User transformation analysis method and device and electronic equipment

Abstract

The invention provides a user transformation analysis method and device and electronic equipment, and the method comprises the steps: carrying out SQL analysis processing on funnel event label data, a calculation time interval and a transformation window period, and generating a transformation data query statement corresponding to each calculation date; analyzing the customer group label data by using a label cube model, determining a customer group query rule indicated by the customer group label data, and generating a customer group query statement corresponding to the target customer group according to the customer group query rule; performing SQL (Structured Query Language) assembly conversion on the customer group query statement and the conversion data query statement corresponding to each calculation date by utilizing a label cube model, and generating a conversion summary query statement corresponding to a calculation time interval; and according to the conversion summary query statement, determining a conversion analysis result of the target customer group for the funnel event. According to the method, the label cube and the window sliding function are combined to jointly realize the reserved transformation analysis of the target customer group, so that complex data calling and calculation are avoided, and the analysis efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of funnel insight analysis, and in particular to a user conversion analysis method, device and electronic device. Background Art

[0002] The existing analysis model for refined customer operations is unable to monitor the entire marketing process, such as detecting the customer conversion rate in each process from launching the app to "payment success". Especially for processes with relatively standardized business processes, long cycles, and more links, a technology is needed that can intuitively discover and explain problems in the business process, and at the same time scientifically reflect the customer behavior status and the important analysis model of the user conversion rate at each stage from the starting point to the end point.

[0003] The prior art provides a funnel analysis model based on customer funnel insight analysis technology to determine the user's conversion rate for a target event. However, in actual operation, the funnel analysis model provided by the prior art involves many analysis scenarios and involves complex data processing and calculation, which reduces the processing efficiency of the prior funnel analysis model. Summary of the invention

[0004] In view of this, the purpose of this application is to at least provide a user conversion analysis method, device and electronic device, which combines the label cube with the window sliding function to jointly realize the retention conversion analysis of the target customer group, avoid complex data calls and calculations, and improve analysis efficiency.

[0005] This application mainly includes the following aspects:

[0006] In the first aspect, an embodiment of the present application provides a user conversion analysis method, the method comprising: determining the rule configuration data corresponding to the funnel event through the funnel analysis interface, the rule configuration data comprising customer group label data, funnel event label data, calculation time interval and conversion window period, the calculation time interval comprising at least one continuous calculation date, and the conversion window period being the effective conversion time corresponding to each calculation date; using a pre-created label cube model and a preset window sliding function to perform SQL parsing processing on the funnel event label data, the calculation time interval and the conversion window period, generating a conversion data query statement corresponding to each calculation date, and the conversion data query statement is used to determine the conversion window period under the calculation date The corresponding first conversion data table is for conversion users who have completed a conversion event within the calculation date and its corresponding conversion window period; the label cube model is used to parse the customer group label data, determine the customer group query rules indicated by the customer group label data, and generate a customer group query statement corresponding to the target customer group according to the customer group query rules. The customer group query statement is used to determine the target customer list corresponding to the target customer group; the label cube model is used to perform SQL assembly conversion on the customer group query statement and the conversion data query statement corresponding to each calculation date to generate a conversion summary query statement corresponding to the calculation time interval; based on the conversion summary query statement, the conversion analysis results of the target customer group for the funnel event are determined.

[0007] In one possible implementation, the funnel event label data includes an event label sequence formed by multiple funnel analysis event labels, each funnel analysis event label indicates a corresponding funnel analysis event, and the funnel analysis event indicated by the funnel analysis event label at the first position in the event label sequence is defined as the funnel initial event, wherein a conversion data query statement corresponding to each calculation date is created in the following manner: the conversion window period, the calculation time interval, the triggering sequence of the funnel analysis event and multiple funnel analysis event labels are brought into a preset window sliding function to generate a first target query statement corresponding to each funnel analysis event, the first target query statement is used to locate the conversion data table that successfully triggers the funnel analysis event in the triggering sequence within the conversion window period; the first target query statement corresponding to the funnel end analysis event on the calculation date is determined as the conversion data query statement.

[0008] In a possible implementation, the method further includes: bringing the conversion window period, the calculation time interval, the triggering order of the funnel analysis event and multiple funnel analysis event tags into a preset window sliding function, generating a second target query statement corresponding to each funnel analysis event, the second target query statement is used to determine the churned user data table, and the churned user data table is used to locate the churned users who did not complete subsequent funnel analysis events in the triggering order within the conversion window period.

[0009] In a possible implementation, the method also includes: determining whether there is comparison dimension label data in the rule configuration data, the comparison dimension label data indicating the analysis dimension for conversion analysis of the target customer group; if there is comparison dimension label data in the rule configuration data, using the label cube model to perform SQL assembly conversion on the conversion data query statement corresponding to each calculation date and different comparison dimensions respectively, and determining the target conversion data query statement for each calculation date under different comparison dimensions, the target conversion data query statement is used to locate the second conversion data table, and the second conversion data table includes users in the first conversion data table that meet the characteristics indicated by the comparison dimension.

[0010] In a possible implementation, the step of determining the conversion analysis results of the target customer group for funnel events based on the conversion summary query statement includes: using the signature cube model to perform SQL assembly conversion on the target customer list and the target conversion data query statement corresponding to each calculation date in each comparison dimension, and generate a target conversion summary query statement corresponding to each comparison dimension; running the target conversion summary query statement corresponding to each comparison dimension, and determining the third conversion data table under each comparison dimension, the third conversion data table including the user intersection between the second conversion data table of the corresponding calculation time interval under the comparison dimension and the target customer list after merging; according to the third conversion data table under each comparison dimension, determining the conversion analysis results of the target customer group for funnel events under each comparison dimension.

[0011] In a possible implementation, the conversion analysis result includes the overall conversion rate corresponding to the entire funnel event, wherein the conversion analysis result of the target customer group for the funnel event under each comparison dimension is determined in the following manner: using a label cube, according to the funnel initial event analysis label and the calculation time interval, a funnel initial event query statement is determined, and the funnel initial event query statement is used to determine the funnel initial event data table of users who have experienced the funnel initial event within the calculation time interval; the number of first users corresponding to the funnel initial event data table and the number of second users corresponding to the third conversion data table are counted respectively; and the ratio between the second number of users and the first number of users is determined as the overall conversion rate corresponding to the funnel event.

[0012] In a possible implementation, the conversion analysis results also include the number of conversions and the number of lost people corresponding to each funnel analysis event, wherein the conversion analysis results of the target customer group for the funnel event under each comparison dimension are determined in the following manner: for each funnel analysis event, the following processing is performed: the first target query statement of the funnel analysis event on each calculation date is assembled and processed to generate a third target query statement, the third target query statement is used to locate the pending conversion number statistics table of the funnel analysis event in the calculation time interval, the pending conversion number statistics table records the number of conversions corresponding to the funnel analysis event in the calculation time interval; the third target query statement corresponding to the funnel analysis event is assembled with the target customer list to generate a fourth target query statement, the fourth target query statement is used to locate the target conversion number statistics table of the funnel analysis event in the calculation time interval, the target conversion number statistics table is a combination of the pending conversion number statistics table and the target customer list. The intersection between the target customer lists; the number of users recorded in the target conversion number statistics table is determined as the conversion number of the funnel analysis event; the second target query statement of the funnel analysis event under each calculation date is assembled and processed to generate a fifth target query statement, the fifth target query statement is used to determine the pending loss number statistics table of the funnel analysis event under the calculation time interval, the pending loss number statistics table records the loss number corresponding to the funnel analysis event in the calculation time interval; for each funnel analysis event, the fifth target query statement corresponding to the funnel analysis event is assembled with the target customer list to generate a sixth target query statement, the sixth target query statement is used to locate the target loss number statistics table of the funnel analysis event under the calculation time interval, the target loss number statistics table is the intersection between the pending loss number statistics table and the target customer list; the number of users recorded in the target loss number statistics table is determined as the loss number of the funnel analysis event.

[0013] In a possible implementation, the method further includes: displaying the conversion analysis results of the funnel events for the target customer group in each comparison dimension by region.

[0014] In the second aspect, an embodiment of the present application also provides a user conversion analysis device, which includes: a rule configuration module, which is used to determine the rule configuration data corresponding to the funnel analysis event through the funnel analysis interface, the rule configuration data including customer group label data, funnel event label data, calculation time interval and conversion window period, the calculation time interval includes at least one continuous calculation date, and the conversion window period is the effective conversion time corresponding to each calculation date; a first parsing module, which is used to use a pre-created label cube model and a preset window sliding function to perform SQL parsing on the funnel event label data, the calculation time interval and the conversion window period, and generate a conversion data query statement corresponding to each calculation date, and the conversion data query statement is used to determine the conversion window period under the calculation date. The first conversion data table is for conversion users who have completed a conversion event within the calculation date and its corresponding conversion window period; the second parsing module is used to use the label cube model to parse the customer group label data, determine the customer group query rules indicated by the customer group label data, and generate a customer group query statement corresponding to the target customer group according to the customer group query rules, and the customer group query statement is used to determine the target customer list corresponding to the target customer group; the assembly module is used to use the label cube model to perform SQL assembly conversion on the customer group query statement and the conversion data query statement corresponding to each calculation date, and generate a conversion summary query statement corresponding to the calculation time interval; the analysis module is used to determine the conversion analysis result of the target customer group for the funnel event according to the conversion summary query statement.

[0015] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: 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 user conversion analysis method in the first aspect or any possible implementation manner of the first aspect.

[0016] The embodiment of the present application provides a user conversion analysis method, device and electronic device, including: performing SQL parsing processing on funnel event label data, calculation time interval and conversion window period, generating conversion data query statements corresponding to each calculation date; using the label cube model, performing SQL assembly conversion on the customer group query rules indicated by the customer group label data, the comparison dimension label data and the conversion data query statements corresponding to each calculation date, generating conversion summary query statements corresponding to different dimensions under the calculation time interval; according to the conversion summary query statements, determining the conversion analysis results of the target customer group for the funnel event. The present application realizes flexible configuration of funnel analysis events through system built-in labels, avoids complex data calls and calculations, improves analysis efficiency, and responds to different conversion analysis requirements. Through multi-dimensional label configuration, combined with label cube and window sliding function, the conversion analysis results of the target customer group for the funnel analysis event are analyzed from different analysis analysis dimensions, and the expansion of different analysis directions of the target customer group for the funnel analysis event is realized. The intuitive display of the comparison results of the customer group funnel under different dimensions can provide optimization ideas for the funnel event from the perspective of differences.

[0017] 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

[0018] 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.

[0019] Figure 1 A flow chart of a user conversion analysis method provided by an embodiment of the present application is shown;

[0020] Figure 2 A functional module diagram of a user conversion analysis device provided in an embodiment of the present application is shown;

[0021] Figure 3 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] The prior art provides a funnel analysis model based on customer funnel insight analysis technology to determine the user's conversion rate for a target event. However, in actual operation, the funnel analysis model provided by the prior art involves many analysis scenarios and involves complex data processing and calculation, which reduces the processing efficiency of the prior funnel analysis model.

[0025] Based on this, the embodiments of the present application provide a user conversion analysis method, device and electronic device, which realize the retention conversion analysis of the target customer group by combining the label cube with the window sliding function, avoiding complex data calls and calculations, and improving the analysis efficiency, as follows:

[0026] See also Figure 1 , Figure 1 A flow chart of a user conversion analysis method provided by an embodiment of the present application is shown. Figure 1 As shown, the method provided in the embodiment of the present application comprises the following steps:

[0027] S100: Determine rule configuration data corresponding to a funnel event through a funnel analysis interface.

[0028] The rule configuration data includes customer group label data, funnel event label data, calculation time interval and conversion window period. The calculation time interval includes at least one continuous calculation date, and the conversion window period is the effective conversion time corresponding to each calculation date.

[0029] S200: Use the pre-created label cube model and the preset window sliding function to perform SQL parsing on the funnel event label data, calculation time interval and conversion window period to generate a conversion data query statement corresponding to each calculation date.

[0030] The conversion data query statement is used to determine the first conversion data table corresponding to the conversion window period under the calculation date. The first conversion data table is for conversion users who completed a conversion event within the calculation date and its corresponding conversion window period.

[0031] S300: parse the customer group label data using a label cube model to determine the customer group query rule indicated by the customer group label data.

[0032] S400: Generate a customer group query statement corresponding to the target customer group according to the customer group query rule.

[0033] The customer group query statement is used to determine the target customer list corresponding to the target customer group.

[0034] S500: Use the label cube model to perform SQL assembly conversion on the customer group query statement and the conversion data query statement corresponding to each calculation date to generate a conversion summary query statement corresponding to the calculation time interval.

[0035] S600: Determine the conversion analysis result of the target customer group for the funnel event according to the conversion summary query statement.

[0036] In specific implementation, the label cube model is a database model based on facts and different data observation angles (i.e., dimensions) established to meet the needs of users to query and analyze data from multiple angles and levels. Its basic application is to realize OLAP (Online Analytical Processing), which is a technology commonly used in data analysis and indexing. It can establish multi-dimensional indexes for original data.

[0037] Analyzing data through the label cube model can speed up data query efficiency. In the intelligent label portrait platform system, the label cube model metadata content management includes table name, dimension column, metric column, threshold column, associated label category, label information, and finally forms a mapping relationship between SQL and metadata and data dictionary, and saves it to the relational database to realize the logical mapping from the application layer to the database layer and as a business rule parsing template.

[0038] In steps S100 to S600, the user conversion method provided in the present application is applied to an intelligent label portrait platform. The intelligent label portrait platform provides a funnel analysis interface. The funnel analysis interface includes multiple configuration items for forming rule configuration data corresponding to funnel analysis events. The multiple configuration items are linked to the label cube model. The configuration of the multiple configuration items is completed through label form data, and the corresponding rule configuration data is generated. The rule configuration data is parsed using the label cube model, and combined with the corresponding window sliding function, a conversion summary query statement is generated for determining the conversion events that occur in the target customer group users within the statistical calculation time interval, and the final conversion analysis result is determined based on the conversion summary query statement. In this process, the label cube model is mainly used to complete the conversion processing of the rule configuration data corresponding to the funnel analysis event, and the corresponding executable conversion summary query statement is generated to improve the efficiency of the conversion analysis of the target customer group.

[0039] In step S100 , the multiple configuration items provided in the funnel analysis interface include a customer group configuration item, a funnel event configuration item, a calculation time interval configuration item, and a conversion window configuration item.

[0040] Specifically, customer group label data is generated in the following ways:

[0041] In response to a selection operation performed on a customer group configuration item, multiple customer group tags are displayed, each customer group tag indicates a customer group (e.g., a lost customer group, a young customer group with a diamond card for a credit card), a selection operation on a target customer group tag is received, and customer group tag data is generated, the customer group tag data indicating the target customer group.

[0042] Preferably, the funnel event label data includes an event label sequence formed by a plurality of funnel analysis event labels, each funnel analysis event label indicates a corresponding funnel analysis event, and the funnel event label data is generated in the following manner:

[0043] In response to the selection operation performed on the funnel event configuration item, the funnel event configuration interface is displayed, and the funnel event configuration interface includes a funnel event sequence configuration item. For the funnel event sequence configuration item, funnel analysis events are added in sequence and an event tag sequence formed by multiple funnel analysis event tags is formed. The funnel event sequence configuration item provides a funnel analysis event configuration item. In a specific implementation, each funnel analysis event tag is determined in the following manner:

[0044] In response to a selection operation performed on a funnel analysis event configuration item, multiple event configuration controls are displayed, and different event configuration controls are used to configure different types of events. Specifically, the multiple event configuration controls include but are not limited to at least one of the following items: a contact event configuration control, a business event configuration control, a contract event configuration control, a product event configuration control, and a transaction event configuration control. In response to a selection operation performed on a target event configuration control, multiple event selection items corresponding to the target event configuration control are displayed. In response to a selection operation performed on a target event selection item, an event list corresponding to the target event selection item is displayed, and the event list includes multiple behavior event tags. In response to a selection operation performed on a target behavior event tag, the target behavior event tag is determined as a funnel analysis event time tag, and the funnel analysis event time tag is used to indicate a corresponding funnel analysis event.

[0045] For example, the multiple event selection items corresponding to the contact event configuration control include but are not limited to at least one of the following items: an application event selection item, a participation event selection item, a browsing event selection item, and a login event selection item, receiving a selection operation performed on the application event selection item, and expanding multiple behavioral events under the application event category, such as a retail customer applying for a credit card or a retail customer applying for a loan.

[0046] In the present application, the present application may use the adding order corresponding to the multiple funnel analysis event labels as the triggering order corresponding to the multiple funnel analysis events and generate a corresponding event label sequence.

[0047] Generally, the funnel analysis event indicated by the funnel analysis event label at the first position in the event label sequence is defined as the funnel initial event, and the funnel analysis event indicated by the funnel analysis event label at the last position in the event label sequence is defined as the funnel end event.

[0048] In another preferred embodiment, in response to a configuration operation performed on a calculation time interval configuration item, a calculation time interval is determined. The calculation time interval configuration item provides multiple options, including but not limited to at least one of the following items: the past seven days, the past 14 days, the past month, the past year, and a custom calculation time interval. In response to a configuration operation performed on a window conversion period configuration item, a window conversion period is determined. Assuming that the calculation time interval is from March 1, 2023 to March 15, 2023, then each day in the interval from March 1, 2023 to March 15, 2023 is counted as a calculation date, and the window conversion period refers to the calculation date. Assuming that the window conversion period is 1 day, the funnel event sequence is event A---event B-----event C, then if the user triggers step A from March 1, 2023 to March 15, 2023, and within 1 day after step A occurs, event B and event C are triggered in sequence, then the user is deemed to have completed a funnel conversion.

[0049] In a preferred embodiment, the multiple configuration items provided in the funnel analysis interface also include a comparison dimension configuration item. The user can decide whether to configure and generate a comparison dimension label according to the actual needs of the funnel event. Specifically, the comparison dimension label data is generated in the following manner:

[0050] In response to a selection operation performed on a comparison dimension configuration item, a plurality of dimension tags are displayed, and in response to a selection operation performed on a plurality of target dimension tags, comparison dimension tag data are generated.

[0051] In a specific embodiment, multiple dimension labels are system built-in labels, and users can gradually select target dimension labels according to the types described in the labels. Specifically, users select dimension labels according to the actual comparison requirements of the funnel events. For example, the comparison dimensions required by the funnel events are gender = male and gender = female. There is no specific restriction on the comparison dimensions here, and users are allowed to select multiple dimension labels at the same time to achieve the final dimension comparison, such as the two comparison dimensions of "gender = male, age 20-30" and "gender = female, age 20-30". In other words, the corresponding labels under each comparison dimension can be combined according to actual needs.

[0052] Specifically, users are segmented in advance according to their characteristics (such as behavior, demographic information, etc.) to form multi-dimensional labels. Multi-dimensional segmentation and presentation of customer conversion status through dimensional label configuration items are more suitable for meeting the conversion analysis needs of different customers and expanding the analysis direction. In addition, the comparison of funnels of customer groups with different attributes can provide optimization ideas for funnel events from a difference perspective.

[0053] In a preferred embodiment, the conversion data query statement corresponding to each calculation date is created in the following manner:

[0054] The conversion window period, calculation time interval, triggering order of funnel analysis events and multiple funnel analysis event labels are brought into the preset window sliding function to generate the first target query statement corresponding to each funnel analysis event. The first target query statement is used to locate the conversion data table that successfully triggers the funnel analysis event in the triggering order within the conversion window period, and the first target query statement corresponding to the funnel end analysis event on the calculation date is determined as the conversion data query statement.

[0055] In a specific embodiment, assuming that the funnel event sequence that forms the funnel event is event A----event B----event C, then event A is the funnel initial event, event C is the funnel end event, the calculation time interval is from March 1, 2023 to March 15, 2023, and the conversion window period is 1 day. Then, after the conversion window period, the calculation time interval, the triggering order of the funnel analysis event and multiple funnel analysis event labels are brought into the preset window sliding function, the first target query statement of event A on each calculation date, the first target query statement of event B on each calculation date and the first target query statement of event C on each calculation date are generated. Assuming that the calculation date is March 1, 2023, the first target query statement corresponding to event A on this date is used to locate the conversion data table where event A occurs on the calculation date, the first target query statement corresponding to event B on this date is used to locate the conversion data table where event B is triggered after event A occurs on the calculation date, and the first target query statement corresponding to event C on this date is used to locate the conversion data table where event A and event B occur successively and then event C occurs on the calculation date.

[0056] The window sliding function calculates the unit time corresponding to the time interval as the unit step for sliding calculation, and the length corresponding to the window sliding function is the length corresponding to the conversion window period + 1 (the window sliding length needs to cover the calculation date + conversion window period). Therefore, each sliding moves a calculation date. Because each sliding of the window can determine a user who successfully completed the conversion on the calculation date and its corresponding conversion window period, combined with the above analysis, for each calculation date, the conversion data table obtained by querying the first target query statement corresponding to the funnel end analysis event on the calculation date is the first conversion data table. Therefore, the first target query statement corresponding to the funnel end analysis event on the calculation date is the conversion data query statement.

[0057] In the present application, for conversion data query statements, in the process of counting successful conversion events (completing events A----B----C in sequence), if some other behavioral events are interspersed, for example, when the corresponding conversion window period limit is met, the sequence of behavioral events that occur to the user is A>X>B>X>C, where X represents any behavioral event, then the user is still regarded as having completed a successful funnel conversion.

[0058] In another preferred embodiment, step S200 further includes:

[0059] The conversion window period, calculation time interval, triggering order of funnel analysis events and multiple funnel analysis event tags are brought into the preset window sliding function to generate a second target query statement corresponding to each funnel analysis event. The second target query statement is used to determine the churned user data table. The churned user data table is used to locate the churned users who did not complete the subsequent funnel analysis events in the triggering order within the conversion window period.

[0060] The method for determining the second target query statement is similar to that for the first target query statement, and will not be described in detail here.

[0061] In a specific embodiment of the present application, in the process of counting lost users using the second target query statement, taking event A as an example, when the corresponding conversion window period limit is met, A>C are triggered in sequence, then it is determined that the user has not completed the conversion of the funnel, and the user is recorded as a lost user of step A. Similarly, taking event B as an example, when the corresponding conversion window period limit is met, A>B>D are triggered in sequence, then it is determined that the user has not completed the conversion of the funnel, and the user is recorded as a lost user of step B.

[0062] In a preferred embodiment, after executing step S200, the method provided by the present application further includes:

[0063] Determine whether there is comparison dimension label data in the rule configuration data, the comparison dimension label data indicates the analysis dimension for conversion analysis of the target customer group. If there is comparison dimension label data in the rule configuration data, use the label cube model to perform SQL assembly conversion on the conversion data query statement corresponding to each calculation date and different comparison dimensions respectively, and determine the target conversion data query statement for each calculation date under different comparison dimensions. The target conversion data query statement is used to locate the second conversion data table, and the second conversion data table includes users in the first conversion data table that meet the characteristics indicated by the comparison dimension.

[0064] After executing step S200, the present application also needs to determine whether there is comparison dimension label data in the rule configuration data. If there is no comparison dimension label data, it means that the retention analysis event does not need to be displayed for dimension comparison. If there is comparison dimension label configuration data, it is also necessary to use the label cube model to group, assemble and transform the conversion data query statements according to the dimension labels indicated in the comparison dimension label data to split them into target retention data query statements under different comparison dimensions. The target retention data query statements actually further screen the users in the first conversion data table according to the comparison dimension to obtain the second conversion data table.

[0065] For example, if the comparison dimension label data includes two dimensions, male and female, then it is necessary to use the label cube model to assemble the conversion data query statement corresponding to each calculation date with the male and female dimensions respectively, and finally generate the target retention data query statements corresponding to the male and female dimensions respectively. For the target retention data query statement corresponding to the male dimension, it is used to locate the male users in the first user retention data table, and the third user retention data table is formed by the male users in the first user retention data table.

[0066] In this application, by providing comparison dimension label data and combining it with the label cube model for further analysis and assembly, multi-dimensional conversion analysis of the target customer group can be achieved. While solving the complexity of multi-dimensional comparison processing, a more comprehensive display of user conversion results can be obtained, which is more convenient for users to perform a series of subsequent operations.

[0067] In step S300, the user predefines different customer group rules, each customer group rule is used to locate a corresponding customer group, and the database is constantly updated, which means that even if the customer group resource package is uploaded, the customer group resource package is not real-time. If you only select the corresponding customer group resource package as the target customer group for analysis, the analysis result will not meet the current actual needs. Therefore, in this application, a mapping relationship between customer group labels and customer group rules is established in advance. After determining the customer group labels, the customer group rules used to locate the target customer group can be directly called, and then with the help of the label cube and arrayJoin function, the selected target customer group rules are further parsed and converted into SQL to generate the corresponding customer query statement, so that when processing funnel analysis events, the accurate target customer list can be located in real time, ensuring the real-time and effectiveness of subsequent conversion analysis results.

[0068] In a preferred embodiment, step S400 includes:

[0069] Using the label cube model, the target customer list and the target conversion data query statement corresponding to each calculation date in each comparison dimension are assembled and converted by SQL to generate the target conversion summary query statement corresponding to each comparison dimension, and the target conversion summary query statement corresponding to each comparison dimension is run to determine the third conversion data table under each comparison dimension. The third conversion data table includes the user intersection between the second conversion data table of the corresponding calculation time interval under the comparison dimension and the target customer list after merging. According to the third conversion data table under each comparison dimension, the conversion analysis results of the target customer group for funnel events under each comparison dimension are determined.

[0070] In the present application, under each comparison dimension, the label cube model first performs a union assembly on the target conversion data query statements corresponding to each calculation date in the comparison dimension. During the union assembly process, repeated user IDs are merged, that is, if a user completes funnel conversion multiple times within the calculation time interval, it is actually only recorded as one funnel conversion. The query statements to be processed formed after the union assembly are assembled here with the target customer list to finally form the target conversion summary query statement corresponding to the comparison dimension.

[0071] Run the target conversion summary query statement corresponding to each comparison dimension to generate a third conversion data table corresponding to each comparison dimension. According to the third conversion data table corresponding to each comparison dimension, determine the conversion analysis results of the target customer group users in different dimensions, so as to facilitate further analysis and processing based on the conversion analysis results of the target customer group users in different dimensions.

[0072] In a preferred embodiment, the conversion analysis result includes the overall conversion rate corresponding to the entire funnel event, and step S600 includes:

[0073] Using the label cube, the funnel initial event query statement is determined according to the funnel initial event analysis label and the calculation time interval. The funnel initial event query statement is used to determine the funnel initial event data table that records the users for whom the funnel initial event occurred within the calculation time interval, and the number of first users corresponding to the funnel initial event data table and the number of second users corresponding to the third conversion data table are counted respectively, and the ratio between the second number of users and the first number of users is determined as the overall conversion rate corresponding to the funnel event.

[0074] In the present application, under each comparison dimension, the present application reflects the overall conversion rate of the target customers for the funnel events under each comparison dimension through the ratio between the number of second users and the number of first users recorded in the third conversion data table of the comparison dimension.

[0075] In another preferred embodiment, the conversion analysis result further includes the number of conversions, the number of churns, and the funnel analysis event conversion rate corresponding to each funnel analysis event, wherein step S600 further includes:

[0076] For each funnel analysis event, the following processing is performed:

[0077] The first target query statement of the funnel analysis event under each calculation date is assembled and processed to generate a third target query statement. The third target query statement is used to locate the pending conversion number statistics table of the funnel analysis event in the calculation time interval. The pending conversion number statistics table records the number of conversions corresponding to the funnel analysis event in the calculation time interval. The third target query statement corresponding to the funnel analysis event is assembled with the target customer list to generate a fourth target query statement. The fourth target query statement is used to locate the target conversion number statistics table of the funnel analysis event in the calculation time interval. The target conversion number statistics table is the intersection between the pending conversion number statistics table and the target customer list. The number of users recorded in the target conversion number statistics table is determined as the number of conversions of the funnel analysis event.

[0078] For each funnel analysis event, the following processing is also performed:

[0079] The second target query statement of the funnel analysis event under each calculation date is assembled and processed to generate a fifth target query statement. The fifth target query statement is used to determine the pending churn statistics table of the funnel analysis event under the calculation time interval. The pending churn statistics table records the churn numbers corresponding to the funnel analysis event in the calculation time interval. For each funnel analysis event, the fifth target query statement corresponding to the funnel analysis event is assembled with the target customer list to generate a sixth target query statement. The sixth target query statement is used to locate the target churn statistics table of the funnel analysis event under the calculation time interval. The target churn statistics table is the intersection between the pending churn statistics table and the target customer list. The number of users recorded in the target churn statistics table is determined as the churn number of the funnel analysis event.

[0080] In this application, by counting the number of churn and conversions corresponding to each funnel analysis event, customers can be assisted in observing the specific conversion and churn situations of users corresponding to multiple funnel analysis events, making it easier for users to make more refined adjustments to the funnel events later.

[0081] The method provided in this application also includes:

[0082] The conversion analysis results of funnel events for the target customer group under each comparison dimension are displayed by region.

[0083] The conversion analysis results of the target customer groups for funnel events in different dimensions are displayed by region using echarts, which makes it easier to intuitively compare and analyze the conversion analysis results in different dimensions.

[0084] Based on the same application concept, the embodiment of the present application also provides a user conversion analysis device corresponding to the user conversion 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 user conversion 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.

[0085] See also Figure 2 , Figure 2 FIG. 1 shows a functional module diagram of a user conversion analysis device provided by an embodiment of the present application. Figure 2 As shown, the device comprises:

[0086] The rule configuration module 700 is used to determine the rule configuration data corresponding to the funnel analysis event through the funnel analysis interface, the rule configuration data includes customer group label data, funnel event label data, calculation time interval and conversion window period, the calculation time interval includes at least one continuous calculation date, and the conversion window period is the effective conversion time corresponding to each calculation date;

[0087] The first parsing module 710 is used to perform SQL parsing processing on the funnel event label data, the calculation time interval and the conversion window period by using the pre-created label cube model and the preset window sliding function, and generate a conversion data query statement corresponding to each calculation date. The conversion data query statement is used to determine the first conversion data table corresponding to the conversion window period under the calculation date. The first conversion data table is for conversion users who have completed a conversion event within the calculation date and its corresponding conversion window period.

[0088] The second parsing module 720 is used to parse the customer group label data using the label cube model, determine the customer group query rule indicated by the customer group label data, and generate a customer group query statement corresponding to the target customer group according to the customer group query rule, and the customer group query statement is used to determine the target customer list corresponding to the target customer group;

[0089] An assembly module 730 is used to perform SQL assembly conversion on the customer group query statement and the conversion data query statement corresponding to each calculation date by using a label cube model, and generate a conversion summary query statement corresponding to the calculation time interval;

[0090] The analysis module 740 is used to determine the conversion analysis results of the target customer group for the funnel event according to the conversion summary query statement.

[0091] Based on the same application idea, please refer to Figure 3 , Figure 3 The structure diagram of an electronic device provided by an embodiment of the present application is shown. The electronic device 800 includes: a processor 810, a memory 820 and a bus 830, the memory 820 stores machine-readable instructions executable by the processor 810, when the electronic device 800 is running, the processor 810 and the memory 820 communicate through the bus 830, and the machine-readable instructions are executed by the processor 810 when running, such as the steps of the user conversion analysis method provided in any of the above embodiments.

[0092] Based on the same application concept, an embodiment of the present application also 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 user conversion analysis method provided in the above embodiment are executed.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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 user conversion analysis method, characterized in that: The method comprises: Determine the rule configuration data corresponding to the funnel event through the funnel analysis interface, wherein the rule configuration data includes customer group label data, funnel event label data, calculation time interval and conversion window period, wherein the calculation time interval includes at least one continuous calculation date, and the conversion window period is the effective conversion time corresponding to each calculation date; Using a pre-created label cube model and a preset window sliding function, the funnel event label data, the calculation time interval and the conversion window period are subjected to SQL parsing processing to generate a conversion data query statement corresponding to each calculation date, wherein the conversion data query statement is used to determine a first conversion data table corresponding to the conversion window period under the calculation date, wherein the first conversion data table is for conversion users who have completed a conversion event within the calculation date and its corresponding conversion window period; Utilizing the label cube model to parse the customer group label data, determine the customer group query rule indicated by the customer group label data, and generate a customer group query statement corresponding to the target customer group according to the customer group query rule, wherein the customer group query statement is used to determine a target customer list corresponding to the target customer group; Using the label cube model, the customer group query statement and the conversion data query statement corresponding to each calculation date are SQL assembled and converted to generate a conversion summary query statement corresponding to the calculation time interval; According to the conversion summary query statement, the conversion analysis results of the target customer group for the funnel event are determined.

2. The method according to claim 1, characterized in that The funnel event tag data includes an event tag sequence formed by a plurality of funnel analysis event tags, each funnel analysis event tag indicates a corresponding funnel analysis event, and the funnel analysis event indicated by the funnel analysis event tag at the first position of the event tag sequence is defined as the funnel initial event. The conversion data query statement corresponding to each calculation date is created in the following way: Bringing the conversion window period, the calculation time interval, the triggering order of the funnel analysis event and the multiple funnel analysis event tags into the preset window sliding function, generating a first target query statement corresponding to each funnel analysis event, wherein the first target query statement is used to locate the conversion data table that successfully triggers the funnel analysis event in the conversion window period according to the triggering order; The first target query statement corresponding to the funnel end analysis event on the calculation date is determined as the conversion data query statement.

3. The method according to claim 2, characterized in that The method further comprises: The conversion window period, the calculation time interval, the triggering order of the funnel analysis event and the multiple funnel analysis event tags are brought into the preset window sliding function to generate a second target query statement corresponding to each funnel analysis event. The second target query statement is used to determine the churned user data table. The churned user data table is used to locate the churned users who did not complete the subsequent funnel analysis events according to the triggering order within the conversion window period.

4. The method according to claim 1, characterized in that: The method further comprises: Determining whether there is comparison dimension label data in the rule configuration data, the comparison dimension label data indicating an analysis dimension for performing conversion analysis on a target customer group; If there is comparison dimension label data in the rule configuration data, the label cube model is used to perform SQL assembly conversion on the conversion data query statement corresponding to each calculation date and different comparison dimensions respectively, and determine the target conversion data query statement for each calculation date under different comparison dimensions. The target conversion data query statement is used to locate the second conversion data table, and the second conversion data table includes users in the first conversion data table that meet the characteristics indicated by the comparison dimension.

5. The method according to claim 4, characterized in that The step of determining the conversion analysis result of the target customer group for the funnel event according to the conversion summary query statement includes: Using the label cube model, the target customer list and the target conversion data query statement corresponding to each calculation date in each comparison dimension are subjected to SQL assembly conversion to generate a target conversion summary query statement corresponding to each comparison dimension; Run the target conversion summary query statement corresponding to each comparison dimension to determine a third conversion data table under each comparison dimension, wherein the third conversion data table includes a user intersection between the second conversion data table corresponding to the calculation time interval under the comparison dimension and the target customer list after being merged; According to the third conversion data table under each comparison dimension, determine the conversion analysis results of the target audience for the funnel events under each comparison dimension.

6. The method according to claim 5, characterized in that The conversion analysis results include the overall conversion rate corresponding to the entire funnel event. The conversion analysis results of the target customer group for the funnel events in each comparison dimension are determined in the following ways: Using the label cube, according to the funnel initial event analysis label and the calculation time interval, a funnel initial event query statement is determined, wherein the funnel initial event query statement is used to determine the funnel initial event data table of the user who has recorded the funnel initial event occurring within the calculation time interval; Respectively counting the number of first users corresponding to the funnel initial event data table and the number of second users corresponding to the third conversion data table; A ratio between the second number of users and the first number of users is determined as an overall conversion rate corresponding to the funnel event.

7. The method according to claim 5, characterized in that The conversion analysis results also include the number of conversions and the number of churn corresponding to each funnel analysis event. The conversion analysis results of the target customer group for the funnel events in each comparison dimension are determined in the following ways: For each funnel analysis event, the following processing is performed: Assemble the first target query statement of the funnel analysis event on each calculation date to generate a third target query statement, wherein the third target query statement is used to locate a statistical table of pending conversions of the funnel analysis event in the calculation time interval, wherein the statistical table of pending conversions records the number of conversions corresponding to the funnel analysis event in the calculation time interval; Assembling the third target query statement corresponding to the funnel analysis event with the target customer list to generate a fourth target query statement, wherein the fourth target query statement is used to locate a target conversion number statistical table of the funnel analysis event in a calculation time interval, wherein the target conversion number statistical table is an intersection between the to-be-processed conversion number statistical table and the target customer list; Determine the number of users recorded in the target conversion number statistics table as the number of conversions of the funnel analysis event; Assemble the second target query statement of the funnel analysis event on each calculation date to generate a fifth target query statement, wherein the fifth target query statement is used to determine a statistical table of pending churn numbers for the funnel analysis event in the calculation time interval, wherein the statistical table of pending churn numbers records the number of churn numbers corresponding to the funnel analysis event in the calculation time interval; For each funnel analysis event, assemble the fifth target query statement corresponding to the funnel analysis event with the target customer list to generate a sixth target query statement, wherein the sixth target query statement is used to locate a target churn number statistical table in a calculation time interval of the funnel analysis event, and the target churn number statistical table is an intersection between the churn number statistical table to be processed and the target customer list; The number of users recorded in the target churn number statistics table is determined as the churn number of the funnel analysis event.

8. The method according to claim 5, characterized in that The method further comprises: The conversion analysis results of funnel events for the target customer group under each comparison dimension are displayed by region.

9. A user conversion analysis device, characterized in that: The device comprises: A rule configuration module, used to determine the rule configuration data corresponding to the funnel analysis event through the funnel analysis interface, wherein the rule configuration data includes customer group label data, funnel event label data, calculation time interval and conversion window period, wherein the calculation time interval includes at least one continuous calculation date, and the conversion window period is the effective conversion time corresponding to each calculation date; A first parsing module is used to perform SQL parsing processing on the funnel event label data, calculation time interval and conversion window period by using a pre-created label cube model and a preset window sliding function, and generate a conversion data query statement corresponding to each calculation date, wherein the conversion data query statement is used to determine a first conversion data table corresponding to the conversion window period under the calculation date, wherein the first conversion data table is for conversion users who have completed a conversion event within the calculation date and its corresponding conversion window period; A second parsing module is used to parse the customer group label data using the label cube model, determine the customer group query rule indicated by the customer group label data, and generate a customer group query statement corresponding to the target customer group according to the customer group query rule, wherein the customer group query statement is used to determine a target customer list corresponding to the target customer group; An assembly module, used to perform SQL assembly conversion on the customer group query statement and the conversion data query statement corresponding to each calculation date by using the label cube model, and generate a conversion summary query statement corresponding to the calculation time interval; The analysis module is used to determine the conversion analysis results of the target customer group for the funnel event according to the conversion summary query statement.

10. 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 user conversion analysis method as described in any one of claims 1 to 7.

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