Data Augmentation by predicting spending pleasure using commercially available external data
Baecke, Philippe Van den Poel, Dirk
Baecke, Philippe
Van den Poel, Dirk
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Publication Type
Journal article
Editor
Supervisor
Publication Year
2011
Journal
Journal of Intelligent Information Systems
Book
Publication Volume
36
Publication Issue
3
Publication Begin page
367
Publication End page
383
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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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Keywords
Marketing & Sales