Method, system, device and terminal for analyzing terminal application operation data

Abstract

The present invention discloses a terminal application operation data analysis method, system, device and terminal, wherein the method includes: obtaining a buried point toolkit of a terminal application from a server and integrating it into the terminal; using the embedded Different types of tracking tools in the point toolkit collect user operation data each time the terminal application is running; analyze and process the user operation data according to the set expected goals and analysis rules to obtain the user individual data; the terminal will The user individual data is reported to the server. The number of buried points and the size of collected user operation data are not limited in the present invention, and refined user complete operation behavior track data, blood relationship of data and user personalized difference data can be obtained.

Description

technical field [0001] The present invention relates to the field of information technology, in particular to an analysis method, system, device and terminal for terminal application operation data. Background technique [0002] One of the indispensable functions of some App (application) products developed by most enterprises for users is the user operation tracking function. Improve product design by collecting, counting, and analyzing information such as the number of user visits, operating habits, and usage rates through user operation burying points. [0003] At present, the commonly used terminal App tracking solution is to integrate the tracking toolkit (SDK) into the App product. When using the App product, call the tracking tool in the SDK in each page operation, and each tracking tool Point tools provide methods for collecting data. When the user operates the App, the terminal invokes the corresponding tracking tool, collects the corresponding user operation data...

AI Technical Summary

Problems solved by technology

The current burying method only counts large-scale data such as access frequency, online duration, and traffic trends. It cannot take into account all operations of users when using the app, and lacks data statistics on subtle functions and branch functions.

For example: only the main function points of the page can be buried, and all the operation data of the user on this page cannot be obtained

[0008] 2. Only user group data, lack of user personalized difference data

[0009] Since all users’ app tracking points are consistent, the current tracking method can only collect user group data such as the number of clicks and online time, lacks user personalized difference data, and cannot reflect the user’s individual behavior trajectory

[0010] 3. Only users use data, lack of blood relationship

[0011] The buried point data obtained by the current solution can only reflect how many times a function is triggered, but cannot reflect the scenarios in which the user triggered this function

User operation data cannot describe the behavior trajectory of individual users, lacking blood relationship between data

For example: when the server pushes a message notification to the terminal App, how long it will cause the user's response, what operations the user will perform in the future, and which functions will be triggered in the end cannot be known.

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