Issues and problems in applying neural computing to target marketing

Jacob Zahavi · Nissan Levin

80 citations0/0 files storedJournal of Direct Marketing

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Customer churn and segmentation Neural Networks and Applications Forecasting Techniques and Applications

Abstract

Applying a neural network (NN) to the targeting and prediction problems in target marketing poses some unique problems and difficulties unparalleled in other business applications of neural computations. We discuss several of these issues in this article, as applied to solo mailings, offer remedies to some, and discuss possible solutions to others. A numerical example, using NN backpropagation models and involving realistic data, is used to exemplify some of the resulting issues.

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References

1986The theory and practice of econometrics 1992Managerial Applications of Neural Networks: The Case of Bank Failure Predictions 1994Bankruptcy prediction using neural networks 2014Discrete Choice Analysis 1988Bond rating: A non-conservative application of neural networks 1989Direct Marketing Management 1997Applying neural computing to target marketing

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2001Knowledge discovery in a direct marketing case using least squares support vector machines 1999Customer list segmentation using the combined response model 2005Constrained optimization of data-mining problems to improve model performance: A direct-marketing application 2004Optimizing Rhenania´s Direct Marketing Business through Dynamic Multi-Level Modeling (DMLM) in a Multi-Catalog-Brand Environment 2005Shelf space assigned to store and national brands 2009Market orientation and performance: modelling a neural network 2001Wrapped input selection using multilayer perceptrons for repeat‐purchase modeling in direct marketing

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