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Published in: Journal of Intelligent Information Systems 3/2011

01-06-2011

Data augmentation by predicting spending pleasure using commercially available external data

Authors: Philippe Baecke, Dirk Van den Poel

Published in: Journal of Intelligent Information Systems | Issue 3/2011

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Abstract

Since customer relationship management (CRM) plays an increasingly important role in a company’s marketing strategy, the database of the company can be considered as a valuable asset to compete with others. Consequently, companies constantly try to augment their database through data collection themselves, as well as through the acquisition of commercially available external data. Until now, little research has been done on the usefulness of these commercially available external databases for CRM. This study will present a methodology for such external data vendors based on random forests predictive modeling techniques to create commercial variables that solve the shortcomings of a classic transactional database. Eventually, we predicted spending pleasure variables, a composite measure of purchasing behavior and attitude, in 26 product categories for more than 3 million respondents. Enhancing a company’s transactional database with these variables can significantly improve the predictive performance of existing CRM models. This has been demonstrated in a case study with a magazine publisher for which prospects needed to be identified for new customer acquisition.

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Appendix
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Metadata
Title
Data augmentation by predicting spending pleasure using commercially available external data
Authors
Philippe Baecke
Dirk Van den Poel
Publication date
01-06-2011
Publisher
Springer US
Published in
Journal of Intelligent Information Systems / Issue 3/2011
Print ISSN: 0925-9902
Electronic ISSN: 1573-7675
DOI
https://doi.org/10.1007/s10844-009-0111-x

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