A Big Data Based Dynamic Weight Approach for RFM Segmentation

Using the RFM (Recency, Frequency, Monetary value) model can provide valuable insights about customer clusters which is core of relationship management. Due to accurate segment coming from dynamic weighted applications, in-depth targeted marketing may also use type weight R, F and M as factors. In this paper, we present our approach intended improve performance segmentation by using factors variations attain weights. Our a kind Custom method in essential roots understanding data set. Firstly, Analytic Hierarchy Process used calculate subjective weight, then entropy applied objective weight. Finally, comprehensive integration weighting combine obtain final index individual user value quantify difference. The experiment shows that vital, affects positively. Also, study indicates segments containing scores bring stronger more association rules for behavior. At last, discuss limitations analysis.