Data Augmentation by Predicting Spending Pleasure Using Commercially Available External Data
AbstractSince 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 purchase 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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Bibliographic InfoPaper provided by Ghent University, Faculty of Economics and Business Administration in its series Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium with number 09/596.
Length: 30 pages
Date of creation: Jun 2009
Date of revision:
customer relationship management (CRM); data augmentation; commercially available external data; new customer acquisition; random forests; purchase behavior; attitude; spending pleasure;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-07-28 (All new papers)
- NEP-CSE-2009-07-28 (Economics of Strategic Management)
- NEP-MKT-2009-07-28 (Marketing)
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