Integrating OWA and data mining for analyzing customers churn in E-commerce

Abstract

Customers are of great importance to E-commerce in intense competition. It is known that twenty percent customers produce eighty percent profiles. Thus, how to find these customers is very critical. Customer lifetime value (CLV) is presented to evaluate customers in terms of recency, frequency and monetary (RFM) variables. A novel model is proposed to analyze customers purchase data and RFM variables based on ordered weighting averaging (OWA) and K-Means cluster algorithm. OWA is employed to determine the weights of RFM variables in evaluating customer lifetime value or loyalty. K-Means algorithm is used to cluster customers according to RFM values. Churn customers could be found out by comparing RFM values of every cluster group with average RFM. Questionnaire is conducted to investigate which reasons cause customers dissatisfaction. Rank these reasons to help E-commerce improve services. The experimental results have demonstrated that the model is effective and reasonable.