An User Behavior Analysis Model Based on AARRR Model and RMF Model

After more than ten years of rapid development, the e-commerce industry led by Taobao has attracted and obtained a large number of users. In the era of mobile Internet, the needs of users determine the future development direction of the e-commerce industry. In this context, it is of great significance for the future development of e-commerce to obtain and analyze user behavior, understand user needs, and guide product marketing and design. This paper selected the user behavior data of Taobao app from November 18,2014 to December 18,2014, and analyzed the user behavior data using AARRR model and RMF model. The analysis results show that the activity of Taobao users is high after 0:00 and 18:00 on the 12th day of the Double 12 shopping festival, and fell to the lowest point at 6:00; The loss of users mainly occurs in the process from clicking to adding to favorites, and the proportion of critical retention customers is the largest.