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Bayesian neural network learning for repeat purchase modelling in direct marketing

Bayesian neural network learning for repeat purchase modelling in direct marketing
Bayesian neural network learning for repeat purchase modelling in direct marketing
We focus on purchase incidence modelling for a European direct mail company. Response models based on statistical and neural network techniques are contrasted. The evidence framework of MacKay is used as an example implementation of Bayesian neural network learning, a method that is fairly robust with respect to problems typically encountered when implementing neural networks. The automatic relevance determination (ARD) method, an integrated feature of this framework, allows us to assess the relative importance of the inputs. The basic response models use operationalisations of the traditionally discussed Recency, Frequency and Monetary (RFM) predictor categories. In a second experiment, the RFM response framework is enriched by the inclusion of other (non-RFM) customer profiling predictors. We contribute to the literature by providing experimental evidence that: (1) Bayesian neural networks offer a viable alternative for purchase incidence modelling; (2) a combined use of all three RFM predictor categories is advocated by the ARD method; (3) the inclusion of non-RFM variables allows to significantly augment the predictive power of the constructed RFM classifiers; (4) this rise is mainly attributed to the inclusion of customer/company interaction variables and a variable measuring whether a customer uses the credit facilities of the direct mailing company.
neural networks, marketing, bayesian learning, response modelling, input ranking
0377-2217
191-211
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Viaene, Stijn
e4f8934b-ddb8-44da-b381-fd54bf99e274
Van den Poel, Dirk
642866fa-0713-4137-8207-3a929aab98a8
Vanthienen, Jan
6f3d818f-0fce-46fa-966b-160e645caf6d
Dedene, Guido
de15fcda-ec48-47e2-bf1e-e882ab48061c
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Viaene, Stijn
e4f8934b-ddb8-44da-b381-fd54bf99e274
Van den Poel, Dirk
642866fa-0713-4137-8207-3a929aab98a8
Vanthienen, Jan
6f3d818f-0fce-46fa-966b-160e645caf6d
Dedene, Guido
de15fcda-ec48-47e2-bf1e-e882ab48061c

Baesens, Bart, Viaene, Stijn, Van den Poel, Dirk, Vanthienen, Jan and Dedene, Guido (2002) Bayesian neural network learning for repeat purchase modelling in direct marketing. European Journal of Operational Research, 138 (1), 191-211. (doi:10.1016/S0377-2217(01)00129-1).

Record type: Article

Abstract

We focus on purchase incidence modelling for a European direct mail company. Response models based on statistical and neural network techniques are contrasted. The evidence framework of MacKay is used as an example implementation of Bayesian neural network learning, a method that is fairly robust with respect to problems typically encountered when implementing neural networks. The automatic relevance determination (ARD) method, an integrated feature of this framework, allows us to assess the relative importance of the inputs. The basic response models use operationalisations of the traditionally discussed Recency, Frequency and Monetary (RFM) predictor categories. In a second experiment, the RFM response framework is enriched by the inclusion of other (non-RFM) customer profiling predictors. We contribute to the literature by providing experimental evidence that: (1) Bayesian neural networks offer a viable alternative for purchase incidence modelling; (2) a combined use of all three RFM predictor categories is advocated by the ARD method; (3) the inclusion of non-RFM variables allows to significantly augment the predictive power of the constructed RFM classifiers; (4) this rise is mainly attributed to the inclusion of customer/company interaction variables and a variable measuring whether a customer uses the credit facilities of the direct mailing company.

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More information

Published date: 2002
Keywords: neural networks, marketing, bayesian learning, response modelling, input ranking

Identifiers

Local EPrints ID: 36517
URI: http://eprints.soton.ac.uk/id/eprint/36517
ISSN: 0377-2217
PURE UUID: ee22aabd-c9c5-48cb-b9db-d84f18693d53
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

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Date deposited: 23 May 2006
Last modified: 17 Dec 2019 01:47

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Contributors

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

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