Customer modeling is a critical component of customer relationship management (CRM). Successful customer modeling requires a holistic view and the consolidation of all customer information available to the business, which is typically stored in a relational database. With this understanding, customer modeling in CRM can be viewed as a special case of the relational learning problem, a recent extension of the traditional machine learning problem that aims to model the relational interdependencies within a database containing multiple interlinked tables. We establish in this paper the connection between relational learning and CRM analysis through detailed discussion of the tasks of customer classification and product recommendation, supported by examples of empirical results on seven real-world CRM data sets. We demonstrate that relational learning approaches can be valuable tools for a variety of CRM modeling tasks and discuss limitations and CRM specific extensions of these general relational learning approaches.
By: Claudia Reisz; Zan Huang
Published in: RC23735 in 2005
LIMITED DISTRIBUTION NOTICE:
This Research Report is available. This report has been submitted for publication outside of IBM and will probably be copyrighted if accepted for publication. It has been issued as a Research Report for early dissemination of its contents. In view of the transfer of copyright to the outside publisher, its distribution outside of IBM prior to publication should be limited to peer communications and specific requests. After outside publication, requests should be filled only by reprints or legally obtained copies of the article (e.g., payment of royalties). I have read and understand this notice and am a member of the scientific community outside or inside of IBM seeking a single copy only.
Questions about this service can be mailed to reports@us.ibm.com .