Conference Sessions - Jesse H. Jones Graduate School of ...

TC01 MARKETING SCIENCE CONFERENCE – 2011
2 - Conspicuous Consumption and Dynamic Pricing
Richard Schaefer, PhD Student, University of Texas at Austin,
1 University Station, B6700, McCombs School of Business,
Austin, TX, 78703, United States of America,
Richard.Schaefer@phd.mccombs.utexas.edu, Raghunath Rao
We study a producer’s dynamic pricing policy when marketing a durable good,
particularly an item that provides consumer utility via two mechanisms; specifically,
consumers experience an intrinsic consumption utility and an externality (denoted
‘fashion utility’) that depends upon the conspicuousness of the product and the
identity of other consumers of using the same product.In our analytical model, we
consider the joint impact of consumption utility and fashion utility, and our results
reverse the direction of causality long emphasized in prior studies. We show that
products with high intrinsic quality command higher prices due to greater input costs;
with a higher retail price, such products become exclusive and, hence, more
fashionable when consumption is visible. Two key results emerge from the analysis of
our dynamic model: a) whether the producer sells its product via skimming over time
depends upon the discount factor, and b) under skimming, more visible products
depreciate faster. We extend our model to study the investments that producer might
make to separate the cohorts over time and thereby not dilute the intertemporal
fashion utility for the early adopters of the product. We find that a producer will be
willing to incur higher costs for cohort separation if the product visibility is higher but
would decrease this investment if the product is higher in intrinsic quality, all else
being equal. The results of our paper are of potential interest to manufacturers of
fashion goods for their pricing strategies as well as to the policy makers for studying
the welfare impact of identity-related goods that have often been derided for
providing very few intrinsic benefits.
3 - Estimating Dynamic Pricing Decisions in Markets with State
Dependent Demand
Koray Cosguner, PhD Student in Marketing, Washington University in
St. Louis, Olin Business School, Campus Box 1133, Saint Louis, MO,
63130, United States of America, cosgunerk@wustl.edu,
Tat Y. Chan, P. B. Seetharaman
We propose an empirical approach to examine the pricing behavior of manufacturers
in the presence of state dependent demand. To the extent that state dependence
characterizes the evolution of brands’ market shares, profit maximizing firms should
consider these inter-temporal linkages in demand and be forward-looking in their
pricing decisions. In this study, we estimate such a dynamic pricing model of firms.
First, we propose a demand model with state dependence and estimate it by using
household scanner panel data. This demand model is inputted into our fully
structural dynamic pricing model that we use to estimate the marginal costs of each
firm in the industry studied. We use approaches proposed by Berry and Pakes (2000)
and Bajari, Benkard and Levin (2007) to estimate this dynamic pricing model. Before
estimating the dynamic pricing model by using store level data, we perform a
simulation study by using Pakes and McGuire (1994) algorithm. This study allows us
to check the performance of Berry and Pakes (2000) and Bajari, Benkard and Levin
(2007) approaches in terms of recovering the assumed structural parameters of
interest. Then, we estimate the marginal costs of each firm in the cola product
category by using our proposed dynamic pricing model and compare our estimates
with the estimates from two benchmark cases: myopic (maximizing single period
profit) and static pricing models (demand without state dependence). Our framework
not only helps firms to understand the demand dynamics in the industry, but also
helps them to make their pricing decisions optimally. Furthermore, the proposed
framework can be used by new potential entrants, incumbent firms or regulators to
understand the cost structure in any oligopolistic market.
4 - Dynamic Targeted Pricing in B2B Settings
Jonathan Zhang, Assistant Professor of Marketing, University of
Washington, 547 Paccar Hall Box 353226, Seattle, WA, 98195,
United States of America, zaozao@uw.edu, Oded Netzer,
Asim Ansari
This research models the impact of firm pricing decisions on different facets of the
customer purchasing process in business-to-business (B2B) contexts and develops an
integrated framework for inter-temporal targeted pricing to maximize long-term
profitability for the firm. B2B pricing often allows considerable flexibility in
implementing first degree and inter-temporal price discrimination, and often involve
a request for a price quote providing the firm with an opportunity to better assess
price sensitivity and unfulfilled demand. The proposed model considers the buyer’s
quantity, timing and bid request and acceptance decisions in an integrated fashion
while accounting for customer heterogeneity, asymmetric reference price effects, and
short- and long-term purchase dynamics. We weave together hierarchical Bayesian
copulas with a non-homogeneous hidden Markov model to account for the interrelated
decisions and for long-term purchase dynamics. The results reveal that the
seller’s pricing decisions can have long-term impact on its buyers by shifting their
preferences between a “vigilant” and “relaxed” buying state, Furthermore, price losses
relative to a reference price not only have larger negative effects relative to gains on
buyers’ buying behavior, but buyers also take longer to adapt to losses. Additionally,
the proposed model exhibits superior predictive performance relative to benchmark
models. In a price policy simulation the proposed model results in a 52%
improvement in profitability, demonstrating the potential to employ value-based
pricing policies even in a traditional B2B industry characterized by cost-plus pricing
practices. Other policy simulations shed light on how B2B firm should price in the
recent economic environment with volatile commodity prices.
18
Thursday, 1:30pm - 3:00pm
■ TC01
Legends Ballroom I
Choice I: New Models of …
Contributed Session
Chair: Peter Stuettgen, Carnegie Mellon University, 5000 Forbes Ave.,
Pittsburgh, PA, 15213, United States of America,
pstuettg@andrew.cmu.edu
1 - Assessing Two Alternative Methods for Modelling Heterogeneity in
Stated Preference Data
Paul Wang, Senior Lecturer, University of Technology, Sydney,
Marketing Discipline Group, P.O. Box 123, Sydney, Australia,
Paul.Wang@uts.edu.au, Jordan Louviere, Kyuseop Kwak
Market segmentation has long been recognized as one of the key concepts in the
marketing discipline (e.g., Frank et al. 1972; Wedel and Kamakura 2000). It refers to
the process of classifying customers into homogeneous groups known as segments.
Stated choice experiment (aka choice-based conjoint analysis) offers a more powerful
method to obtain customer preference data than traditional rating method (Louviere
et al. 2000; Street et al. 2005). Stated choice models based on the random utility
framework are becoming increasingly popular in marketing and applied economics
literature (Louviere et al. 2000; Train 2009). The need to account for preference
heterogeneity in such models has motivated researchers to develop various ways to
solve the problem. Most commonly, researchers make a certain distributional
assumption for unknown heterogeneity across respondents and across choice tasks. If
a continuous distribution is assumed, mixed logit (Train 2009) or hierarchical
Bayesian approaches (Rossi et al. 2005) are used to approximate such unobserved
heterogeneity. If a discrete distribution is assumed, latent class model is mostly used
(Kamakura and Russell 1989) to identify latent market segments. The primary
purpose of this paper is to introduce two alternative methods for modeling preference
heterogeneity in stated choice data: (1) Cutler and Breiman’s (1994) archetypal
analysis method and (2) Kaufman and Rousseeuw’s (1990) fuzzy analysis clustering
method. We compare these less well-known ways of incorporating preference
heterogeneity with the traditional latent class modeling approach using health carerelated
choice data. We find the two alternative methods have considerable promise
for strategic market segmentation applications.
2 - A Direct Utility Model for Asymmetric Complements
Sanghak Lee, The Ohio State University, 2100 Neil Ave, Columbus,
43210, United States of America, lee_3121@fisher.osu.edu,
Greg Allenby, Jaehwan Kim
Asymmetric complements refer to goods where one is more dependent on the other,
yet consumers receive enhanced utility from consuming both. Examples include
garden hoses and sprinklers, chips and dip, and routine versus personalized services
where the former has a broader base for utility generation and the latter is more
dependent on the other’s presence. Measuring the presence of asymmetries is difficult
because it requires longitudinal variation of utility where the degree of interdependency
changes. We introduce a utility structure capable of identifying the origin
of demand variation, and investigate the presence of asymmetric complementarity
using scanner panel data of milk, cereal, ketchup, and yogurt purchases. Implications
are explored through counterfactual analyses involving price elasticities, spillover
effects and the influence of merchandising variables.
3 - Utility-based Model of Asymmetric Competitive Structure using
Store-level and Forced Switching Data
Paul Messinger, University of Alberta, 3-23 Business Building,
Edmonton, Canada, paul.messinger@ualberta.ca, Fang Wu
We propose a utility-based model of competitive structure that accounts for and
spatially represents both vertical (quality) and horizontal characteristics, where we
show how the vertical characteristics govern observed asymmetric substitution
patterns in the data. We find it desirable to estimate our model jointly with two kinds
of data, aggregate store-level sales data and forced switching survey (stated
preference) data. To facilitate estimation with such two kinds of data, we develop a
behavioral justification that establishes a conceptual linkage between these two kinds
of data. The intuition idea behind our modeling approach is that consumers’ actual
purchase behavior for brands gives us important market structure information
regarding brands’ proximities in some latent attribute space, and the same proximity
relationships across brands influence the consumers’ forced switching behavior. By
imposing a particular distribution on latent ideal points and willingness to pay
parameters, we are able to estimate heterogeneity in our proposed model.
4 - A Satisficing Choice Model
Peter Stuettgen, Carnegie Mellon University, 5000 Forbes Ave.,
Pittsburgh, PA, 15213, United States of America,
pstuettg@andrew.cmu.edu, Peter Boatwright, Robert Monroe
In addition to the standard compensatory choice models like logit and probit models,
a new stream of research has recently proposed several non-compensatory choice
models, most of them variants of the conjunctive choice rule (e.g., Gilbride and
Allenby 2004, Jedidi and Kohli 2005). We contribute to this new stream by
formulating a consumer choice model based on Simon’s (1955) idea of “satisficing”
decision making, in which alternatives are evaluated sequentially and the first
satisfactory alternative is chosen. This requires knowledge (or probabilistic modeling)