Title: Application of data mining techniques for customer lifetime value parameters: a review

Authors: Harsha Aeron, Ashwani Kumar, M. Janakiraman

Addresses: Indian Institute of Management, FPM-33, Prabandh Nagar, Lucknow-226013, India. ' Indian Institute of Management, Room No. 204, Prabandh Nagar, Lucknow-226013, India. ' Indian Institute of Management Calcutta, Joka, Diamond Harbour Road, Kolkota 700 104, India

Abstract: Computational and digital advancements with the advent of relationship marketing have changed the land signs of business. Digital revolution led to generation and collection of data in companies and extracting knowledge from this data through knowledge discovery in databases (KDD) process. KDD involves many steps, of which an important step is data mining. Data mining is a process of extracting patterns in data through statistical and other techniques and algorithms. In business, firms are shifting their marketing approach from mass marketing to relationship based marketing leading to an era of customer relationship management (CRM). CRM requires sustainable long term relationship with customers and allocation of resources to maintain these relationships. Customer lifetime value (CLV) is a metric to justify resource allocation by segregating customers on the basis of their contribution to the company. In this paper we review applications of statistical and data mining techniques for predicting CLV and its parameters. The applications of techniques such as logistic regression, decision trees, artificial neural networks, genetic algorithms, fuzzy logic and support vector machines are covered. In the end, a case study is presented to estimate few CLV parameters for a direct marketing company.

Keywords: data mining; customer relationship management; CRM; customer lifetime value; CLV prediction; decision trees; artificial neural networks; genetic algorithms; fuzzy logic; relationship marketing; ANNs; knowledge discovery databases; KDD; pattern extraction; statistical techniques; mass marketing; sustainable relationships; long term relationships; resource allocation; customer segregation; logistic regression; support vector machines; direct marketing; USA; United States; business information systems.

DOI: 10.1504/IJBIS.2010.035744

International Journal of Business Information Systems, 2010 Vol.6 No.4, pp.514 - 529

Published online: 03 Oct 2010 *

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