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A hybrid model of data mining and MCDM methods for estimating customer lifetime value
Date Issued
2011-12-01
Date Available
2019-04-29T07:30:10Z
Abstract
Due to competitive environment, companies want to create a long-term relationship with their customers throughout customer relationship management (CRM). Building effective customer relationship management, companies should estimate customer lifetime value (CLV). CLV is normally calculated in terms of recency, frequency and monetary (RFM) variables. In this paper, a model for estimating CLV based on RFM variables integrated with data mining and multi criteria decision making (MCDM) methods has been proposed. The proposed methodology contains three phases in which Fuzzy Analytical Hierarchy Process (FAHP) has been used to determine RFM variables' weights. Then, Kmeans clustering method was employed in order to customer clustering and segmentation. Customer clusters were then ranked using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Finally, the proficiency of the model was shown by conducting a case study of cosmetics industry.
Type of Material
Conference Publication
Start Page
44
End Page
49
Language
English
Status of Item
Not peer reviewed
Part of
41st International Conference on Computers and Industrial Engineering 2011
Conference Details
The 41st International Conference on Computers and Industrial Engineering (CIE41), Los Angeles, United States of America, 23-26 2011
ISBN
9781627486835
This item is made available under a Creative Commons License
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