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A study on the applications of data mining techniques to enhance customer lifetime value — based on the department store industry

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2 Author(s)
Huan-Ming Chuang ; Dept. of Inf. Manage., Nat. Yunlin Univ. of Sci. & Technol., Yunlin ; Chia-Cheng Shen

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. Study findings show that clustering analysis can locate high value customers, and appropriate target marketing can enhance their lifetime value effectively. The implication for marketer is that leveraging techniques of data mining can make the most from data of customers and transactions databases, to create sustainable competitive advantages.

Published in:

Machine Learning and Cybernetics, 2008 International Conference on  (Volume:1 )

Date of Conference:

12-15 July 2008