2012 INFORMS Marketing Science Conference June 7

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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.
■ TB06 Legends Ballroom VII Competition II: More on Measuring the Impact in Retail Markets Cluster: Special Sessions Invited Session Chair: A. Yesim Orhun, University of Chicago, Booth School of Business, 5807 Woodlawn Avenue, Chicago, IL, 60637, United States of America, yesim.orhun@chicagobooth.edu 1 - Entry with Social Planning Stephan Seiler, London School of Economics, Department of Economics, Houghton Street, London, WC2A 2AE, United Kingdom, s.seiler@lse.ac.uk, Pasquale Schiraldi, Howard Smith In 1996 a regulatory reform was introduced in the UK that made it more difficult to open large out-of-town supermarkets. The idea behind this planning regulation was to protect town centre vitality. In this paper we analyze the consequences for consumers and firms of alternative planning policies. We start by analyzing demand for the UK supermarket industry. The industry is characterized by stores that are differentiated along various dimensions: they offer different ranges of product and products of different quality, they vary in size etc. As most consumers regularly visit several supermarkets in the same week it is important to carefully model the interactions between different stores. To this end we propose a demand system in which we allow consumers to visit up to two stores in each week and do not impose a priori restrictions as to whether two different types of supermarkets are substitutes or complements. We use individual level data on store choice as well as expenditure to estimate the model. In the case of two-stop shopping we also use information on how weekly expenditure is split up between the two stores. In the estimation we carefully disentangle correlation in preferences from true complementarity. Finally we use the demand estimates in order to compute supermarket profits which are used in a model of store entry. We explicitly model the decision of the planning authority as we have data on planning applications (both accepted and rejected ones). We simulate a counterfactual in which we remove the asymmetric treatment of large and small stores in order to analyze the effect on store profits and consumer welfare. 2 - Sleeping with the “Frenemy”: The Agglomeration-differentiation Tradeoff in Spatial Location Choice Sumon Datta, Purdue University, Krannert School of Management, 403 W. State Street, West Lafayette, IN, 47907, United States of America, sdatta@purdue.edu, K. Sudhir A central tradeoff in location choice is the balance between agglomeration and differentiation. Should a firm co-locate (sleep) with a competitor to increase volume (competitor is a “friend” who can draw more customers to the location with agglomeration) or locate far away from a competitor in order to reduce competition (competitor is an “enemy” from who one should spatially differentiate)? Since observed co-location may be consistent with pure differentiation rationales such as (a) high demand at the location; (b) low cost at the location and (c) restrictive zoning regulations which allow entry in only small areas, it is challenging to disentangle the agglomeration- differentiation tradeoff from firms’ location choices. The paper develops a comprehensive structural model of entry and location choice that helps disentangle the agglomeration-differentiation tradeoff by decomposing profits into revenue and cost, and then further decomposing the revenue into its components of consumer choice based volume and competition based price. To capture zoning effects, we introduce a new approach to obtain zoning data, an approach that should be of general interest for a large stream of spatial location applications. Our results show that the agglomeration effect explains a significant fraction of observed colocation. Surprisingly, zoning has little direct effect on co-location. But tighter zoning restrictions interact with the agglomeration effect to explain a surprisingly large fraction of observed co-location. We find that strategic firms respond in complex and nonlinear ways to a small change in zoning which could cause a discontinuous impact on the observed location pattern. 3 - Does Reducing Spatial Differentiation Increase Product Differentiation? Effects of Zoning on Retail Entry and Format Variety K. Sudhir, Yale School of Management, Yale School of Management, New Haven, CT, United States of America, k.sudhir@yale.edu, Sumon Datta Zoning regulations limit the extent to which a firm can spatially differentiate. Even though the inability to spatially differentiate can lead to lower prices, a common conjecture is that zoning can be anti-competitive because fewer retailers will choose to enter tightly zoned markets. However, retailers also have the choice to profitably compete by choosing a higher level of format differentiation. Using estimates from a structural model of entry and location choice in the presence of zoning restrictions, we are able to perform counterfactuals to evaluate the relative impact of zoning on both the number of firms and format variety. We find that zoning impacts entry significantly more strongly when firms cannot differentiate on formats. Also, given the ability to differentiate on formats, for large ranges of zoning restrictions, retailers respond only with changes in the format mix rather than by reducing the number of firms. This implies that empirically, one may find weak linkages between zoning restrictions and entry, when retailers can differentiate on format. MARKETING SCIENCE CONFERENCE – 2011 TB07 13 ■ TB07 Founders I ASA Special Session on the <strong>Marketing</strong>-Statistics Interface - II Cluster: Special Sessions Invited Session Chair: Anindya Ghose, New York University, 44 W 4th Street, Suite 8-94, New York, NY, 10012, United States of America, aghose@stern.nyu.edu 1 - Assessing the Validity of Market Structure Analysis Derived from Text Mining Data Oded Netzer, Philip H. Geier Jr. Associate Professor, Columbia Business School, 3022 Broadway, New York, NY, 10027, United States of America, on2110@columbia.edu, Ronen Feldman, Moshe Fresko, Jacob Goldenberg Can one analyze the information posted by consumers on the Internet to allow managers to assess market structure? Web 2.0 provides gathering places for internet users in blogs, forums, and chat-rooms. These gathering places leave footprints in the form of colossal amounts of data. This type of information offers the firm an opportunity to “listen” to consumers in the market in general and to its own customers in particular. Our objective is to utilize the large-scale consumer generated data posted on the Web, in order to allow firms to understand consumers’ brand associative network and the implied market structure insights. We first text-mine the Web exploratory data and convert them into quantifiable perceptual association and similarity between brands and products. We use network analysis techniques to convert the text-mined data into a semantic network, which can in turn inform the firm, or the researcher, about the market structure and some meaningful relationships therein. We demonstrate this approach using two cases - sedan cars and diabetes drugs - generating market structure perceptual maps, without interviewing a single consumer. The proposed approach demonstrates high degree of internal validity. We examine the external validity of the proposed approach by comparing the market structure mined from the user-generated content to those obtain from traditional market structure approaches based on sales and survey data. The comparison to traditional market structure approaches provides strong support for the external validity of utilizing online conversations and the text mining approach to derive market structure. 2 - What Drives Me? A Novel Application of the Conjoint Adaptive Ranking Database System to Vehicle Consideration Set Formation using Population Statistics Ely Dahan, Princeton University, (Visiting), Princeton, NJ, United States of America, elydahan@gmail.com A new method of individual, internet-based adaptive choice-based conjoint analysis for vehicles points to a future of highly efficient questioning with a new purpose: helping customers understand themselves. Several novel approaches underpin this method: (1) the development of adaptive choice-based conjoint based on a predefined database of utilities, (2) development of a random utility generator utilizing population statistics that acts as a simulator of the actual market, including the ability to reproduce real market shares, (3) the use of actual products as conjoint stimuli, and (4) fine-tuning the tradeoff between allowing for respondent error versus enforcing consistent answers using computer assistance. 3 - How is the Mobile Internet Different? Search Costs and Local Activity Sangpil Han, New York University, New York, NY, United States of America, sangpil78@gmail.com, Avi Goldfarb, Anindya Ghose We explore how internet browsing behavior varies between mobile devices and personal computers. Smaller screen sizes on mobile devices increase the cost to the user of reading information. In addition, a wider range of locations for mobile internet usage suggests that the offline context can be particularly important. Using data on user behavior at a microblogging service (similar to Twitter), we exploit randomization in the ranking mechanism for user-generated microblog posts to identify a random experiment in the cost of reading information. Using a hierarchical Bayesian framework to better control for heterogeneity, our estimates show that search costs are higher on mobile devices compared to PCs. While links that appear at the top of a page are always more likely to be clicked, this effect is much stronger on mobile devices. We also find that the benefit of searching for geographically close matches is higher on mobile devices. Stores located in close proximity to a user are much more likely to be clicked on mobile devices than PCs. We discuss how these changes may affect market outcomes in local commerce and the implications for monetization of user-generated content in social media platforms.
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Session Index Thursday, 8:30am - 10
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2012 INFORMS Marketing Science Conference June 7

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