Value of Learning and Acting Upon Customer Information

The rapid growth of demand and supply of sophisticated data mining and analytical decision tools calls for research to understand the value of learning and how learning interacts with firm’s day-to-day marketing strategies. In this paper, we consider a market in which the firm can reduce its service cost when the right product is matched with the right customer. With uncertainty on customer types, the firm can gradually learn about customer types using observed service costs realized from recent interaction as a noisy signal. Based on the most updated information, the firm makes matching and selection decisions to maximize long-term profit. Solving the closed form solution to the fully dynamic optimization problem with infinite horizon, we analytically investigate the dynamic and endogenous nature of learning process, the interaction between learning and decision making, and the evolution of profit over time. We also examine how optimal decision path, market size in steady state and evolution of profit are affected by parameters such as discount rate and the precision of information. Extending the model to a setting in which the precision of signals is proportional to the units of goods, we study how firm can endogenize the speed of learning. Our results shed lights on the value of learning and acting upon information, the time to discontinue service to a customer, the duration and amount of short-term fi-nancial losses before learning pays off, and how the firm can modify its decisions to speed up the learning process