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

TB05 MARKETING SCIENCE CONFERENCE – 2011
2 - A Dynamic Spatial Hierarchical Model of Theater Level
Box-office Performance
Shyam Gopinath, Doctoral Student, Kellogg School of Management,
2001 Sheridan Road, Evanston, IL,
United States of America, s-gopinath@kellogg.northwestern.edu,
Pradeep Chintagunta, Hedibert Lopes, Sriram Venkataraman
Most of the prior research on the movie industry has focused on the box office
performance at the movie level. The goal of this research is to investigate the drivers
of theater level box-office performance. One key challenge for theaters is the
competition faced by other theaters in the same region. We provide a novel modeling
framework that takes into account this spatial relationship. Moreover, we allow this
relationship to change over time. The first key objective of this research is to compare
the relative importance of the different box-office drivers. In addition, we investigate
whether there is seasonality in the impacts i.e., are some factors more important than
others during certain times of the year like holidays (e.g., summer and Christmas).
The second key objective is to develop a forecasting model and compare it with
several benchmark models.
3 - Attribute-level Heterogeneity
Peter Ebbes, The Ohio State University, Fisher College of Business,
2100 Neil Ave, Columbus, OH, 43210, United States of America,
ebbes.1@osu.edu, John Liechty, Rajdeep Grewal, Matthew Tibbits
This research studies finite mixture specifications for modeling consumer
heterogeneity where each regression coefficient has its own finite mixture, that is, an
attribute finite mixture model. This attribute specification has two advantages over
traditional vector finite mixture specifications, in which the finite mixture applies to
the whole vector of regression coefficients. First, as our results demonstrate, the
attribute finite mixture model can identify heterogeneity structures using less data.
Second, the attribute model enables managers to get a better understanding of highly
complex market structures. By allowing for different numbers of support points per
covariate, the proposed method easily uncovers complex market structures in
simulated and real data. We use recent advances in reversible jump Markov Chain
Monte Carlo (MCMC) to estimate the parameters for the attribute-based finite
mixture model. The model allows for a different number of components per attribute,
so the possible total number of vector components across all attributes may become
quite large. Furthermore, in practice, we do not know a priori how many support
points each attribute level has. We solve this computational problem by placing a
prior on the space of the attribute-based models, assuming that the number of
components per attribute level is a discrete random variable. The Reversible Jump
MCMC algorithm moves around this variable dimension’s model space and
simultaneously estimates the posterior distributions for the number of components
and the mixture components, conditional on the number of components. It thus
provides an established framework for model choice.
4 - A Nonparametric Model of Attribute Based
Inter-product Competition
Sudhir Voleti, Assistant Professor, Indian School of Business (ISB),
2118, AC2 L1, ISB Campus, Gachibowli, Hyderabad, AP, 50032, India,
sudhir_voleti@isb.edu, Pulak Ghosh, Praveen Kopalle
We propose an approach to assess attribute based inter-product competition that
employs a novel nonparametric statistical method, the nested Dirichlet Process (nDP),
to effect a hierarchical clustering of our units of analysis – brands and SKUs. Our
method exploits the information content in both the observed and the latent
attributes in the data to induce a pattern of dependence that groups ‘similar’ (and
hence, more substitutable) brands into similar-brand clusters and, simultaneously,
groups similar SKUs within the similar-brand clusters into similar-SKU clusters.
Subsequently, linear programming methods yield attribute weights that best
discriminate between each pair of similar-unit clusters. These attribute weights feed
into a comprehensive competition measure that easily finds place in most generalized
linear models of sales or market response. We then demonstrate the utility of the
proposed competition measure by applying it in a category-profit maximization
problem. We find that the competition measure proposed improves fit, explained
variance, prediction and managerial insight in the application context. Our approach
bears several advantages over extant methods - it is parsimonious, flexible, avoids
distributional assumptions on model terms, avoids the model selection problem by
endogenously determining the appropriate number of similarity clusters, allows
inference on the clusters obtained, is able to accommodate restrictions defined a
priori based on category structure, accommodates asymmetric competitive effects
between pairs of products, and yields realistic cross-product substitution effects,
intuitive marginal effects as well as own- and cross-attribute elasticities.
12
■ TB05
Legends Ballroom VI
New Product II: Diffusion
Contributed Session
Chair: Li Zheng, ESSEC, 7, Rue Du General Henrys, Esc. 6;
Apt. 67, Paris, 75017, France, li.zheng@essec.edu
1 - Modeling Seasonality in New Product Diffusion
Yuri Peers, Erasmus University Rotterdam, Erasmus School of
Economics, P.O. Box 1738, Room H11-26, Rotterdam, 3000DR,
Netherlands, ypeers@ese.eur.nl, Philip Hans Franses, Dennis Fok
The authors propose a method to include seasonality in any diffusion model that has
a closed-form solution. The resulting diffusion model captures seasonality in a way
that naturally matches the overall S-shaped pattern. The method assumes that
additional sales at seasonal peaks are drawn from previous or future periods. This
implies that the seasonal pattern does not influence the underlying diffusion pattern.
The model is compared with alternative approaches through simulations and
empirical examples. As alternatives we consider the standard Generalized Bass Model
[GBM] and the basic Bass model, which ignores seasonality. One of our main findings
is that modeling seasonality in a GBM generates good predictions, but gives biased
estimates. In particular, the market potential parameter is underestimated. Ignoring
seasonality, in cases where data of the entire diffusion period is available, gives
unbiased parameter estimates in most relevant scenarios. However, when only part of
the diffusion period is available, estimates and predictions become biased. We
demonstrate that our model gives correct estimates and predictions even if the full
diffusion process is not yet available.
2 - Empirical Test of the Bass Diffusion Model using Exogenous Shocks
on Word of Mouth Effect
Sungjoon Nam, Assistant Professor, Rutgers University,
1 Washington St., #992, Newark, NJ, 07102,
United States of America, sjnam@business.rutgers.edu
This paper empirically tests whether the Bass Diffusion Model really captures the
communication structure by using a video-on-demand service adoption data.
Previous attempts to identify the communication structure suffer from aggregation
errors and endogeneity biases caused by the “correlated” errors. In contrast, the new
data features enable us identify the different communication structure of a diffusion
process using micro level diffusion data with exogenous shocks on word of mouth
effects. The data has objective measures on exogenously determined signal quality
that influences the number of new movies updated. Since the signal quality is not
observed for new adopters but only could be inferred from their neighbors, it only
influences the rate of imitation. In contrary to BDM’s assumptions, the estimated
results show that exogenous shocks on word of mouth effect affect the coefficient of
innovation, not the coefficient of imitation. The Bass model’ ability to capture the
communication structure is not supported at the micro level adoption of video on
demand service. Consequently we should be very careful to interpret these
parameters in BDM to represent the communication structure in new product
diffusion.
3 - Spatiotemporal Analysis of New Product Diffusion
Li Zheng, ESSEC, 7, Rue Du General Henrys, Esc. 6; Apt. 67,
Paris, 75017, France, li.zheng@essec.edu
The classical diffusion model implies two key assumptions: 1) all members of a given
population equally affect and are affected by one another; 2) the potential influence
of prior adoption remains constant through time from the beginning of their
occurrence. This article will challenge these two key assumptions, and attempt to
develop a spatiotemporal hierarchical model of diffusion that allows heterogeneity
both within the population and over time. In the space dimension, spatial
heterogeneity and spatial dependence play a role in innovation diffusion. Captured by
space-specific covariates, the spatial heterogeneity refers to the aspects that influence
the marginal diffusion speed across spaces. Several kinds of regional characteristics
such as economy, demography and telecommunication infrastructure are modeled as
drivers of diffusion growth. The spatial dependence refers to the spillover influence of
region j on region i, which connects the current adopters in each region to the past
adopters in related regions (i.e., from the ‘influence set’). Several kinds of proximity
such as geographic, transportation, technological and demographic similarity help in
defining such an ‘influence set’. In the time dimension, the power of earlier adopters’
influence on the potential adopters decays over time. The intuition behind the model
structure is that not all the adopters have influence on the potential adopters’
decision-making, and such impact may vary across space and over time.