An approach to optimised customer segmentation and profiling using RFM, LTV, and demographic features. (1st January 2011)

• Record Type: Journal Article
• Authors: Namvar, Morteza; Khakabimamaghani, Sahand; Gholamian, Mohammad Reza
• Is Part Of: International journal of electronic customer relationship management. Volume 5:Number 3/4(2011)
• Page Start: 220
• Page End: 235
• Publication Date: 2011-01-01
• Subjects: customer relationship management -- segmentation -- data mining -- clustering -- profiling; Customer relations -- Management -- Data processing -- Periodicals; Electronic commerce -- Periodicals; 658.8120285
• Languages: English
• ISSNs: 1750-0664

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.