IE SEMINAR:Estimation using marginal competitor sales information

Guest: Müge Tekin, Rotterdam School of Management (RSM), Erasmus University (Netherlands)

Title: Estimation using marginal competitor sales information (IE, DSA)

Date/Time: March 26, 2025,13:40

Location: FENS G029

Abstract: An abiding preoccupation for firms is how customers value their product compared to the competitor’s. This is difficult to quantify and estimate from data as even though prices are public information, competitors’ sales are typically unobservable. There are some industries however, most prominently the hotel industry, where marginal aggregated information about competitor sales can be obtained through third-party information brokers. In the hotel industry these reports (called STR reports) are widely subscribed; a hotel can participate by reporting its sales information and in turn subscribe to get the marginal sales information of its competitor set, albeit aggregated across groups and the lengths-of-stay (LOS). Such data, however, is not widely incorporated into revenue management (RM) estimation, possibly for the lack of robust models and methodologies to do so. In this paper we tackle this estimation problem under a simple market-share model, focusing on the hotel industry, and develop methodologies to overcome the following significant challenges: (i) competitor’s data is aggregated across multiple LOS with distinct demands (ii) we do not observe no-purchasers, i.e. those who purchase neither ours nor the competitor’s products, and finally, (iii) the competitor makes private sales to groups before the retail sales period; thus even the competitor’s capacity is unobservable. Using Monte-Carlo simulation and a model of generalized Nash competition, we first test our procedure on synthetic data; our method nearly recovers the true parameters in all cases. We then apply it to real hotel bookings data, comparing with alternate estimation methods from the network tomography and RM literature. (Joint with Kalyan Talluri, Imperial College Business School)

Bio: Müge Tekin is an Assistant Professor of Operations Management at the Rotterdam School of Management (RSM), Erasmus University (Netherlands). Before joining RSM, Müge worked at Imperial College Business School (UK) as a postdoctoral researcher for 1.5 years. She earned her Ph.D. in Operations Management from Universitat Pompeu Fabra (Spain) in 2018, a Masters degree in Industrial Engineering from Koç University (Turkey) in 2012 and B.S. in Industrial Engineering from Bilkent University (Turkey) in 2010. Her research focuses on service operations, specifically how competitive intelligence and advanced data analytics can be leveraged to improve firm decision-making. She specializes in demand estimation, using customer choice behavior models to understand customer preferences and purchasing dynamics in the presence of unobservable data and persistent endogeneity issues. To address these challenges, she employs a combination of econometrics, ML and optimization methods to disentangle causal relationships and build robust models that generate actionable insights for optimizing operational strategies.