Informs 2015

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INFORMS Philadelphia – 2015
299
TB43
43-Room 103A, CC
Choice Modeling and Assortment Optimization
Sponsor: Revenue Management and Pricing
Sponsored Session
Chair: Vineet Goyal, Columbia University IEOR department, 500 West
120th Street, 304 Mudd, New York, NY, 10027, United States of
America,
1 - Approximation Algorithms for Dynamic Assortment
Optimization Models
Ali Aouad, Massachusetts Institute of Technology, 77
Massachusetts Avenue, Bldg. E40-149, Cambridge, MA, 02139,
United States of America, Danny Segev,
Retsef Levi
We study the joint assortment and inventory management problem, where
demand consists in a random sequence of heterogeneous customers. Although
the problem is hard in general, we provide the first polynomial time algorithms
that attain constant approximations, for variants proposed in previous literature
as well as more general choice models. In addition, our algorithms provide
practical means for solving large-scale instances and for incorporating more
realistic contraints.
2 - Capacity Constrained Assortment Optimization under the Markov
Chain Based Choice Model
Chun Ye, Columbia University IEOR department, 500 West 120th
Street, Mudd 315, New York, NY, 10027, United States of
America, Danny Segev, Vineet Goyal,
Antoine Desir
We consider a capacity constrained assortment optimization problem under the
Markov Chain based choice model proposed by Blanchet et al. We first show that
even severely-restricted special cases are APX-hard. We then present a constant
factor approximation for the general problem. Our algorithm is based on a “local-
ratio” method that allows us to transform a non-linear revenue function into a
linear function over appropriately modified item prices.
3 - Assortment Optimization under a Random Swap Based
Distribution over Permutations Model
Antoine Desir, Columbia University IEOR department, 500 West
120th Street, Mudd 315, New York, NY, 10027, United States of
America, Vineet Goyal, Danny Segev
We consider a special class of distribution over permutations model based on
modeling the consumer preferences by a random number of random swaps from
a small set of fixed preference lists. This model is motivated from practical
applications where preferences of “similar” consumers differ in a small number of
products. We present polynomial time approximation schemes for capacity
constrained assortment optimization problem under the random swap based
distribution over permutation model.
4 - Design of an Optimal Membership Promotion Policy
with Experiments
Spyros Zoumpoulis, Insead, ,
Duncan Simester, Artem Timoshenko
Deciding what customer to target with what type of membership promotion is
among the most important decisions that wholesale clubs face. We use the results
of a large-scale membership promotion field experiment involving multiple types
of membership promotions to propose various promotion policies, each relying on
a different algorithm for customer segmentation. We then evaluate the
performance of the proposed policies as implemented in a large-scale field test.
TB44
44-Room 103B, CC
Machine Learning in Operations
Sponsor: Revenue Management and Pricing
Sponsored Session
Chair: Srikanth Jagabathula, NYU, 44 West Fourth Street, New York,
United States of America,
1 - Prediction vs Prescription in Data-driven Pricing
Nathan Kallus, MIT, 77 Massachusetts Ave., E40-149, Cambridge,
MA, 02139, United States of America, ,
Dimitris Bertsimas
We study the problem of data-driven pricing and show that a naive but common
predictive approach leaves money on the table. We bound missed revenue
relative to the prescriptive optimum, which we show is unidentifiable from data.
We provide conditions for identifiability and appropriate pricing schemes. A new
hypothesis test shows that predictive approaches are practically insufficient while
parametric approaches often suffice but only if they take into account the
problem’s prescriptive nature.
2 - The Big Data Newsvendor: Practical Insights from
Machine Learning
Gah-Yi Vahn, Assistant Professor, London Business School,
Sussex Place, Regent’s Park, London, NW1 4SA, United Kingdom,
Cynthia Rudin
We study the newsvendor problem when one has n observations of p features
related to the demand as well as demand data. Both low- and high-dimensional
data are considered. We propose Machine Learning (ML) and Kernel
Optimization (KO) approaches, and derive tight bounds on their performance. In
a nurse staffing case study we find that the best KO and ML results beat best
practice by 23% and 24% respectively.
3 - Applying Machine Learning to Revenue Management at Groupon
David Simchi-levi, Professor, Massachusetts Institute of
Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139,
United States of America, , Alexander Weinstein,
He Wang, Wang Chi Cheung
We propose a new data-driven pricing algorithm for online retailers, which learns
customer demand from online transaction data. Our method first generates
multiple demand functions using a clustering algorithm, and then learns on the
fly which demand function is more likely to be correct. We will also discuss some
field experiment result through collaborating with Groupon, a large daily deal
website.
4 - Demand Forecasting when Customers Consider, Then Choose
Ying Liu, Stern School of Business, New York University, 44 West
4th Street, KMC 8-154, New York, NY, 10012, United States of
America, , Srikanth Jagabathula
We consider the problem of demand forecasting when customers choose by first
forming a consideration set and then choosing the most preferred product from
the consideration set. The consideration set is sampled from a general model over
subsets. We propose techniques to estimate such models from purchase
transaction data.
TB45
45-Room 103C, CC
Revenue Management for Marketing
Sponsor: Revenue Management and Pricing
Sponsored Session
Chair: John Turner, Assistant Professor, University of California, Irvine,
Room SB2 338, The Paul Merage School of Business, Irvine, CA,
92697-3125, United States of America,
1 - Scheduling of Guaranteed Targeted Display Advertising under
Reach and Frequency Requirements
Ali Hojjat, University of California Irvine, Paul Merage School of
Business, Irvine, CA, 92697, United States of America,
John Turner, Suleyman Cetintas, Jian Yang
We propose a novel mechanism for the scheduling of guaranteed targeted
advertising in online media. We consider a new form of contract in which
advertisers specify the number of unique individuals (reach) and the minimum
number of times (frequency) each individual should be exposed. We further
integrate a variety of new features such as desired diversity and pacing of ads over
time or the number of competing brands seen by each individual. We perform
extensive numerical tests on industry data.
2 - Transaction Attributes and Customer Valuation
Michael Braun, Associate Professor Of Marketing, Southern
Methodist University, 6212 Bishop Blvd., Fincher 309, Dallas, TX,
75275, United States of America, Eli Stein,
David Schweidel
We propose a model of customer value and marketing ROI that incorporates
transaction-specific attributes and unobserved heterogeneity. From this model,
one can estimate an upper bound on the amount to invest in retaining a
customer. This amount depends on the recency and frequency of past customer
purchases. Using data from a B2B service provider, we estimate the revenue lost
by the firm when it fails to deliver a customer’s requested level of service.
3 - Auctions with Dynamic Costly Information Acquisition
Negin Golrezaei, Auctions With Dynamic Costly Information
Acquisition, University of Southern California, Bridge Memorial
Hall, 3670 Trousdale Parkway, Los Angeles, CA, 90089, United
States of America, , Hamid Nazerzadeh
We study the mechanism design problem for the seller of an indivisible good in a
setting where buyers can purchase the additional information and refine their
valuations for the good. This is motivated by information structures in online
advertising where advertisers can target users using cookie-matching services. For
this setting, we propose a rich class of dynamic mechanisms, called Sequential
Weighted Second-Price, which encompasses the optimal and the efficient
mechanisms as special cases.
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