2015 Informs Annual Meeting

WA46

INFORMS Philadelphia – 2015

WA45 45-Room 103C, CC

4 - A Dynamic Learning Approach for Personalized Promotion Recommendations Adam Elmachtoub, Assistant Professor, Columbia IEOR, 500 West 120th St, New York, NY, United States of America, adam@ieor.columbia.edu, Markus Ettl, Sechan Oh, Marek Petrik Many companies are aiming to offer real-time personalized promotions to online shoppers with the goal of increasing conversion rates and revenue. In this work, we provide a dynamic learning model and algorithm that simultaneously maximizes revenue while learning how customers choose based on their attributes and the promotions they receive. We provide theoretical bounds on the regret as well as new analytical tools to determine feature importance in the context of promotion recommendations. WA44 44-Room 103B, CC Dynamic Pricing Sponsor: Revenue Management and Pricing Sponsored Session Chair: Candace Yano, University of California, Berkeley, IEOR Dept.and Haas School of Business, Berkeley, CA, 94720, Mohammad Ghuloum, Doctoral Student, Indiana University, 1309 E 10th St, Bloomington, IN, 47405, United States of America, mghuloum@indiana.edu, Goker Aydin, Gilvan (Gil) Souza Trade-in managers continuously monitor their inventory of used products, and adjust the acquisition and selling prices accordingly. Considering such a firm, we study a novel dynamic pricing problem, where not only the demand of the product is random and sensitive to the selling price, but also its supply is random and sensitive to the acquisition price. 2 - Pricing in Crowdfunding Ming Hu, Associate Professor, University of Toronto, 105 St. George Street, Toronto, Canada, Ming.Hu@Rotman.Utoronto.Ca, Mengze Shi, Xi Li, Longyuan Du We study the pricing decisions under an all-or-nothing crowdfunding scheme. First, menu or intertemporal pricing is more likely than a single price to be optimal. Second, dynamic pricing (contingent on the pledge amount) can help the creator to stay over the funding tipping point over time, increasing success rate and profitability. 3 - Dynamic Competition under Market Size Dynamics: Balancing the Exploitation-induction Trade-off Nan Yang, Assistant Professor, University of Washington at St. Louis, St. Louis, MO, 63130, United States of America, yangn@wustl.edu, Renyu Zhang We study a dynamic competition model, in which retail firms periodically compete on promotional effort, sales price, and service level over a finite planning horizon. The key feature of our model is that the current decisions influence the future market sizes through the service effect and the network effect. Using the linear separability approach, we characterize the pure strategy Markov perfect equilibrium in both the simultaneous competition and the promotion-first competition. 4 - Optimizing Pre-season Order Quantities in the Presence of Planned Promotions Dimin Xu, UC Berkeley, Haas School of Business, Berkeley, CA, United States of America, dimin_xu@haas.berkeley.edu, Candace Yano Most retailers plan major promotions well before a product’s selling season, possibly to coincide with storewide sales events. We optimize the pre-season order quantity for a product considering planned promotions (and consequent time-varying prices), when demand is price- and time-sensitive and stochastic. Our approach accounts for both systematic fluctuations and uncertainty in the implied salvage value over the season. We present structural results and managerial insights. United States of America, yano@haas.berkeley.edu 1 - Optimal Dynamic Pricing for Trade-in Programs

Topics in Revenue Management Sponsor: Revenue Management and Pricing Sponsored Session Chair: Florin Ciocan, INSEAD, Boulevard de Constance 77305, Fontainebleu, France, florin.ciocan@insead.edu 1 - When Fixed Pricing Meets Priority Auctions: Service Systems with Dual Modes Jiayang Gao, PhD Candidate, Cornell University, 507 Hasbrouck Apts, Ithaca, NY, 14850, United States of America, jg838@cornell.edu, Huseyin Topaloglu, Krishnamurthy Iyer Suppose a firm offers two modes of service: a fixed price, FIFO queue, and a priority queue. Customers choose a mode to participate, as well as their bids if they join the priority queue. We prove that in the unique symmetric equilibrium, customer behavior has a threshold structure, in which customers with very high and very low patience levels join the priority queue, whereas those with intermediate patience levels join the FIFO queue. We then discuss the firm’s server allocation problem. 2 - Product Support Forum: Customers as Partners in Service Delivery Online product support forums where customers can post complaints and questions, or report issues about a product or service abound. More and more companies crowdsource their product and service support back to their customers, employing a few dedicated service operators.Through an analytical model, we characterize the equilibrium behavior of such a service system and compare it with a call center model. 3 - Econometrics for Learning Agents Vasilis Syrgkanis, Microsoft Research, 641 Avenue of the The goal of this paper is to develop a theory of inference of player valuations from observed data in the generalized second price auction without relying on the Nash equilibrium assumption. Existing work assumes that each player’s strategies are best responses to the observed play of others. We show how to perform inference relying on the weaker assumption that players use some form of no- regret learning. We apply our techniques to a dataset from Microsoft’s sponsored search auction system. 4 - Adwords Equilibria with Budgeted Bidders Florin Ciocan, INSEAD, Boulevard de Constance 77305, Fontainebleu, France, florin.ciocan@insead.edu, Krishnamurthy Iyer We examine a model of the AdWords market where bidders strategically choose their budgets and bids, while the network can throttle bidders to optimize its own revenues. We show the equilibria in this market take a simple form and that for these equilibria the network’s optimal throttling policy is greedy. Americas, New York, United States of America, vasy@microsoft.com, Eva Tardos, Denis Nekipelov Konstantinos Stouras, PhD Candidate, INSEAD, Bd. de Constance, Fontainebleau, 77305, France, Konstantinos.Stouras@insead.edu, Serguei Netessine, Karan Girotra

WA46 46-Room 104A, CC Empirical Studies in Public Services: Health Care and Education Sponsor: Manufacturing & Service Oper Mgmt/Service Operations Sponsored Session

Chair: Jun Li, Assistant Professor, Ross School of Business, University of Michigan, 701 Tappan St, Ann Arbor, 48103, United States of America, junwli@umich.edu 1 - A Multiple Case Study of Resource Flow in Education Systems Samantha Meyer, Research Fellow, University of Michigan, Ross School of Business, R5340, Ann Arbor, MI, 48109, United States of America, srmeyer@umich.edu, Karen Smilowitz The US spends more to educate its children than nearly every other developed nation, but scores near the bottom on international tests. Yet, how the US could better use its resources is hard to know. Social scientists focus on the way resources influence power, trust, and competition, whereas operations scholars focus on technical problems of resource distribution and use. The reality is that both matter. In this study we examine the way social and technical issues interact in education systems.

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