Tech Report

Profiling your Customers using Bayesian Networks

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.


Also in ACM SIGKDD Explorations, 1(2), 2000

ID: kmi-00-06

Date: 2000

Author(s): Paola Sebastiani, Marco Ramoni and Alexander Crea

Download PDF

View By

Other Publications

Latest Seminar
Alessia Pisu
University of Cagliari

AI-Driven Ontology extraction of Research Areas

Watch the live webcast


Knowledge Media Institute
The Open University
Walton Hall
Milton Keynes
United Kingdom

Tel: +44 (0)1908 653800

Fax: +44 (0)1908 653169

Email: KMi Support


If you have any comments, suggestions or general feedback regarding our website, please email us at the address below.

Email: KMi Development Team