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Wrapped input selection using multilayer perceptrons for repeat-purchase modeling in direct marketing

Wrapped input selection using multilayer perceptrons for repeat-purchase modeling in direct marketing
Wrapped input selection using multilayer perceptrons for repeat-purchase modeling in direct marketing
In this paper, we try to validate existing theory on and develop additional insight into repeat-purchase behavior in a direct marketing setting by means of an illuminating case study. The case involves the detection and qualification of the most relevant RFM (Recency, Frequency and Monetary) variables, using a neural network wrapper as our input pruning method. Results indicate that elimination of redundant and/or irrelevant inputs by means of the discussed input selection method allows us to significantly reduce model complexity without degrading the predictive generalization ability. It is precisely this issue that will enable us to infer some interesting marketing conclusions concerning the relative importance of the RFM predictor categories and their operationalizations. The empirical findings highlight the importance of a combined use of RFM variables in predicting repeat-purchase behavior. However, the study also reveals the dominant role of the frequency category. Results indicate that a model including only frequency variables still yields satisfactory classification accuracy compared to the optimally reduced model.
115-126
Viaene, Stijn
e4f8934b-ddb8-44da-b381-fd54bf99e274
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Van den Poel, Dirk
642866fa-0713-4137-8207-3a929aab98a8
Dedene, Guido
de15fcda-ec48-47e2-bf1e-e882ab48061c
Vanthienen, Jan
6f3d818f-0fce-46fa-966b-160e645caf6d
Viaene, Stijn
e4f8934b-ddb8-44da-b381-fd54bf99e274
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Van den Poel, Dirk
642866fa-0713-4137-8207-3a929aab98a8
Dedene, Guido
de15fcda-ec48-47e2-bf1e-e882ab48061c
Vanthienen, Jan
6f3d818f-0fce-46fa-966b-160e645caf6d

Viaene, Stijn, Baesens, Bart, Van den Poel, Dirk, Dedene, Guido and Vanthienen, Jan (2001) Wrapped input selection using multilayer perceptrons for repeat-purchase modeling in direct marketing. Intelligent Systems in Accounting, Finance and Management, 10 (2), 115-126. (doi:10.1002/isaf.195).

Record type: Article

Abstract

In this paper, we try to validate existing theory on and develop additional insight into repeat-purchase behavior in a direct marketing setting by means of an illuminating case study. The case involves the detection and qualification of the most relevant RFM (Recency, Frequency and Monetary) variables, using a neural network wrapper as our input pruning method. Results indicate that elimination of redundant and/or irrelevant inputs by means of the discussed input selection method allows us to significantly reduce model complexity without degrading the predictive generalization ability. It is precisely this issue that will enable us to infer some interesting marketing conclusions concerning the relative importance of the RFM predictor categories and their operationalizations. The empirical findings highlight the importance of a combined use of RFM variables in predicting repeat-purchase behavior. However, the study also reveals the dominant role of the frequency category. Results indicate that a model including only frequency variables still yields satisfactory classification accuracy compared to the optimally reduced model.

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More information

Published date: 2001

Identifiers

Local EPrints ID: 36739
URI: http://eprints.soton.ac.uk/id/eprint/36739
PURE UUID: 524d1a39-d0b5-4ba4-aa2c-df60043c4296
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

Catalogue record

Date deposited: 26 May 2006
Last modified: 16 Mar 2024 03:39

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Contributors

Author: Stijn Viaene
Author: Bart Baesens ORCID iD
Author: Dirk Van den Poel
Author: Guido Dedene
Author: Jan Vanthienen

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