A method for customer lifetime value ranking — Combining the analytic hierarchy process and clustering analysis

Abstract

Creating successful transaction actions to retain customers for future repurchasing is extremely important in today's fiercely competitive environments. The best marketing strategy is to obtain customers who are profitable and remain loyal for a lifetime. However, although the recency, frequency and monetary (RFM) technique has been used to predict customer behaviour for over 50 years in direct marketing, few studies have discussed the relative importance of RFM variables via a systematic approach. Therefore, this study applies the analytic hierarchy process (AHP) to determine the relative importance of RFM variables (weighted RFM) in evaluating customer lifetime value (CLV). Clustering techniques are then employed to cluster customers according to weighted RFM value. The simple weighted sum approach is then used to derive CLV ranking and thus customer segments can be identified and compared clearly. The usefulness of the approach is verified by applying it to a hardware retailer, and a useful marketing database is derived to evaluate the proposed method. Finally, this study also discusses three perspectives for validating the proposed method. This study provides a useful method of CLV ranking and can thus assist market practitioners in performing more effective market segmentation.