Recently organizationâs interaction with customers have been changed. Therefore organizations must get to know their customers and predict their needs to improve their Strategies and selling and marketing plans. Clustering is a way to get to know the customers and identify each cluster's features.
The purpose of this research is to identify an alimentary industry's (Kadbano Company) customers and classify them into different clusters. By clustering and labeling customers, different rebate strategies can be deduced.
In order to create an appropriate customer clustering, RFM and LRFM models were used, K-means algorithm, created the optimum number of clusters and the outcomes were compared by Dun and SSE indexes. The results of this research illustrated that the best number of clusters for segmenting Kadbano Co.âs customers was obtained from RFM model. Eight clusters were analysed and labled Finally variant rebating strategies for each cluster were extracted.
Hamedi, P., Khadivar, A., & Razmi, Z. (2013). Customer clustering for appointing rebating strategies, case study: Kadbano Co.. New Marketing Research Journal, 3(3), 135-150.
MLA
Proshat Hamedi; Ameneh Khadivar; Zahra Razmi. "Customer clustering for appointing rebating strategies, case study: Kadbano Co.". New Marketing Research Journal, 3, 3, 2013, 135-150.
HARVARD
Hamedi, P., Khadivar, A., Razmi, Z. (2013). 'Customer clustering for appointing rebating strategies, case study: Kadbano Co.', New Marketing Research Journal, 3(3), pp. 135-150.
VANCOUVER
Hamedi, P., Khadivar, A., Razmi, Z. Customer clustering for appointing rebating strategies, case study: Kadbano Co.. New Marketing Research Journal, 2013; 3(3): 135-150.
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