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A Learning Approach for Interactive Marketing to a Customer Segment

Sloan School of Management and Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Kenan-Flagler Business School, University of North Carolina, Chapel Hill, North Carolina 27599
dbertsim{at}mit.edu
adam_mersereau{at}unc.edu

When a marketer in an interactive environment decides which messages to send to her customers, she may send messages currently thought to be most promising (exploitation) or use poorly understood messages for the purpose of information gathering (exploration). We assume that customers are already clustered into homogeneous segments, and we consider the adaptive learning of message effectiveness within a customer segment. We present a Bayesian formulation of the problem in which decisions are made for batches of customers simultaneously, although decisions may vary within a batch. This extends the classical multiarmed bandit problem for sampling one-by-one from a set of reward populations. Our solution methods include a Lagrangian decomposition-based approximate dynamic programming approach and a heuristic based on a known asymptotic approximation to the multiarmed bandit solution. Computational results show that our methods clearly outperform approaches that ignore the effects of information gain.

Subject classifications: dynamic programming/optimal control; relaxations; multiarmed bandit problem; marketing; advertising and media.
History: Received December 2003; revision received December 2006; accepted December 2006.