a target endpoint behavior event and a time analysis interval; locating at least one target customer group from a preset data label cube model according to at least one target customer group label, the at least one target customer group comprising multiple target users; determining multiple target behavior paths formed by at least one target process behavior event and a target endpoint behavior event, the target endpoint behavior event being a behavior path endpoint; screening out, from multiple target users, directional users having a target behavior path within a time analysis interval according to a behavior event sequence corresponding to each target user, the behavior event sequence describing the behavior events occurring to the user and the time when the behavior events occurred; generating, according to multiple directional users corresponding to each target behavior path, a path flow diagram corresponding to the target endpoint behavior event.

[0006] In one possible implementation, the multi-path behavior event analysis interface includes a customer group label configuration item, a process behavior event configuration item, an endpoint behavior event configuration item, an endpoint event type configuration item, and a time analysis interval configuration item, wherein at least one customer group label, at least one target process behavior event, a target endpoint behavior event, and a time analysis interval are determined in the following manner: in response to a configuration operation performed on the customer group label configuration item, at least one target customer group label is determined from a plurality of customer group labels; in response to a configuration operation performed on the process behavior event configuration item, at least one target process behavior event is determined from a plurality of behavior events; in response to a configuration operation performed on the endpoint behavior event configuration item, a target endpoint behavior event is determined from a plurality of behavior events and an endpoint event type corresponding to the target endpoint behavior event is determined; in response to a configuration operation performed on the time analysis interval configuration item, a time analysis interval is determined.

[0007] In a possible implementation, at least one target customer group is located by: determining a customer group query rule corresponding to each target customer group label; establishing a customer group query index corresponding to each customer group query rule; and locating at least one target customer group from a preset data label cube model according to the customer group query index.

[0008] In one possible implementation, the analysis rule configuration data also includes an event type corresponding to a target endpoint behavior event, and the event type includes an initial behavior event and an end behavior event, wherein multiple target behavior paths are determined in the following manner: if the target endpoint behavior event is an initial behavior event, a first target behavior path is determined, which has the target endpoint behavior event as the starting endpoint of the behavior path and involves at least one process behavior event; if the target endpoint behavior event is an end behavior event, a second target behavior path is determined, which has the target endpoint behavior event as the ending endpoint of the behavior path and involves at least one process behavior event.

[0009] In a possible implementation, the analysis rule configuration data also includes an event interval time constraint, which indicates the interval time between two adjacent process behavior events or a target endpoint behavior event and its adjacent process behavior event involved in the behavior path, wherein the targeted users corresponding to each target behavior path are screened in the following manner: for each target user, a specified sequence segment within the time analysis interval is intercepted from the behavior event sequence corresponding to the target user; based on the specified sequence segment corresponding to each target user, a plurality of candidate targeted users having a target behavior path are screened out from a plurality of target users, where the target behavior path is a first target behavior path or a second target behavior path; using the event interval time constraint and the target behavior path involved in each candidate targeted user, a plurality of first target targeted users whose first target behavior path meets the event interval time constraint are screened out from a plurality of candidate targeted users, or a plurality of second target targeted users whose second target behavior path meets the event interval time constraint are screened out from a plurality of candidate targeted users.

[0010] In a possible implementation, the step of generating a path flow graph corresponding to a target endpoint behavior event based on multiple directional users corresponding to each target behavior path includes: for a first target positioning path, based on multiple first target directional users corresponding to the first target positioning path, displaying a first path flow graph with the target endpoint behavior event as a starting event; for a second target positioning path, based on multiple second target directional users corresponding to the second target positioning path, displaying a second path flow graph with the target endpoint behavior event as an ending event.

[0011] In a possible implementation, the multipath behavior event analysis interface further includes a session interval configuration item, wherein the event interval time constraint condition is determined in the following manner: in response to an edit configuration operation performed on the session interval configuration item, the event interval time constraint condition is determined.

[0012] In a second aspect, an embodiment of the present application also provides a multi-path behavior event analysis device, the device comprising: a data configuration module, for determining analysis rule configuration data corresponding to a target behavior analysis project based on a multi-path behavior event analysis interface, the analysis rule configuration data comprising at least one target customer group label, at least one target process behavior event, a target endpoint behavior event and a time analysis interval; a customer group determination module, for locating at least one target customer group from a preset data label cube model according to at least one customer group label, the at least one target customer group comprising a plurality of target users; a behavior path determination module, for determining a plurality of target behavior paths formed by at least one target process behavior event and a target endpoint behavior event, the target endpoint behavior event being a behavior path endpoint; a user screening module, for screening out, from a plurality of target users, directional users having a target behavior path within a time analysis interval, according to a behavior event sequence corresponding to each target user, the behavior event sequence describing the behavior events occurring to the user and the time when the behavior events occurred; a path flow graph generation module, for generating a path flow graph corresponding to a target endpoint behavior event according to a plurality of directional users corresponding to each target behavior path.

[0013] In a third aspect, an embodiment of the present application further provides an electronic device comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and the machine-readable instructions are executed by the processor to execute the steps of the multi-path behavior event analysis method in the first aspect or any possible implementation manner of the first aspect.

[0014] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of multi-path behavioral event analysis in the above-mentioned first aspect or any possible implementation manner of the first aspect are performed.

[0015] The embodiment of the present application provides a multi-path behavior event analysis method, device, electronic device and storage medium, including: locating at least one target customer group from a preset data tag cube model according to at least one target customer group label; determining multiple target behavior paths formed by at least one target process behavior event and a target endpoint behavior event; screening out directional users with target behavior paths within a time analysis interval from multiple target users according to the behavior event sequence corresponding to each target user; generating a path flow diagram corresponding to the target endpoint behavior event according to multiple directional users corresponding to each target behavior path. By combining the tag cube to perform multi-path information mining and analysis on user behavior events, multiple behavior paths corresponding to the target customer group can be quickly and comprehensively located, helping users to fully understand the behavior patterns of target behavior events occurring in the target customer group.

[0016] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A flowchart of a multi-path behavior event analysis method provided by an embodiment of the present application is shown;

[0019] Figure 2 A schematic diagram of a second path flow diagram provided in an embodiment of the present application is shown;

[0020] Figure 3 A functional module diagram of a multi-path behavior event analysis device provided by an embodiment of the present application is shown;

[0021] Figure 4 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0022] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application only serve the purpose of explanation and description and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn in real proportion. The flowchart used in this application shows the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowchart can be implemented out of sequence, and the steps without logical context can be reversed in order or implemented simultaneously. In addition, those skilled in the art, under the guidance of the content of the present application, can add one or more other operations to the flowchart, or remove one or more operations from the flowchart.

[0023] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0024] In the existing analysis models related to refined customer operations, single-path analysis is often performed on behavioral events or businesses that need to be observed, which cannot help users understand the flow rules and behavior patterns of users in the product. For example, a webpage provides a "debit card application" service window. If the user's behavioral event "retail customer successfully applies for a debit card" is observed or mined, during the process of the user's "retail customer successfully applies for a debit card" behavior event, conventional analysis methods may often only focus on the user's click data related to the "debit card application" business window. However, in fact, the "retail customer successfully applies for a debit card" behavior event is not only determined by the user's click data, but may also have corresponding correlations with the user's page browsing or account opening behavior. If the analysis relies too much on click data, it cannot fully mine all the user's behaviors in the "retail customer successfully applies for a debit card" event, resulting in the final analysis result of the event being too one-sided and unable to fully understand the user's related behavior patterns in the "retail customer successfully applies for a debit card" behavior event.

[0025] Based on this, the embodiments of the present application provide a multi-path behavior event analysis method, device, electronic device and storage medium, which can quickly and comprehensively locate multiple behavior paths corresponding to the target customer group by combining the tag cube to perform multi-path information mining and analysis on user behavior events, and help users to fully understand the behavior patterns of the target customer group when the target behavior events occur, as follows:

[0026] See also Figure 1 , Figure 1 FIG. 1 is a flow chart showing a multi-path behavior event analysis method provided by an embodiment of the present application. Figure 1 As shown, the method provided in the embodiment of the present application is applied to the smart tag portrait platform, comprising the following steps:

[0027] S100: Based on the multi-path behavior event analysis interface, determine analysis rule configuration data corresponding to the target behavior analysis project.

[0028] The analysis rule configuration data includes at least one target customer group tag, at least one target process behavior event, a target endpoint behavior event, and a time analysis interval.

[0029] S200: Locate at least one target customer group from a preset data label cube model according to at least one target customer group label.

[0030] At least one target customer group includes a plurality of target users.

[0031] S300: Determine a plurality of target behavior paths formed by at least one target process behavior event and a target endpoint behavior event.

[0032] The target endpoint behavior event is the behavior path endpoint.

[0033] S400: According to the behavior event sequence corresponding to each target user, select directional users having a target behavior path within a time analysis interval from multiple target users.

[0034] The behavior event sequence describes the behavior events that occur to the user and the time when the behavior events occur.

[0035] S500: Generate a path flow graph corresponding to a target endpoint behavior event according to multiple directional users corresponding to each target behavior path.

[0036] In steps S100 to S500, when analyzing or mining a target behavior event based on the prior art, it is often based on single-path behavior analysis, that is, only one target behavior event-related path is locked and a single-path analysis is performed. Such an analysis process is difficult to fully explain or analyze the process experienced by the user in performing the target behavior event, and the analysis results are not accurate. Based on this, the present application takes the endpoint behavior event as a benchmark, combines it with its related process behavior events, constructs multiple behavior paths related to the endpoint behavior event, and further statistics and visualizes the behavior paths to form a corresponding path flow diagram. The path flow diagram not only displays the target behavior path, but also counts the directional user traffic corresponding to different target behavior paths and displays it in the direction of line thickness. This not only reasonably analyzes the user's entire behavior path for the endpoint behavior event, but also through the path flow diagram, it can intuitively let the user feel the difference between the directional users who execute the target behavior path.

[0037] Furthermore, the summary statistics of the target behavior path in this application can reveal the behavioral patterns of users in executing endpoint behavior events in the past, and based on the path flow diagram, it can also reflect the execution path tendencies of the target customer group for endpoint behavior events in the future, helping with subsequent analysis.

[0038] In step S100, the analysis rule configuration data also includes an event interval time constraint, which indicates the interval time between two adjacent process behavior events or a target endpoint behavior event and its adjacent process behavior event involved in the behavior path. The intelligent label portrait platform provides a multi-path behavior event analysis interface, which includes customer label configuration items, process behavior event configuration items, endpoint behavior event configuration items, endpoint event type configuration items and time analysis interval configuration items.

[0039] In a preferred embodiment, at least one target audience tag is determined by:

[0040] In response to a configuration operation performed on the customer group tag configuration item, at least one target customer group tag is determined from a plurality of customer group tags.

[0041] In this application, the smart tag portrait platform pre-adds multiple customer tags, such as frequent transaction customer tags and Puhui consumer loan customer tags. There is no specific restriction here. Users can configure at least one target customer tag according to actual analysis needs.

[0042] Preferably, at least one target process behavior event is determined by:

[0043] In response to a configuration operation performed on a process behavior event configuration item, at least one target process behavior event is determined from a plurality of behavior events.

[0044] Specifically, the smart tag portrait platform provides multiple types of events, including but not limited to at least one of the following: contact events, business events, contract events, product events and transaction events. Users can select at least one target process behavior event from the multiple types of events according to actual analysis needs. For example, at least one target process behavior event determined by the user from the multiple types of events includes "retail customer successfully applies for a debit card", "retail customer successfully makes an appointment to open an account", "retail customer successfully browses the homepage" and "retail customer successfully inquires about deposit business information".

[0045] In addition, for each determined target process behavior event, the user can add corresponding process event constraints to the target process behavior event according to actual analysis requirements, such as occurrence event constraints and occurrence location constraints, which are not specifically limited here.

[0046] Preferably, the endpoint behavior event configuration item provides an endpoint event selection item and an endpoint event type selection item, wherein the target endpoint behavior event is determined in the following manner:

[0047] In response to the configuration operation performed on the endpoint event selection item, the target endpoint behavior event is determined from multiple target endpoint behavior events indicated by multiple types of events; in response to the configuration operation performed on the endpoint event type selection item, the event type corresponding to the target process behavior event is determined.

[0048] In a specific embodiment, at least one target process behavior event may include a target endpoint behavior event. For example, at least one target process behavior event includes "retail customer successfully applies for a debit card", "retail customer successfully makes an appointment to open an account", "retail customer successfully browses the homepage" and "retail customer successfully inquires about deposit business information", and the target endpoint behavior event is "retail customer successfully applies for a debit card".

[0049] Preferably, the time analysis interval is determined by:

[0050] In response to a configuration operation performed on a time analysis interval configuration item, a time analysis interval is determined.

[0051] For example, according to actual event analysis needs, the time analysis interval can be the past 7 days, the past 14 days, the past month, etc., or the time analysis interval can be specified according to actual needs, such as October 1, 2023 - November 30, 2023. There is no specific restriction on the time analysis interval configuration method and the time analysis interval itself.

[0052] The data label cube model is a fact- and dimension-based database model established to meet users' needs for data query and analysis from multiple angles and levels. Its basic application is to implement OLAP (On-line Analytical Processing). The data label cube model is a technology commonly used in data analysis and indexing. It can establish multi-dimensional indexes for raw data. Analyzing data through the data label cube model can speed up data query efficiency.

[0053] In the intelligent label portrait platform system, the metadata content management of the data label cube model includes table name, dimension column, metric column, threshold column, and associated label category and label information, and finally forms a mapping relationship between SQL and metadata and data dictionary, which is saved in the relational database to realize the logical mapping from application layer to database layer and serve as a business rule parsing template.

[0054] In a preferred embodiment, step S200 includes:

[0055] Determine the customer group query rule corresponding to each target customer group label, establish a customer group query index corresponding to each customer group query rule, and locate at least one target customer group from a preset data label cube model according to the customer group query index.

[0056] In the present application, each customer group label corresponds to a customer group query rule, and the application does not directly associate the customer group label with the corresponding customer group resource package, but rather associates it with the corresponding customer group query rule. This is because the database model corresponding to the data label cube model is constantly updated, that is, the customer group resource package actually changes over time. If the resource package is bound, its updated data cannot be detected, which will cause the subsequent analysis process to be inaccurate. Therefore, the present application establishes a corresponding customer group query index through the customer group query rule indicated by the customer group label, and pulls the latest target customer group from the data label cube model based on the customer group query index, which can ensure the accuracy of the subsequent analysis results.

[0057] This application configures and generates corresponding analysis rule configuration data through different data tags built into the intelligent tag portrait platform system, and can form business rules with tags as the main body. When processing is required later, the analysis rule configuration data can be parsed by the calcite parsing engine to form a corresponding SQLNode, which is then further verified and converted into a RelNode to obtain the physical execution SQL and execute it. The target customer group is mainly formed through the arrayJoin function and combined with the customer group query rules to form the corresponding customer group query index to transfer the customer list data corresponding to the target customer group. The customer list data includes multiple target users.

[0058] In step S300, the event types corresponding to the endpoint behavior event are divided into initial behavior events and end behavior events, wherein step S300 includes:

[0059] If the target endpoint behavior event is an initial behavior event, determine a first target behavior path with the target endpoint behavior event as the starting endpoint of the behavior path and involving at least one process behavior event; if the target endpoint behavior event is an ending behavior event, determine a second target behavior path with the target endpoint behavior event as the ending endpoint of the behavior path and involving at least one process behavior event.

[0060] In one example, taking the target endpoint event as the initial behavior event, assuming that the target endpoint behavior event is the retail customer successfully browsing the homepage, and the process behavior events include the retail customer successfully making an appointment to open an account and the retail customer successfully applying for a debit card, the following first target behavior path can be determined:

[0061] The retail customer successfully browses the homepage --- the retail customer successfully applies for a debit card;

[0062] The retail customer successfully browses the homepage --- the retail customer successfully makes an appointment to open an account;

[0063] The retail customer successfully browses the homepage --- the retail customer successfully makes an appointment to open an account --- the retail customer successfully applies for a debit card;

[0064] The retail customer successfully browsed the homepage---the retail customer successfully applied for a debit card---the retail customer successfully made an appointment to open an account.

[0065] In the above four first target behavior paths, the target endpoint behavior event ("retail customer browses the homepage successfully") is used as the path starting endpoint.

[0066] Similarly, taking the target endpoint event as the end behavior event as an example, assuming that the target endpoint behavior event is the successful application of a debit card by a retail customer, the corresponding event type is an initial behavior event, and the process behavior events include the successful appointment of a retail customer to open an account and the successful browsing of the homepage by a retail customer, then the following second target behavior path can be determined:

[0067] The retail customer's account opening appointment is successful --- the retail customer's application for a debit card is successful;

[0068] The retail customer successfully browses the homepage --- the retail customer successfully applies for a debit card;

[0069] The retail customer successfully made an appointment to open an account---the retail customer successfully browsed the homepage---the retail customer successfully applied for a debit card;

[0070] The retail customer successfully browsed the homepage---the retail customer successfully made an appointment to open an account---the retail customer successfully applied for a debit card.

[0071] In the above four second target behavior paths, the target endpoint behavior event ("retail customer successfully applies for a debit card") is used as the path end endpoint.

[0072] The analysis rule configuration data also includes event interval time constraints, which indicate the interval time between two adjacent process behavior events or target endpoint behavior events involved in the behavior path and their adjacent process behavior events. For example, taking the above-mentioned second target behavior path "retail customer browses the homepage successfully---retail customer makes an appointment for account opening successfully---retail customer applies for a debit card successfully" as an example, the event interval time constraint constrains the interval time between "retail customer browses the homepage successfully---retail customer makes an appointment for account opening successfully" and "retail customer makes an appointment for account opening successfully---retail customer applies for a debit card successfully".

[0073] Preferably, the multipath behavior event analysis interface also provides a session interval configuration item, wherein the event interval time constraint condition is determined by:

[0074] In response to the edit configuration operation performed on the session interval configuration item, a target interval time is determined, and the target interval time is determined as an event interval time constraint condition. For example, the target interval time may be 1 day.

[0075] In a preferred embodiment, step S400 includes:

[0076] For each target user, a specified sequence segment within the time analysis interval is intercepted from the behavior event sequence corresponding to the target user. According to the specified sequence segment corresponding to each target user, multiple candidate directional users having a target behavior path are screened out from multiple target users, and the target behavior path is a first target behavior path or a second target behavior path. By using the event interval time constraint condition and the target behavior path involved in each candidate directional user, multiple first target directional users whose first target behavior path meets the event interval time constraint condition are screened out from multiple candidate directional users, or multiple second target directional users whose second target behavior path meets the event interval time constraint condition are screened out from multiple candidate directional users.

[0077] In a specific implementation, the designated sequence fragment is a target behavior event sequence formed by behavioral events occurring in a user within a time analysis interval. Taking the target behavior path as the second target behavior path as an example, first, based on the designated sequence fragment corresponding to each target user, determine whether there is a second target behavior path in the target behavior event sequence corresponding to the target user. If there is a second target behavior path, the target user is determined as a candidate directional user. Furthermore, using the interval time indicated by the event interval time constraint, multiple second target directional users whose corresponding second target behavior paths meet the event interval time constraint are screened from the multiple candidate directional users.

[0078] In a preferred embodiment, step S500 includes:

[0079] For the first target positioning path, based on the multiple first target oriented users corresponding to the first target positioning path, a first path flow diagram with the target endpoint behavior event as the starting event is displayed. For the second target positioning path, based on the multiple second target oriented users corresponding to the second target positioning path, a second path flow diagram with the target endpoint behavior event as the ending event is displayed.

[0080] For an example, see Figure 2 , Figure 2 The schematic diagram of a second path flow diagram provided by the embodiment of the present application is shown. Taking "retail customer successfully applies for a debit card" as the path end endpoint, the target process behavior events include "retail customer successfully browses the homepage", "retail customer successfully makes an appointment to open an account", "retail customer successfully applies for a debit card" and "retail customer successfully consults about deposit services", then when generating the following Figure 2As shown in the second path flow diagram, in the second path flow diagram, as long as "retail customer successfully applies for a debit card" is used as the path end endpoint, it is identified as a second target path, such as "retail customer successfully browses the homepage---retail customer successfully applies for a debit card" and "retail customer successfully browses the homepage---retail customer successfully makes an appointment to open an account---retail customer successfully consults about deposit business---retail customer successfully applies for a debit card", etc.

[0081] The line between two adjacent behavior events in the second target path represents behavior events that occur continuously within the target time interval, and the thickness of the line reflects the user flow of the second target-oriented user in which the two adjacent behavior events occur.

[0082] The generation method of the first path flow diagram is similar to that of the second path flow diagram, and will not be described in detail here.

[0083] In other words, the path flow graph can reflect the user's preferences for subsequent behavioral events during the execution of the target endpoint behavioral event, thereby facilitating the provision of more personalized services to users in a targeted manner. For example, after obtaining the path flow graph, the behavioral events of interest to the target user are determined based on the behavioral path indicated by the path flow graph, thereby recommending content or products corresponding to the behavioral events of interest to the target user, thereby improving user satisfaction and stickiness.

[0084] On the other hand, the path flow diagram can also help users identify high-conversion target behavior paths and low-conversion target behavior paths related to target endpoint behavior events. Specifically, high and low conversion paths can be identified through the user traffic indicated by the target behavior path. This makes it easier to subsequently optimize the low-conversion path or guide users to the high-conversion path, thereby improving the overall conversion rate.

[0085] Based on the same application concept, the embodiment of the present application also provides a multi-path behavior event analysis device corresponding to the multi-path behavior event analysis method provided in the above embodiment. Since the principle of solving the problem by the device in the embodiment of the present application is similar to the multi-path behavior event analysis method in the above embodiment of the present application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0086] See also Figure 3 , Figure 3 FIG. 2 shows a functional module diagram of a multi-path behavior event analysis device provided by an embodiment of the present application. Figure 3 As shown, the device comprises:

[0087] The data configuration module 600 is used to determine the analysis rule configuration data corresponding to the target behavior analysis project based on the multi-path behavior event analysis interface, and the analysis rule configuration data includes at least one target customer group label, at least one target process behavior event, a target endpoint behavior event and a time analysis interval;

[0088] A customer group determination module 610 is used to locate at least one target customer group from a preset data tag cube model according to at least one target customer group tag, wherein the at least one target customer group includes a plurality of target users;

[0089] A behavior path determination module 620 is used to determine a plurality of target behavior paths formed by at least one target process behavior event and a target endpoint behavior event, wherein the target endpoint behavior event is a behavior path endpoint;

[0090] The user screening module 630 is used to screen out targeted users with target behavior paths within the time analysis interval from multiple target users according to the behavior event sequence corresponding to each target user, where the behavior event sequence describes the behavior events that occurred to the user and the time when the behavior events occurred;

[0091] The path flow graph generation module 640 is used to generate a path flow graph corresponding to a target endpoint behavior event according to multiple directional users corresponding to each target behavior path.

[0092] Based on the same application idea, please refer to Figure 4 , Figure 4 FIG. 1 is a schematic diagram showing the structure of an electronic device provided by an embodiment of the present application. Figure 4 As shown, the electronic device 700 includes: a processor 710, a memory 720 and a bus 730. The memory 720 stores machine-readable instructions executable by the processor 710. When the electronic device 700 is running, the processor 710 and the memory 720 communicate through the bus 730. The machine-readable instructions are executed by the processor 710 when it is running, such as the steps of the multi-path behavior event analysis method provided in any of the above embodiments.

[0093] Based on the same application concept, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the multi-path behavior event analysis method provided in the above embodiment are executed.

[0094] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the system and device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. In the several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0095] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0096] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0097] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the 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 to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0098] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A multi-path behavior event analysis method, characterized in that: The method comprises: Based on the multi-path behavior event analysis interface, determining analysis rule configuration data corresponding to the target behavior analysis project, wherein the analysis rule configuration data includes at least one target customer group label, at least one target process behavior event, a target endpoint behavior event, and a time analysis interval; According to the at least one target customer group label, locating at least one target customer group from a preset data label cube model, wherein the at least one target customer group includes a plurality of target users; Determine a plurality of target behavior paths formed by at least one target process behavior event and a target endpoint behavior event, the target endpoint behavior event being a behavior path endpoint; According to the behavior event sequence corresponding to each target user, a directional user having a target behavior path within the time analysis interval is screened out from the multiple target users, wherein the behavior event sequence describes the behavior events occurring to the user and the time when the behavior events occurred; According to the multiple directional users corresponding to each target behavior path, a path flow graph corresponding to the target endpoint behavior event is generated.

2. The method according to claim 1, characterized in that The multi-path behavior event analysis interface includes customer group label configuration items, process behavior event configuration items, endpoint behavior event configuration items, endpoint event type configuration items, and time analysis interval configuration items. The at least one target customer group tag, at least one target process behavior event, target endpoint behavior event and time analysis interval are determined in the following manner: In response to the configuration operation performed on the customer group label configuration item, determining at least one target customer group label from a plurality of customer group labels; In response to a configuration operation performed on the process behavior event configuration item, determining at least one target process behavior event from a plurality of behavior events; In response to a configuration operation performed on the endpoint behavior event configuration item, determining a target endpoint behavior event from a plurality of behavior events and determining an endpoint event type corresponding to the target endpoint behavior event; In response to a configuration operation performed on the time analysis interval configuration item, a time analysis interval is determined.

3. The method according to claim 1, characterized in that Identify at least one target audience by: Determine the customer group query rules corresponding to each target customer group label; Establish a customer group query index corresponding to each customer group query rule; At least one target customer group is located from the preset data tag cube model according to the customer group query index.

4. The method according to claim 1, characterized in that The analysis rule configuration data also includes an event type corresponding to the target endpoint behavior event, and the event type includes an initial behavior event and an end behavior event. Among them, multiple target behavior paths are determined in the following ways: If the target endpoint behavior event is an initial behavior event, determining a first target behavior path with the target endpoint behavior event as the starting endpoint of the behavior path and involving at least one process behavior event; If the target endpoint behavior event is an end behavior event, a second target behavior path is determined, which has the target endpoint behavior event as the end endpoint of the behavior path and involves at least one process behavior event.

5. The method according to claim 4, characterized in that The analysis rule configuration data also includes an event interval time constraint condition, which indicates the interval time between two adjacent process behavior events involved in the behavior path or between a target endpoint behavior event and its adjacent process behavior event. Among them, the targeted users corresponding to each target behavior path are screened in the following ways: For each target user, extracting a specified sequence segment within the time analysis interval from the behavior event sequence corresponding to the target user; According to the designated sequence fragment corresponding to each target user, a plurality of candidate directional users having a target behavior path are screened out from the plurality of target users, where the target behavior path is a first target behavior path or a second target behavior path; Using the event interval time constraint and the target behavior path involved in each candidate directional user, a plurality of first target directional users whose first target behavior paths meet the event interval time constraint are selected from the plurality of candidate directional users, Or, a plurality of second target oriented users whose second target behavior paths meet the event interval time constraint condition are screened from the plurality of candidate oriented users.

6. The method according to claim 5, characterized in that The step of generating a path flow graph corresponding to a target endpoint behavior event according to multiple directional users corresponding to each target behavior path includes: For the first target positioning path, according to the multiple first target directional users corresponding to the first target positioning path, a first path flow diagram with the target endpoint behavior event as the starting event is displayed; For the second target positioning path, according to multiple second target directional users corresponding to the second target positioning path, a second path flow diagram with the target endpoint behavior event as the end event is displayed.

7. The method according to claim 5, characterized in that The multipath behavior event analysis interface also includes a session interval configuration item, The event interval time constraint condition is determined in the following manner: In response to an edit configuration operation performed on the session interval configuration item, the event interval time constraint condition is determined.

8. A multi-path behavior event analysis device, characterized in that: The device comprises: A data configuration module, for determining analysis rule configuration data corresponding to a target behavior analysis project based on a multi-path behavior event analysis interface, wherein the analysis rule configuration data includes at least one target customer group label, at least one target process behavior event, a target endpoint behavior event, and a time analysis interval; A customer group determination module, configured to locate at least one target customer group from a preset data tag cube model according to the at least one target customer group tag, wherein the at least one target customer group includes a plurality of target users; A behavior path determination module, used to determine a plurality of target behavior paths formed by at least one target process behavior event and a target endpoint behavior event, wherein the target endpoint behavior event is a behavior path endpoint; A user screening module, configured to screen out targeted users having a target behavior path within the time analysis interval from the plurality of target users according to a behavior event sequence corresponding to each target user, wherein the behavior event sequence describes the behavior events occurring to the user and the time when the behavior events occurred; The path flow graph generation module is used to generate a path flow graph corresponding to a target endpoint behavior event according to multiple directional users corresponding to each target behavior path.

9. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and the machine-readable instructions are executed by the processor to execute the steps of the multi-path behavior event analysis method as described in any one of claims 1 to 7.

10. 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 steps of the multi-path behavior event analysis method according to any one of claims 1 to 7 are executed.

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