Customer segmentation in Fast Moving Consumer Goods (FMCG) Industries by using developed RFM model

Abstract

Customers segmentation and analyzing their behavior at fast moving costumer goods (FMGS) industries which deal with a large number of customers with a variety of characteristics causes the marketing activities to be targeted and leads to effective communication with the customers. Segmentation, a data mining approach, which leads to the discovery of similar groups of customers, is usually done by recency, frequency and purchased volume variables in RFM model. Using proper segmentation variables affects the quality of segmentation. Analyzing the quality of Golsetan customer segments, the biggest FMCG industry in Iran, confirms the hypothesis which the recency variable is not effective in customer segmentation in FMCG industries. In this paper, purchase sequence (continuity) variable is defined as a new customer performance variable in FMCG industries. By replacing the continuity variable (C) with recency in RFM model, the quality of segmentation has been improved. Customers of Golestan Company were segmented by two RFM and proposed (CFM) models. The Davis-Bouldin criterion reduced more than 11 percent and the forecast accuracy for customers cluster in artificial neural networks increased about 1 percent.

Keywords

• customer segmentation
• data mining
• Fast-Moving Consumer Goods Industry
• RFM Model