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Abstract
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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.
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