Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce

Electronic commerce is revolutionizing the way we think about data modeling, by making it possible to integrate the processes of (costly) data acquisition and model induction. The opportunity for improving modeling through costly data acquisition presents itself for a diverse set of electronic commerce modeling tasks, from personalization to customer lifetime value modeling; we illustrate with the running example of choosing offers to display to web-site visitors, which captures important aspects in a familiar setting. Considering data acquisition costs explicitly can allow the building of predictive models at significantly lower costs, and a modeler may be able to improve performance via new sources of information that previously were too expensive to consider. However, existing techniques for integrating modeling and data acquisition cannot deal with the rich environment that electronic commerce presents. We discuss several possible data acquisition settings, the challenges involved...

• Type: Conference
• Year: 2007
• Where: ACMICEC
• Authors: Foster J. Provost, Prem Melville, Maytal Saar-Tsechansky
• Keywords: ACMICEC 2007, Data Acquisition, ECommerce, Electronic Commerce, Electronic Commerce Modeling