A novel utility-based model for identifying the customer value in online shopping

Abstract

This study focuses on identifying customer's utility in online shopping. The utility is defined as the level of customer satisfaction in the online shopping experience. Our study is consisted of three phases: In the first phase, customers are segmented by K-means algorithm according to some demographic and preference properties. The optimal number of cluster is determined by DUNN index. In the second phase, one-dimensional utility functions are identified according to each significant factor in online context. By testing the utility independency, the multiplicative utility functions are used for identifying the global customer's utility. In the third phase, a novel customer value model is developed with utility usage. The proposed RFMU model can identify customer value better than RFM model and can resolve the main drawback of RFM model in predicting the value of new customers. This model is tested in one electronic retailer that provides flower and plant products and services.