Combining Data Mining and Group Decision Makingin Retailer Segmentation Based on LRFMP Variables

Data mining is a powerful tool for firms to extract knowledge fromtheir customers’ transaction data. One of the useful applications ofdata mining is segmentation. Segmentation is an effective tool formanagers to make right marketing strategies for right customersegments. In this study we have segmented retailers of a hygienicmanufacture. Nowadays all manufactures do understand that forstaying in the competitive market, they should set up an effectiverelationship with their retailers. We have proposed a LRFMP(relationship Length, Recency, Frequency, Monetary, and Potential)model for retailer segmentation. Ten retailer clusters have beenobtained by applying K-means algorithm with K-optimum accordingDavies-Bouldin index on LRFMP variables. We have analyzedobtained clusters by weighted sum of LRFMP values, which theweight of each variable calculated by Analytic Hierarchy Process(AHP) technique. In addition we have analyzed each cluster in orderto formulate segment-specific marketing actions for retailers. Theresults of this research can help marketing managers to gain deepinsights about retailers.

کلیدواژه ها:

Market segmentation ، Customer Lifetime Value (CLV) ، LRFMP model ، Analytic Hierarchy Process (AHP) ، Clustering ، Cluster analysis