Fast application statistical method and system

Abstract

The invention discloses a fast application statistical method, which comprises the following steps: performing access mode configuring to enable a fast application to finish platform access by using aunique identifier of the fast application in a fast application statistical platform; carrying out data acquisition through a code burying point mode, obtaining the fast application information, carrying out acquisition statistics on current fast application user behaviors, and associating the obtained statistical data with the fast application information; identifying user attribute informationaccording to the local cache desensitization user identifier, and obtaining user state statistical data of the current fast application; and according to the user behavior statistical data and the user state statistical data, in combination with the information of the fast application, performing fast application comprehensive data statistics; according to the system and the method, comprehensivestatistical analysis of multivariate data such as entrance source analysis, manufacturer flow analysis and desktop con creation situation analysis can be carried out, the system and the method serve as a third statistical platform to help developers to acquire more intuitive, accurate and reliable data statistical analysis, and the developers can better operate and popularize products according todata analysis results.

Description

technical field [0001] The present invention relates to the field of quick application technology, in particular, to a quick application statistical method and system. Background technique [0002] With the rapid development of Quick Apps, Quick Apps have covered more than one billion devices, and their monthly active users have reached 200 million. Users have opened Quick Apps 2 billion times, and more than 20,000 developers have registered. In terms of statistical analysis of quick apps, in the existing technology, the current ten manufacturers of the alliance have their own statistical methods. Developers who want to use data to understand the user behavior of self-supporting quick apps will have the following problems: [0003] 1. Developers need to create accounts on the statistics platforms of different manufacturers and access the statistical interfaces of each manufacturer; [0004] 2. The dimensions and methods of statistical data of various manufacturers are not u...

AI Technical Summary

Problems solved by technology

[0003] 1. Developers need to create accounts on statistical platforms of different manufacturers and access the statistical interfaces of each manufacturer;

[0004] 2. The dimensions and methods of statistical data of various manufacturers are not uniform;

[0005] 3. The lack of statistical functions of various manufacturers cannot meet individual needs;

[0006] 4. It is impossible to compare and analyze intuitively through the data, and the maintainability is poor;

[0007] 5. Apps can perform user statistics based on installation and uninstallation; and quick apps do not need to be installed, so they want to count user retention rates. Developers usually create and delete icons to count, and the accuracy is not high

[0008] The above problems will not only increase the development cost, but also make it difficult to analyze various data, which will not be a good reference for product operation promotion and decision-making, resulting in the loss of developers

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