Profiling your customers using Bayesian networks

Authors:
Paola Sebastiani;Marco Ramoni;Alexander Crea
Affiliations:
Imperial College, London, United Kingdom;The Open University, Milton Keynes, United Kingdom;The Open University, Milton Keynes, United Kingdom
Venue:
ACM SIGKDD Explorations Newsletter
Year:
2000

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Abstract

This report describes a complete Knowledge Discovery session using Bayesware Discoverer, a program for the induction of Bayesian networks from incomplete data. We build two causal models to help an American Charitable Organization understand the characteristics of respondents to direct mail fund raising campaigns. The first model is a Bayesian network induced from the database of 96,376 Lapsed donors to the June '97 renewal mailing. The network describes the dependency of the probability of response to the renewal mail on a subset of the variables in the database. The second model is a Bayesian network representing the dependency of the dollar amount of the gift on the variables in the same reduced database. This model is induced from the 5% of cases in the database corresponding to the respondents to the renewal campaign. The two models are used for both predicting the expected gift of a donor and understanding the characteristics of donors. These two uses can help the charitable organization to maximize the profit.