Title: Knowledge discovery in a direct marketing case using least squares support vector machines
Authors: Viaene, Stijn ×
Baesens, Bart
Van Gestel, Tony
Suykens, Johan
Van den Poel, D
Vanthienen, Jan
De Moor, Bart
Dedene, Guido #
Issue Date: Sep-2001
Publisher: John wiley & sons inc
Series Title: International journal of intelligent systems vol:16 issue:9 pages:1023-1036
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 REM 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. (C) 2001 John Wiley & Sons, Inc.
ISSN: 0884-8173
Publication status: published
KU Leuven publication type: IT
Appears in Collections:ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
Research Center for Management Informatics (LIRIS), Leuven
Electrical Engineering - miscellaneous
× corresponding author
# (joint) last author

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