combining data mining and group decision making in retailer segmentation based on lrfmp variables

data mining is a powerful tool for firms to extract knowledge from their customers’ transaction data. one of the useful applications of data mining is segmentation. segmentation is an effective tool for managers to make right marketing strategies for right customer segments. in this study we have segmented retailers of a hygienic manufacture. nowadays all manufactures do understand that for staying in the competitive market, they should set up an effective relationship with their retailers. we have proposed a lrfmp (relationship length, recency, frequency, monetary, and potential) model for retailer segmentation. ten retailer clusters have been obtained by applying k-means algorithm with k-optimum according davies-bouldin index on lrfmp variables. we have analyzed obtained clusters by weighted sum of lrfmp values, which the weight of each variable calculated by analytic hierarchy process (ahp) technique. in addition we have analyzed each cluster in order to formulate segment-specific marketing actions for retailers. the results of this research can help marketing managers to gain deep insights about retailers.