Amalgamation of Statistics and Data Mining Techniques: Explorations in Customer Lifetime Value Modeling

2005 ◽  
pp. 229-250
Author(s):  
D. R. Mani ◽  
James Drew ◽  
Andrew Betz ◽  
Piew Datta
Keyword(s):  
Data Mining ◽  
Customer Lifetime Value ◽  
Data Mining Techniques ◽  
Customer Lifetime ◽  
Lifetime Value ◽  
Value Modeling

Data Mining Techniques to Enhance Customer Lifetime Value

2011 ◽  
Vol 225-226 ◽  
pp. 3-7
Author(s):  
Chia Chia Lin ◽  
Dong Her Shih
Keyword(s):  
Data Mining ◽  
Clustering Analysis ◽  
Critical Factor ◽  
Department Store ◽  
Customer Lifetime Value ◽  
Data Mining Techniques ◽  
Customer Lifetime ◽  
Rfm Model ◽  
Lifetime Value

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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