Title :
A Two Phase Clustering Method for Intelligent Customer Segmentation
Author :
Namvar, Morteza ; Gholamian, Mohammad R. ; KhakAbi, Sahand
Author_Institution :
Dept. of Ind. Eng., Iran Univ. of Sci. & Technol., Tehran, Iran
Abstract :
Customer Segmentation is an increasingly significant issue in today´s competitive commercial area. Many literatures 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 a special point of view, rather than from systematical method considering all stages of CRM. This paper, with the aid of data mining tools, constructs a new customer segmentation method based on RFM, demographic and LTV data. The new customer segmentation method consists of two phases. Firstly, with K-means clustering, customers are clustered into different segments regarding their RFM. Secondly, using demographic data, each cluster again is partitioned into new clusters. Finally, using LTV, a profile for customer is created. The method has been applied to a dataset from Iranian bank, which resulted in some useful management measures and suggestions.
Keywords :
commerce; customer relationship management; data mining; pattern clustering; Iranian bank; K-means clustering; LTV data; data mining technology; demographic; demographic data; intelligent customer segmentation; management measurement; two phase clustering method; Clustering methods; Competitive intelligence; Customer relationship management; Data mining; Demography; Image segmentation; Industrial engineering; Input variables; Intelligent systems; Profitability; Iran; clustering; customer relationship management; data mining; segmentation;
Conference_Titel :
Intelligent Systems, Modelling and Simulation (ISMS), 2010 International Conference on
Conference_Location :
Liverpool
Print_ISBN :
978-1-4244-5984-1
DOI :
10.1109/ISMS.2010.48
