Mining case bases for action recommendation

Abstract

Corporations and institutions are often interested in deriving marketing strategies from corporate data and providing informed advice for their customers or employees. For example, a financial institution may derive marketing strategies for turning their reluctant customers into active ones and a telecommunications company may plan actions to stop their valuable customers from leaving. In data mining terms, these advice and action plans are aimed at converting individuals from an undesirable class to a desirable one, or to help devising a direct-marketing plan in order to increase the profit for the institution. We present an approach which uses 'role models' for generating such advice and plans. These role models are typical cases that form a case base and can be used for customer advice generation. For each new customer seeking advice, a nearest-neighbor algorithm is used to find a cost-effective and highly probable plan for switching a customer to the most desirable role models. We explore the tradeoff among time, space and quality of computation in this case-based reasoning framework. We demonstrate the effectiveness of the methods through empirical results.