2016 INFORMS Annual Meeting Program

MD46

INFORMS Nashville – 2016

MD46 209B-MCC Customer Choice Estimation and Assortment Optimization Sponsored: Revenue Management & Pricing Sponsored Session Chair: Vineet Goyal, Columbia University, New York, NY, United States, vgoyal@ieor.columbia.edu 1 - Rationalizing Empirical Choice Counts We consider the rank-aggregation problem where the objective is to find the ranking over n items that minimizes the number of conflicts with the given choice observations. A ranking has a conflict with a choice observation (i, S) if i is not the most preferred item in the subset S according to the ranking. This problem appears in many practical applications. This a known NP-hard problem with almost no algorithms that provide theoretical guarantees. We propose a graphical model approach and show that the complexity scales in the tree-width of the graph, defined on the choice sets. Numerically, our algorithm out-performs existing heuristics for important sub-classes of problems of interest in operations. 2 - The Impact Of Consumer Search Cost On Assortment Planning And Pricing Ozge Sahin, Johns Hopkins University, ozge.sahin@jhu.edu, Ruxian Wang Consumers search for product information to resolve valuation uncertainties before purchase. Under the consider-then-choose policy: a consumer forms her consideration set by balancing utility uncertainty and search cost, then she evaluates all products in her consideration set and chooses the one with the highest net utility. The choice behavior within consideration sets is governed by the multinomial logit model. The assortment problems are NP-hard. For the joint assortment planning and pricing problem, we show that the intrinsic-utility- ordered assortment and the quasi-same-price policy, which charges a same price for all products except at most one, are optimal for the joint problem. 3 - Waste Reduction Of Perishable Products Using Dynamic Pricing Arnoud V. den Boer, University of amsterdam, KdVI, science park 904, room F3.33, amsterdam, Netherlands, A.V.denBoer@uva.nl, Jieying Jiao According to the 2013 UN Food Wastage Footprint study, “approximately one- third of all food produced for human consumption in the world is lost or wasted”. The impact of this waste can hardly be overstated. Some Dutch supermarkets try to mitigate food loss by discounting products sold on their expiry date. This measure reduces the expected waste, but its effect on profit is an open question. In this talk we show how to optimize the discount percentage in a Markovian framework, and we discuss the effect on profit and waste in a diffusion limit of the inventory process. MD47 209C-MCC Pricing, Promotion Planning, and Revenue Management Sponsored: Revenue Management & Pricing Sponsored Session Chair: N. Bora Keskin, Duke University, Durham, NC, United States, bora.keskin@duke.edu 1 - Multiple Equilibria In Pricing Problems With Network Effects William L Cooper, University of Minnesota, 111 Church Street S.E., University of Minnesota, Minneapolis, MN, 55455, United States, billcoop@umn.edu, Chenhao Du, Zizhuo Wang We consider multi-product price-optimization problems with network effects wherein the expected utility each individual customer obtains from a product is increasing in the number of other customers who buy that product. Such network effects give rise to equilibrium constraints that describe how sales depend upon prices. In some cases there may be multiple different vectors of sales quantities that satisfy the equilibrium constraint for a given vector of prices. Moreover, the seller’s revenue may be quite different at those different equilibria. In this talk, we compare the seller’s revenue-maximizing prices under differing assumptions about which of the multiple equilibria will prevail. Srikanth Jagabathula, NYU Stern School of Business, sjagabat@stern.nyu.edu, Paat Rusmevichientong

2 - Value Of Targeted Promotions: Evidence From A Large Department Store Bharadwaj Kadiyala, PhD Candidate, The University of Texas at Dallas, Richardson, TX, 75080, United States, bharadwaj.kadiyala@utdallas.edu, Ozalp Ozer, A Serdar Simsek Gift cards have become a popular vehicle for promotional campaigns run by many departmental, consumer electronic, and online retail stores. Using a proprietary data set from a large department store, we investigate the value of targeted marketing efforts via emails in the context of gift-card promotional campaigns. We estimate the effects of online gift card promotions on customer purchase behavior and then discuss how to use these estimates to plan for targeted promotional events, a step towards one-to-one marketing. 3 - Pricing From Observational Data Nathan Kallus, Assistant Professor, Cornell University and Cornell Tech, 111 8th Avenue #302, New York, NY, 10011, United States, kallus@cornell.edu, Dimitris Bertsimas Given price-demand data, pricing is often addressed by a predictive approach: fit a model to predict demand given price observation, substitute into profit, and optimize price. Predictive approaches fail to find the optimal price, which is not generally identifiable from observational data. We bound suboptimality. We provide identifiability conditions and corresponding methods for pricing and prove consistency, asymptotic normality, and convergence rates. We develop a hypothesis test for optimality of pricing from observational data and demonstrate predictive approaches lose significant profit while our parametric method is indistinguishable from optimal and recovers 36-70% of losses. 4 - Using Contingent Markdown With Reservation To Deter Strategic Consumer Behavior Gustavo Vulcano, NYU/UTDT, New York, NY, United States, gvulcano@stern.nyu.edu, Navaporn Surasvadi, Christopher S Tang We examine a contingent price markdown (CM) mechanism with guaranteed reservations under which a retailer sells multiple units to forward-looking consumers who arrive over time according to a Poisson process. We study the consumer purchasing behavior in equilibrium, and numercially compare the performance of our mechanism against two benchmarks: Fixed Price (FP) and Pre-announced Discount (PD). Using extensive numerics, we identify market conditions under which CM dominates both FP and PD in terms of the retailer’s revenue and consumer’s surplus. Finally, through a fluid approximation to the stochastic model, we analytically show that CM weakly dominates the other two mechanisms. 210-MCC Peeling Back the Onion in Social Media Analysis Invited: Social Media Analytics Invited Session Chair: Chris Smith, Air Force Institute of Technology, 2950 Hobson Way, Wright-Patterson AFB, OH, 45433, United States, cms3am@virginia.edu 1 - Assessing Bias Correction For Social Media Samples Christopher Wienberg, USC Institute for Creative Technologies, cwienberg@ict.usc.edu While social media has made it possible to quickly and cheaply gather the opinions and experiences of large numbers of people, it is unclear how well social media users represent broader real-world populations, introducing the possibility of serious estimation bias. We investigate the applicability of traditional sample reweighting techniques for estimating real world population characteristics from a sample of social media users, with an eye towards using automatic attribute inference from social media profiles to make predictions about real-world populations. 2 - Who’s In Charge: Looking At Hierarchy And Heterarchy On Social Media Robert Schroeder, Naval Postgraduate School, rcschroe@nps.edu, Sean Everton Depending on the context of the information being passed on social media, conversations can seem to be fairly flat with no-one in charge, or highly centralized with a few main accounts key to the flow of information. With networks of different size, this paper compares various network measures of hierarchy in order to better classify social media conversations. MD48

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