Experts Address Series 2017-18 (11 July 2018)
Jul, 04 2018
By Programme Office
Speaker: Professor Alan Montgomery
Professor of Marketing at the Tepper School of Business, Carnegie Mellon University
Visiting Professor, Department of Computer Science, HKU
Abstract: Managers often have information that they want reflected when training predictive models from data. A popular method for doing this is with business rules. These rules are meant to capture managerial knowledge and insights that impose important constraints on decision problems like setting the optimal price. Traditional approaches to price optimization take a two-step approach to setting prices. First a sales response model is specified and the parameters are estimated given an observed dataset. Second this model is used for inference to make decisions about the optimal price. Often the optimal pricing solutions from the estimated sales response model are nonsense suggesting prices that are unfairly high, which leads the manager to impose a set of post hoc constraints on the feasible price space to find a more appropriate solution. We argue that manager’s constraints on the price solution represent prior information about the model. We show that incorporating this information post hoc instead of a priori leads to inefficient pricing decisions. To facilitate the creation of the model and its prior we show how constraints implied by business rules and statements about optimal prices can be translated into informative prior distributions. These prior distributions appropriately weigh the managerial knowledge against the data unlike the traditional approach. In summary, our approach improves the quality of the pricing decisions made by managers and offers a consistent and scientific approach for incorporating managerial expertise into the pricing problem.
For registration, please go to Experts Address section.
All are welcome. Don't miss it.