A method and device for funnel analysis of user behavior data

Abstract

The embodiment of the present invention provides a method and device for funnel analysis of user behavior data, wherein the method includes: for each user, obtain a one-dimensional array predefined according to the application scenario, and store the preset elements in the order of preset behaviors Timestamp of the behavior; get the timestamp of the first default behavior that appears for the first time in the behavior sequence and fill it in the first element; if the next behavior in the obtained behavior sequence is the first preset behavior, then Fill the timestamp of the behavior into the first element; if the behavior is the Nth preset behavior, the first timestamp in the obtained N‑1th element is the same as the timestamp of the Nth preset behavior in In the same time window, fill the first timestamp into the Nth element; stop filling the elements with timestamps in the current one-dimensional array, the number of elements that have been filled with timestamps will be used as the last The number of long execution behaviors is used to calculate the conversion rate from the N-1th preset behavior to the Nth preset behavior.

Description

technical field [0001] The present invention relates to the technical field of data analysis, in particular to a method and device for performing funnel analysis on user behavior data. Background technique [0002] At present, the funnel analysis method is usually used to analyze user behavior data to monitor whether users convert according to predefined behaviors, and to find optimized behaviors in each conversion process. Generally speaking, user behaviors are executed in accordance with the preset behavior sequence, for example: in order: the first behavior A, the second behavior B, the third behavior C, the fourth behavior D..., the fourth behavior M row is X, and M is greater than or equal to 5. For example, if figure 1 As shown, if the application scenario is that the user operates the shopping webpage, then A is to purchase products, B is to add to the shopping cart, C is to go to the shopping cart for settlement, D is to check the order information..., and X is to ...

AI Technical Summary

Problems solved by technology

That is to say, in the prior art, the method of determining the longest set of execution behaviors of each user by traversing all user behavior data multiple times has a high space complexity, which leads to the efficiency of data analysis in the prior art lower

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