Director of Data Science at Vistex
Maarten Oosten is Director of Data Science at Vistex and has 20 years of experience designing and implementing high-end pricing solutions across various industries, ranging from manufacturing and distribution to travel and transportation. Maarten has a Ph.D. in Mathematics and an M.S. in Econometrics. He has held positions as visiting assistant professor at Carnegie Mellon University, as post-doctoral fellow at the University of British Columbia, as director of Science & Research at PROS and as manager of pricing solutions at SAS. He is an active member of the INFORMS Section of Revenue Management and Pricing and the Professional Pricing Society.
Track: Marketing Analytics
Tuesday, April 16, 11:30am–12:20pm
Managing All Pricing Levers
One of the challenges of price optimization is that the price that the manufacturer lists for a product is not the same as the price the buyer pays (sales price) or the price that the seller receives (net price). Besides various types of costs related to delivery and sales, there are can be many price levers in play. Examples are end customer rebates, distributor discount programs, distributor charge-backs, royalties, channel rebates, and sales commission. These price levers are also controlled by the sellers, but at different levels, not necessarily the transaction level. For example, a distributor rebate applies to a subset of products for a specific period. When optimizing prices, all these levers should be taken into account.After a brief discussion of the general concepts, we will illustrate the concepts by means of an example: trade promotion optimization. Trade promotion optimization is similar to promotion optimization, excepts that it approaches the problem from the perspective of the manufacturer. The manufacturer negotiates the promotions with the various retailers. Therefore, the manufacturer should model the behavior of the end users as well as that of the retailer. After all, if the promotion is not attractive for the retailer, there won’t be a promotion. In this paper we discuss the challenges this poses in both the estimation of the promotion effects as well as the optimization of the promotion schedule, and propose models that address these challenges.