2016 INFORMS Annual Meeting Program

TA48

INFORMS Nashville – 2016

TA47 209C-MCC Personalized E-commerce Sponsored: Revenue Management & Pricing Sponsored Session Chair: Van Anh Truong, Cornell University, Ithaca, NY, United States, vat3@cornell.edu 1 - Distribution-free Pricing Ming Hu, University of Toronto, Toronto, ON, Canada, Ming.Hu@rotman.utoronto.ca, Hongqiao Chen We study a monopoly robust pricing problem in which the seller does not know the customers’ valuation distribution but knows its mean and variance. Such minimum requirement of information is nothing but asking two questions: How much your targeted customers are going to pay on average? And how sure are you? We obtain the best robust price heuristic in closed form and provide its distribution-free, worst-case performance bound. We then provide easily verifiable distribution-free sufficient conditions to guarantee the pure bundle to be more profitable than separately sales. We illustrate the benefit of bundling by a couple of practical examples such as subscription services of digital music. 2 - Revenue Management With Consumer Search Cost Zizhuo Wang, University of Minnesota, Minneapolis, MN, United States, zwang@umn.edu, Yan Liu, William L Cooper We consider a pricing problem in which the product valuations are uncertain to the consumers. The consumers can find the valuation of the product by incurring a search cost. We study the seller’s problems of whether it should lower the search costs of the products, and what prices it should charge. We find that when there are two products, lowering the search cost of one product while maintaining a high search cost for the other product may be optimal. We also show how our results vary depending on the correlation between the uncertainty of the products. 3 - Approximation Algorithms For Product Framing And Pricing Anran Li, Columbia University, al2942@columbia.edu We propose one of the first models of “product framing” and pricing. Product framing refers to the way consumer choice is influenced by how the products are displayed. We present a model where consumers consider only products in a random number of top web pages. Consumers select a product from these pages following a general choice model. We show that the product framing problem is NP-hard. We derive algorithms with guaranteed performance relative to an optimal algorithm under reasonable assumptions. We also present structural results for pricing under framing effects. At optimality, products are sorted in descending order of quality, and prices are shown to be page dependent.

3 - Vssa – A Variable Sample-size Stochastic Approximation Schemes For Stochastic Convex Optimization Uday V. Shanbhag, Pennsylvania State University, udaybag@engr.psu.edu, Afrooz Jalilzadeh, Jose Blanchet, Peter W Glynn Traditional stochastic approximation (SA) schemes employ a single gradient or a fixed batch of noisy gradients in computing a new iterate. We consider SA schemes in which Nk samples are utilized at step k and the total simulation budget is M. This paper derives error bounds in this budget-constrained regime in both strongly convex and convex regimes with constant and increasing sample- sizes. Notably, trade-offs between sample-complexity and computational complexity are examined. Preliminary numerics suggest that such avenues provide approximate solutions in less than a hundredth of the time taken by standard SA schemes with modest drops in accuracy. 4 - A New Consistency Theory For Approximate Bayesian Inference Ye Chen, University of Maryland, yechen@math.umd.edu, Ilya O Ryzhov Approximate Bayesian inference is a powerful methodology for constructing statistical learning mechanisms in problems where incomplete information is collected sequentially. Approximate Bayesian models have been widely applied, but the convergence or consistency results for approximate Bayesian estimators are largely unavailable. We develop a new consistency theory for these learning schemes by interpreting them as stochastic approximation (SA) algorithms with additional “bias” terms. We prove the convergence of a general SA algorithm of this type, and through this, for the first time, show the consistency of several approximate Bayesian methods from the recent literature. TA46 209B-MCC Dynamic Games and Applications to Revenue Management Sponsored: Revenue Management & Pricing Sponsored Session Chair: Konstantinos Bimpikis, Stanford, Stanford, Palo Alto, CA, 94305, United States, kostasb@stanford.edu 1 - Dynamic Selling Mechanisms For Product Differentiation And Learning N. Bora Keskin, Duke University, Durham, NC, United States, bora.keskin@duke.edu, John R Birge We consider a firm that designs a menu of vertically differentiated products for a population of customers with heterogeneous quality sensitivities. The firm faces an uncertainty about production costs. We characterize the structure of the firm’s optimal dynamic learning policy and construct simple and practically implementable policies that are near-optimal. 2 - When Fixed Price Meets Priority Auctions: Service Systems With Dual Modes Krishnamurthy Iyer, Cornell University, kriyer@cornell.edu, Jiayang Gao, Huseyin Topaloglu We consider a service system where service is offered via two modes. The first mode charges a fixed price, and the service discipline is FIFO. In the second mode, called the bid-based priority mode, customers submit a bid, obtain service in the descending order of their bids, and make payments equal to their bids. We assume the customers have heterogeneous waiting costs, and choose the service mode strategically on arrival. We establish the existence and uniqueness of a symmetric equilibrium, which has a simple threshold structure: customers with either high or low waiting cost obtain service from the bid-based priority mode, whereas those with moderate waiting cost obtain service from the FIFO mode. 3 - Customizing Marketing Decisions Using Field Experiments Spyros Zoumpoulis, INSEAD, spyros.zoumpoulis@insead.edu, Theodoros Evgeniou, Duncan I Simester, Artem Timoshenko We investigate how firms can use the results of field experiments to optimize marketing decisions, and in particular allocating different promotional offers to different customer segments for the customer acquisition problem of a large retailer. In the first stand of the work, we solve the problem of finding the optimal one-shot promotion policy: what promotional offer should be sent to what customer segment? In the second strand of the work, we solve the problem of optimally retargeting nonrespondents through promotions in multiple waves: what customer segment should we stop mailing to, and for what segment would we benefit from repeated promotions?

TA48 210-MCC

Social Media Analysis II Invited: Social Media Analytics Invited Session

Chair: Yen-Yao Wang, Michigan State University, 5211 Madison Avenue, Apartment A5, Okemos, MI, 48864, United States, wangyen@broad.msu.edu 1 - A Unified Framework For Credit Evaluation For Internet Finance Companies Meheli Basu, Graduate Research Assistant, University of Pittsburgh, 5820 Elwood Street, APT 33, Pittsburgh, PA, 15232, United States, meb209@pitt.edu We developed and detailed a multi-criteria decision-making framework based on interface of the subjective approach of analytical hierarchical process (AHP) and validated by comparative analysis using the objective approach of data envelopment analysis (DEA) to evaluate credit index. Our framework identifies and weighs the most important characteristics of SMEs and start-ups which contribute to overall credit rating. Although our target implementation group is the internet finance industry, our framework for credit evaluation will also give start-ups and SMEs an insight into favorable criteria for a good credit standing.

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