Customer Relationship Management (CRM) is about introducing the right product to the right customer at the right time through the right channel to satisfy the customer's evolving demands; however, most existing CRM practice and academic research focuses on methods to select the most profitable customers for a scheduled CRM intervention. In this article, we discuss a two-step procedure comprising “adaptive learning” and “proactive” CRM decisions. We also discuss three key components for customer-centric CRM: adaptive learning, forward-looking, and optimization. We then formulate CRM interventions as solutions to a stochastic dynamic programming problem under demand uncertainty in which the company learns about the evolution of customer demand as well as the dynamic effect of its marketing interventions, and make optimal CRM decisions to balance the cost of interventions and the long-term payoff. Finally, we choose two examples to demonstrate the input, output, and benefit of “adaptive” learning and “proactive” CRM.
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