A learning approach for interactive marketing to a customer segment
成果类型：
Article
署名作者：
Bertsimas, Dimitris; Mersereau, Adam J.
署名单位：
Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); University of North Carolina; University of North Carolina Chapel Hill
刊物名称：
OPERATIONS RESEARCH
ISSN/ISSBN：
0030-364X
DOI：
10.1287/opre.1070.0427
发表日期：
2007
页码：
1120-1135
关键词：
摘要：
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.