Title: An approach to optimised customer segmentation and profiling using RFM, LTV, and demographic features
Authors: Morteza Namvar; Sahand Khakabimamaghani; Mohammad Reza Gholamian
Addresses: No. 23, Syasat 11 Alley, Modaress Street, Kashan, 87196-87384, Iran. ' 3rd Floor, No.86, Shahid Beheshti Blvd., 1st Phase, 13888-17165, Western Tehransar, Tehransar, Tehran, Iran. ' School of Industrial Engineering, Iran University of Science and Technology (IUST), Narmak, 16844, Tehran, Iran
Abstract: Customer segmentation and profiling are increasingly significant issues in today's competitive commercial area. Many studies have reviewed the application of data mining technology in customer segmentation, and achieved sound effectives. But in the most cases, it is performed using customer data from especial point of view, rather than from systematical method considering all stages of CRM. This paper constructs a new customer segmentation method based on RFM, LTV, and demographic parameters with the aid of data mining tools. In this method, first different combinations of RFM and demographic variables are used for clustering. Second, using LTV, the best clustering is chosen. Finally, to build customer profiles each segment is compared to other segments regarding different features. The method has been applied to a dataset from a food chain stores and resulted in some useful management measures and suggestions.
Keywords: customer relationship management; CRM; optimisation; customer segmentation; data mining; clustering; customer profiling; demographics; food chain stores; recency; frequency; monetary value; lifetime value.
International Journal of Electronic Customer Relationship Management, 2011 Vol.5 No.3/4, pp.220 - 235
Available online: 31 Dec 2011 *Full-text access for editors Access for subscribers Purchase this article Comment on this article