User Value Identification Based on Improved RFM Model and K‐Means++ Algorithm for Complex Data Analysis

Abstract

In recent years, with the development of machine learning and big data technology, user data has become an important element in the production process of enterprises. For today’s e‐commerce platforms, the deep mining of user’s purchase behavior is helpful to understand user’s purchase preferences and accurately recommend products that meet user expectations, which can not only improve user satisfaction but also reduce platform marketing cost. To accurately identify the user value of online purchasing on an e‐commerce platform, this paper uses an improved RFM model to extract user features and uses the K‐means++ clustering algorithm to realize user classification. The indicators of the traditional RFM model characterize user features from three angles: recent purchase time (R), purchase frequency (F), and total consumption amount (M). The user group and scenarios studied in this paper are different from the previous literature: (1) the user group is relatively fixed, (2) the consumer goods are relatively single, and (3) the characteristics of repeated purchase are obvious. Therefore, based on the existing literature, this paper extracts the user characteristics studied and improves and models the traditional indicators. Based on the real purchasing data from September to December 2018, it calculates the indicators that improved RFM, empowers the weight to indicators, and finally classifies the value of users by using the K‐means++ algorithm. The experimental results show that the user classification based on the improved RFM model is more accurate than the user classification based on the traditional RFM model, and the improved RFM model can identify the user value more accurately, which provides a strong support for the e‐commerce platform to realize the accurate marketing strategy based on big data.