Dynamic Selling Mechanisms for Product Differentiation and Learning.

PRODUCT differentiation; DECISION making in business; DYNAMIC programming; PRODUCT quality; CONSUMER preferences; COST

Changing regulations, macroeconomic factors, and technological innovations cause substantial uncertainty in the cost of providing services. In their study "Dynamic Selling Mechanisms for Product Differentiation and Learning," N. Bora Keskin and John R. Birge analyze the design of vertically differentiated product lines in the face of cost uncertainty. Keskin and Birge show that, although cost uncertainty makes a seller shrink its product line, the opportunity to learn about costs encourages expanding the product line. Keskin and Birge also show that imposing minimum quality standards on the product line can facilitate learning and consequently, yield good profit performance. We consider a firm that designs a vertically differentiated product line for a population of customers with heterogeneous quality sensitivities. The firm faces an uncertainty about the cost of quality, and we formulate this uncertainty as a belief distribution on a set of cost models. Over a time horizon of T periods, the firm can dynamically adjust its menu and make noisy observations on the underlying cost model through customers' purchasing decisions. We characterize how optimal product differentiation depends on the "informativeness" of quality choices, formally measured by a contrast-to-noise ratio defined on the firm's feasible quality set. Based on this, we design a minimum quality standard (MQS) policy that mimics the salient features of the optimal product differentiation policy and prove that the MQS policy is near-optimal. We also prove that, if there exists a certain continuum of informative quality choices, then even a myopic policy that makes no attempt to learn exhibits near-optimal profit performance. This stands in stark contrast to the poor performance of myopic policies in pricing and learning problems in the absence of product differentiation. Finally, we extend our results to the case where the firm simultaneously learns the customers' quality-sensitivity distribution as well as the cost model. [ABSTRACT FROM AUTHOR]

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