Knowledge discovery in a direct marketing case using least squares support vector machines
Viaene, S Baesens, B Van Gestel, T Suykens, JAK Van den Poel, D Vanthienen, J De Moor, B Dedene, G
Viaene, S
Baesens, B
Van Gestel, T
Suykens, JAK
Van den Poel, D
Vanthienen, J
De Moor, B
Dedene, G
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Journal article with impact factor
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Supervisor
Publication Year
2001-09
Journal
International Journal of Intelligent Systems
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Publication Volume
16
Publication Issue
9
Publication Begin page
1023
Publication End page
1036
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Abstract
We study the problem of repeat‐purchase modeling in a direct marketing setting using Belgian data. More specifically, we investigate the detection and qualification of the most relevant explanatory variables for predicting purchase incidence. The analysis is based on a wrapped form of input selection using a sensitivity based pruning heuristic to guide a greedy, stepwise, and backward traversal of the input space. For this purpose, we make use of a powerful and promising least squares support vector machine (LS‐SVM) classifier formulation. This study extends beyond the standard recency frequency monetary (RFM) modeling semantics in two ways: (1) by including alternative operationalizations of the RFM variables, and (2) by adding several other (non‐RFM) predictors. Results indicate that elimination of redundant/irrelevant inputs allows significant reduction of model complexity. The empirical findings also highlight the importance of frequency and monetary variables, while the recency variable category seems to be of somewhat lesser importance to the case at hand. Results also point to the added value of including non‐RFM variables for improving customer profiling. More specifically, customer/company interaction, measured using indicators of information requests and complaints, and merchandise returns provide additional predictive power to purchase incidence modeling for database marketing. © 2001 John Wiley & Sons, Inc.
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Keywords
46 Information and Computing Sciences