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.

A method is described for real-time market intelligence and competitive analysis. News stories are collected online for a designated group of companies. The goal is to detect critical differences in the text written about a company versus the text for its competitors. A solution is found by mapping the task into a non-stationary text categorization model. The overall design consists of the following components: (a) a real-time crawler that monitors newswires for stories about the competitors (b) a conditional document retriever that selects only those documents that meet the indicated conditions (c) text analysis techniques that convert the documents to a numerical format (d) rule induction methods for finding patterns in data (e) presentation techniques for displaying results. The method is extended to combine text with numerical measures, such as those based on stock prices and market capitalizations, that allow for more objective evaluations and projections.