A hybrid model of data mining and MCDM methods for estimating customer lifetime value

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Title: A hybrid model of data mining and MCDM methods for estimating customer lifetime value
Authors: Azadnia, Amir Hossein
Ghadimi, Pezhman
Molani-Aghdam, Mohammad
Permanent link: http://hdl.handle.net/10197/10167
Date: 1-Dec-2011
Online since: 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
Keywords: Customer Lifetime ValueData miningMulti Criteria Decision Making
Other versions: https://www.computers-and-ie.org/conferences/2017/1/15/41st-international-conference-1
Language: en
Status of Item: Not peer reviewed
Is 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
Appears in Collections:Mechanical & Materials Engineering Research Collection

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