Knowledge discovery in a direct marketing case using least squares support vector machines

✍ Scribed by S. Viaene; B. Baesens; T. Van Gestel; J. A. K. Suykens; D. Van den Poel; J. Vanthienen; B. De Moor; G. Dedene

• Publisher: John Wiley and Sons
• Year: 2001
• Tongue: English
• Weight: 111 KB
• Volume: 16
• Category: Article
• ISSN: 0884-8173
• DOI: 10.1002/int.1047

✦ Synopsis

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 Ž . use of a powerful and promising least squares support vector machine LS-SVM classifier formulation. This study extends beyond the standard recency frequency mone-Ž . Ž . tary RFM modeling semantics in two ways: 1 by including alternative operationaliza-Ž . Ž . tions of the RFM variables, and 2 by adding several other non-RFM predictors. Results indicate that elimination of redundantrirrelevant 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, customerrcompany interaction, measured using indicators of information requests and complaints, and merchandise returns provide additional predictive power to purchase incidence modeling for database marketing.