2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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4 - Tractable Equilibria For Sponsored Search With Budget
Optimizing Bidders
Dragos Florin Ciocan, INSEAD,
Krishnamurthy Iyer
We examine a model of sponsored search markets where bidders strategically
choose their budgets and bids, while the ad 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.
SB47
209C-MCC
New Topics in Revenue Management and Pricing
Sponsored: Revenue Management & Pricing
Sponsored Session
Chair: So Yeon Chun, McDonough School of Business, Georgetown
University, Washington, DC, United States,
1 - Worker Poaching In A Supply Chain: Enemy From Within?
Gad Allon, Northwestern University,
, Achal Bassamboo,
Evan Barlow
Poaching workers has become a universal practice. We explore worker poaching
between firms linked in a supply chain. We show that the classical intuition from
labor economics is insufficient in explaining poaching between supply chain
partners. We also show how and under what conditions worker poaching can
actually improve supply chain performance. Finally, we show how the
equilibrium identity of the supply chain bottleneck depends on the interaction
between hiring, poaching, and productivity.
2 - The Revenue Impact Of Dynamic Pricing Policies In Major League
Baseball Ticket Sales
Joseph Xu, University of Pennsylvania, Philadelphia, PA, United
States, Senthil Veeraraghavan,
Peter Fader
We study RM implementation of multiproduct dynamic pricing by a Major
League Baseball franchise for their single game tickets. We develop a
comprehensive customer choice model to calibrate and design a dynamic pricing
policy for the franchise. Our model also incorporates external factors that drive
customer valuation of sports tickets, such as the effect of the home team’s on-field
performance and the effect of overall attendance level. Our counterfactuals show
potential revenue improvement of up to 15% through the effective use of
dynamic pricing. We also find that a properly calibrated fixed pricing policy can
achieve similar levels of performance as the optimal dynamic pricing policy.
3 - Designing Rewards-based Crowdfunding Campaigns For
Strategic Contributors
Soudipta Chakraborty, Duke University, Durham, NC, 27708,
United States, , Robert Swinney
We study a model of rewards crowdfunding with the all or nothing funding
mechanism. The creator of a crowdfunding campaign sets a target funding level
and the campaign is successful only if the funding it receives meets this target. A
creator can have two possibly competing objectives: maximize the likelihood of
success and maximize the expected total funding. The contributors incur a
transaction cost while pledging to a campaign. As a result, they behave
strategically and decide whether to pledge at the beginning or to wait till the
target is met. We analyze how a creator, who encounters such strategic behavior,
can achieve her objectives by optimally using the operational parameters of her
campaign.
4 - Setting The Optimal Value Of Loyalty Points
So Yeon Chun, McDonough School of Business, Georgetown
University, , Dan Andrei Iancu,
Nikolaos Trichakis
A loyalty program introduces a new currency—the points—through which
customers transact with a firm. We study the problem of optimally setting the
monetary value of points, i.e., pricing in this new currency, in a multi-period
setting. We first show that point pricing is different from cash pricing primarily
due to the way points are accounted for, as liabilities on the firm’s balance sheet,
and then we characterize the optimal cash and point pricing policies.
SB48
210-MCC
Social Media Analytics for Businesses
Invited: Social Media Analytics
Invited Session
Chair: Panagiotis Adamopoulos, New York University,
School of Business, New York, NY, 11111, United States,
1 - Monetizing Sharing Traffic Through Incentive Design: Evidence
From A Randomized Field Experiment
Tianshu Sun, University of Southern California,
3330 Van Munching Hall, Los Angeles, CA, 20742, United States,
, Siva Viswanathan, Elena Zheleva
Using a large-scale randomized field experiment, we examine whether and how
firms can engage customers involved in online social sharing, through the design
of novel incentive mechanisms. We find evidence that incentive design has a
significant impact on both sender’s purchase and referrals, but in a different ways.
Specifically, compared to the senders who receive non-shareable promotional
code, senders who receives shareable code are less likely to make purchases
themselves, but much more likely to make further referrals. We further leverage
variation in incentive design to untangle three motives underlying the sender’s
sharing:self-regarding, other-regarding, group-regarding motive.
2 - Realizing The Activation Potential Of Online Communities
Marios Kokkodis, Boston College,
In this work we present a data-driven stochastic framework that identifies which
users and when are more likely to become heavy contributors in an online
community.
3 - Word Of Mouth Vs. Word Of Health Inspectors: Evidence From
Restaurant Reviews
Chenhui Guo, University of Arizona, 1130 E Helen St,
McClelland Hall 430, Tucson, AZ, 85721, United States,
, Paulo B Goes, Mingfeng Lin
Prior to purchase, consumers are naturally exposed to multiple sources of quality
information. We study whether and how consumer word of mouth of
restaurants—both volume and valence—is influenced by co-presence of
information from health inspectors. We build a simple analytical model and
conduct an empirical study using data from a leading consumer review site,
showing that the availability of official information has a significant dampening
effect on the volume of reviews generated by consumers. Moreover, the effect on
valence is significantly positive, with a very small magnitude.
4 - The Role Of Dimensionality Reduction In Binary Classification For
Social Network Data
Jessica Clark, New York University, ,
Foster Provost
Dimensionality reduction is regarded as a key part of the predictive analytics
process. We take a design-science approach to analyzing the role of
dimensionality reduction via matrix factorization for binary classification using
large, sparse social network data. The experiments in this work (which span a
variety of data sets, modeling techniques, and DR methods) find that DR at best
provides little advantage in terms of classification performance, and at worst can
significantly negatively impact performance. The results emphasize the need for
caution when utilizing DR in predictive modeling, which should serve as a
guideline for applied data science researchers and industry practitioners.
SB49
211-MCC
Case Competition II
Sponsored: Education (INFORMED)
Sponsored Session
Chair: Palaniappa Krishnan, University of Delaware, Newark, DE,
United States,
1 - Dynamic In-Game advertising: Managing Complex
High-Stakes Operations
Alan Scheller-Wolf, Carnegie Mellon University, Pittsburgh, PA,
United States, John Turner
Dynamic in-game advertising is an advanced form of advertising in which ads are
displayed on electronic billboards, stadium walls, or in other visually appealing
spots within the 3D worlds of video games. This case teaches students not only
about the economics of online advertising and how to solve complex multi-
objective ad planning problems using goal programming, but also covers broader
modeling concepts, practical modeling considerations, and discusses relevant
strategic issues from the fast-growing and fast-changing online advertising
industry.
SB49