An Empirical Model of Optimal Dynamic Product Launch and Exit Under Demand Uncertainty

Abstract

This paper considers the decision problem of a firm that is uncertain about the demand, and hence profitability, of a new product. We develop a model of a decision maker who sequentially learns about the true product profitability from observed product sales. Based on the current information, the decision maker decides whether to scrap the product. Central to this decision problem are sequential information gathering, and the option value of scrapping the product at any point in time. The model predicts the optimal demand for information (e.g., in the form of test marketing), and it predicts how the launch or exit policy depends on the firm's demand uncertainty. Furthermore, it predicts what fraction of newly developed products should be launched on average, and what fraction of these products will “fail,” i.e., exit. The model is solved using numerical dynamic programming techniques. We present an application of the model to the case of the U.S. ready-to-eat breakfast cereal industry. Simulations show that the value of reducing uncertainty can be large, and that under higher uncertainty firms should strongly increase the fraction of all new product opportunities launched, even if their point estimate of profits is negative. Alternative, simpler decision rules are shown to lead to large profit losses compared to our method. Finally, we find that the high observed exit rate in the U.S. ready-to-eat cereal industry is optimal and to be expected based on our model.new product strategy, product launch, product exit, managerial decision making under uncertainty, Bayesian learning, numerical dynamic programming, dynamic structural models