Data Mining Techniques to Enhance Customer Lifetime Value

Chia Chia Lin; Dong Her Shih

doi:10.4028/www.scientific.net/AMR.225-226.3

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Data Mining Techniques to Enhance Customer Lifetime Value
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 225-226Data Mining Techniques to Enhance Customer...

Data Mining Techniques to Enhance Customer Lifetime Value

Abstract:

It is proved by many studies that it is more costly to acquire than to retain customers. Consequently, evaluating current customers to keep high value customers and enhance their lifetime value becomes a critical factor to decide the success or failure of a business. This study applies data from customer and transaction databases of a department store, based on RFM model to do clustering analysis to recognize high value customer groups for cross-selling promotions.

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Periodical:

Advanced Materials Research (Volumes 225-226)

Pages:

3-7

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.225-226.3

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Online since:

April 2011

Authors:

Chia Chia Lin, Dong Her Shih

Keywords:

Customer Lifetime Vale, Data Mining (DM), Target Marketing

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References

[1] I. Bose and R. K. Mahapatra, Business data mining-a machine learning perspective, Information and Management, Vol 39, pp.211-225, (2001).

DOI: 10.1016/s0378-7206(01)00091-x

Google Scholar

[2] R. J. Brachman, T. Khabaza, W. Kloesgen, G. Piatetsky-Shapiro, and E. Simoudis, Mining business databases, Communications of the ACM, Vol 39, pp.42-48, (1996).

DOI: 10.1145/240455.240468

Google Scholar

[3] J. R. Bult and T. Wansbeek, Optimal selection for direct mail, Marketing Science, Vol 14, pp.378-395, (1995).

DOI: 10.1287/mksc.14.4.378

Google Scholar

[4] M. S. Chen, J. Han, and P. S. Yu, Data mining: an overview from a database perspective, IEEE Transactions on Knowledge and Data Engineering, Vol 8, pp.866-883, (1996).

DOI: 10.1109/69.553155

Google Scholar

[5] J. Goodman, Leveraging the customer database to your competitive advantage, Direct Marketing, Vol 55, pp.26-27, (1992).

Google Scholar

[6] F. H. Grupe and M. M. Owrang, Data Base Mining, Information System Management, Vol 12, pp.25-31, (1995).

Google Scholar

[7] A. M. Hughes, Strategic Database Marketing, Probus Publishing, Chicago, (1994).

Google Scholar

[8] R. Kahan, Using Database Marketing Techniques to Enhance Your One-to-One Marketing Initiatives, Journal of Consumer Marketing, Vol 15, pp.491-493, (1998).

DOI: 10.1108/07363769810235965

Google Scholar

[9] P. Kotler, Marketing Management - analysis, planning, implementation, Prentice-Hall, New York, (1997).

Google Scholar

[10] D. R. Liu and Y. Y. Shih, Integrating AHP and data mining for product recommendation based on customer lifetime value, Information & Management, Vol 42, pp.387-400, (2005).

DOI: 10.1016/j.im.2004.01.008

Google Scholar

[11] T. L. Satty, The Analytical Hierarchy Process: Planning, Priority Setting, Resource Allocation, McGraw-Hill, New York, (1980).

Google Scholar

[12] M. J. Shaw, C. Subramaniam, G. W. Tan, and M. E. Welge, Knowledge management and data mining for marketing, Decision Support Systems, Vol 31, pp.127-137, (2001).

DOI: 10.1016/s0167-9236(00)00123-8

Google Scholar

[13] R. W. Stone and D. J. Good, The assimilation of computer-aided marketing activities, Information and Management, Vol 38, pp.437-447, (2001).

DOI: 10.1016/s0378-7206(00)00080-x

Google Scholar

[14] W. Z. Zikmund, R. McLeod, and F. W. Gilbert, Customer Relationship Management - Integrating Marketing Strategy and Information Technology, Wiley, New Jersey, (2003).

Google Scholar