چکیده:
The purpose of this study is to segment customers by value and observe different various characteristics in different clusters. We propose a new segmentation method based on DM and most commonly used CRM models; RFM, and demographic variables. The method is based on two-phases clustering model by k-means and SOM techniques and has been implemented in chain stores B in Iran. The descriptive findings of the study were rated clusters and pattern types of these customers to identify the target customers’ positions. The existing customers were divided into 35 groups.
In this chain store, each customer has a transaction record that stored in the store’s database but for Demographic data they were asked telephonic. Beyond simply understanding customer value in each cluster, the chain store would gain the opportunities to establish better customer relationship management strategies, improve customer loyalty and revenue and find opportunities for up and cross selling.
خلاصه ماشینی:
"In this study we aim at combining these two input variables in an innovative approach for customer segmentation using the well-known DM clustering technique, K-means and self-organization map.
Finally, a new procedure, joining quantitative value of RFM attributes and K-means algorithm into rough set theory (RS theory), is proposed to extract meaning rules, The data of this case study is from the electronics industry in Chang Hua, contains 401 records of company transactions that have been carried out in 2006 [17].
Customer’s profile Demographic data Customer’s transaction WRFM K-Means clustering Segmentation of each cluster by SOM Two-phase Clustering Cluster’s ranking Determining the relative weights of RFM model by AHP Determining k- optimum by Davies- Bouldin Index 2.
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