Dynamic Marketing Resource Allocation for Long-Term Profitability : A Pharmaceutical Application

The ability to identify customers' dynamics and its drivers present an opportunity for firms to optimize their marketing resource allocation and increase long-run profitability. Accordingly, we address the following managerial questions in this research: (1) how can firms dynamically segment their customer base? (2) what are the short- and long-term effects of marketing activities? and (3) how should firms allocate and target their marketing resources to maximize long-term profitability? To address these questions, we propose a non-homogeneous hidden Markov model that captures the dynamics in customer behavior and the enduring effect of marketing actions. To optimally allocate marketing activities, we formulate a dynamic programming approach which takes into account the evolution of customers' behavior. We apply the model in the context of pharmaceutical marketing. We identify three prescription-behavior states, a high degree of physicians' dynamics, and substantial long-term effects for detailing and sampling. We find that detailing is most effective as an acquisition tool, whereas sampling is most effective as a retention tool. Our dynamic marketing allocation approach suggests that the firm could increase its pro¿ts substantially, while decreasing its marketing efforts. Our integrative framework provides important implications for dynamically managing customers and maximizing long-run profitability