document: Cross channel optimized marketing by reinforcement learning The issues of cross channel integration and customer life time value modeling are two of the most important topics surrounding customer relationship management ( CRM ) today . In the present paper , we describe and evaluate a novel solution that treats these two important issues in a unified framework of Markov Decision Processes ( MDP ) . In particular , we report on the results of a joint project between IBM Research and Saks Fifth Avenue to investigate the applicability of this technology to real world problems . The business problem we use as a testbed for our evaluation is that of optimizing direct mail campaign mailings for maximization of profits in the store channel . We identify a problem common to cross-channel CRM , which we call the Cross-Channel Challenge , due to the lack of explicit linking between the marketing actions taken in one channel and the customer responses obtained in another . We provide a solution for this problem based on old and new techniques in reinforcement learning . Our in-laboratory experimental evaluation using actual customer interaction data show that as much as 7 to 8 per cent increase in the store profits can be expected , by employing a mailing policy automatically generated by our methodology . These results confirm that our approach is valid in dealing with the cross channel CRM scenarios in the real world .
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