Mailing decisions in the catalog sales industry
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Measuring and Mitigating the Costs of Stockouts
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Reinforcement learning with a bilinear q function
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Dynamic Capacity Allocation to Customers Who Remember Past Service
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Model selection in markovian processes
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Computers and Industrial Engineering
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Deciding who should receive a mail-order catalog is among the most important decisions that mail-order-catalog firms must address. In practice, the current approach to the problem is invariably myopic: firms send catalogs to customers who they think are most likely to order from that catalog. In doing so, the firms overlook the long-run implications of these decisions. For example, it may be profitable to mail to customers who are unlikely to order immediately if sending the current catalog increases the probability of a future order. We propose a model that allows firms to optimize mailing decisions by addressing the dynamic implications of their decisions. The model is conceptually simple and straightforward to implement. We apply the model to a large sample of historical data provided by a catalog firm and then evaluate its performance in a large-scale field test. The findings offer support for the proposed model but also identify opportunities for further improvement.