Applying neural computing to target marketing

Abstract

Database marketing uses the power of data and information technology in the pursuit of personal marketing of products and services to consumers, based on their preferences and needs. We explore the feasibility of using neural computing as a means for targeting audiences for promotion through the mail, from among a list of customers in a database, either as an alternative and/or as a supplement to discrete-choice logistic regression models. Detailed numerical examples involving realistic data are used throughout to support the analysis and demonstrate the results. It is shown that, at least for the data used in this study the fit achieved for both methods is approximately the same, but the process of configuring and setting up a neural network for a database marketing application is not straightforward and may require extensive experimentation and computer resources. The results are therefore not encouraging for the neural net approach.

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