Optimized RFV analysis

Purpose – In the classic recency‐frequency‐monetary value (RFV or RFM) approach to market segmentation, customers are grouped together into an arbitrary number of segments according to data on their most recent day of purchase (R), the number of buying orders placed (F) and the total monetary value of their purchases (V). The purpose of this paper is to show how to select the order in which the RFV dimensions are applied to data and choose the number of segments and the time frame used in such a way as to maximize the results of direct marketing campaigns. Design/methodology/approach – A “genetically” optimized RFV model is built from data collected from a real world direct marketing campaign. The results produced when it is used are compared with the results yielded without the use of any forecasting method at all and with the support of a widely used basic RFV model. Findings – Not only does the new model provide better results, but it is also easy to build and allows for the introduction of new dimensions that may improve its performance even further. Practical implications – The new model improves the cost‐effectiveness of direct marketing campaigns by permitting more accurate identification of a company's most valuable customers and improving the quality of communication with its customers. It can thereby help them to become more competitive and profitable. This has clear implications for the gathering of marketing intelligence and planning of marketing strategies. Originality/value – Although genetic algorithms have been shown to be powerful tools for problem solving, their use in marketing has been little reported. This work is a step towards bridging that gap. The genetically optimized RFV model is a new contribution to direct and relationship marketing, generating a positive qualitative and quantitative impact on the way companies relate to their customers.