Integrating AHP and Data Mining for Effective Retailer Segmentation Based on Retailer Lifetime Value

Data mining techniques have been used widely in the area of customer relationship management (CRM). In this study, we have applied data mining techniques to address a problem in business-to-business (B2B) setting. In a manufacturer-retailer-consumer chain, a manufacturer should improve its relationship with retailers to continue its business. Segmentation is a useful tool for identifying groups of similar retailers in order to improve retailer loyalty by developing and implementing segment-specific marketing strategies. In this study, we have proposed a methodology for retailer segmentation based on their LRFM variables (relation Length, Recency, Frequency and Monetary) and analytical hierarchy process (AHP). The proposed methodology has been implemented by using data of a firm from a hygienic industry in Iran. The empirical results indicated that there are six groups of retailers. After analyzing each segment according to LRFM values, we labeled each retailer group according to its performance. Furthermore, we provided some possible actions that can be taken in order to improve the relationship between the firm and retailers.