Analysis of Taobao User Behavior Based on Improved RFM Model and K-means Algorithm
In the era of big data,China's e-commerce is developing rapidly,with an increasing amount of user behavior data.Utilizing massive amounts of data to analyze user behavior and provide decision-making basis for precision marketing,thereby improving user loyalty,satisfaction,and activity,has become the focus of attention for e-commerce platforms.Based on the real data set of Taobao users,a user behavior analysis method based on the improved RFM model and K-means algorithm is proposed.In order to better describe the characteristics of user behavior,the"activity conversion rate"indicator is created for analysis.The experimental results show that this method can effectively divide user catego-ries,and the division results comply with the"Pareto principle",which can help e-commerce platforms complete accurate customer relationship management.
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