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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)
of the sequence of evaluation, a part of decision making that does not appear in previous models. We estimate our model using data from a conjoint-like experiment, in which the stimuli are pictorials of supermarket shelves and the participants’ gaze is recorded via eye-tracking equipment. Eye-tracking not only allows us to know the order of evaluation, but also to only include information into the decision making process that the decision maker was actually aware of (e.g., if the participant never looked at price, we know that price did not affect the decision). Finally, modeling the observed eye path and the decision making jointly lets us (1) differentiate between variable that affect attention vs. variables that affect choice and (2) explore interactions of how choice variables may affect search. ■ TC02 Legends Ballroom II Online Advertising - I Cluster: Internet and Interactive Marketing Invited Session Chair: Laura Kornish, Associate Professor, University of Colorado-Boulder, Boulder, CO, United States of America, laura.kornish@colorado.edu 1 - How Do Advertising Standards Affect Online Advertising? Avi Goldfarb, University of Toronto, Toronto, ON, Canada, avi.goldfarb@rotman.utoronto.ca The technological transformation and automation of delivery methods has revolutionized the advertising industry. However, increasing reliance on technology has also led to requirements for standardization of advertising formats. This paper examines how the memorability of banner advertising changed with the advent of new standards regularizing the size of display advertising. Using data from randomized field tests, we find evidence that for most ads, recall of banner advertising declines as a result of standardization. Because we find that the decline is much weaker when a standardized ad is the only ad on the page, and when the ads appear to be more original, a likely explanation is that standardization makes it harder for basic ads to distinguish themselves from their competition. 2 - Internet Display Advertising and Consumer Purchase Behavior: Do Ad Platforms Matter? Paul Hoban, PhD Student, Marketing Area, UCLA Anderson School, 110 Westwood Plaza, Los Angeles, CA, 90059, paul.hoban.2013@anderson.ucla.edu, Randolph Bucklin Total spending on Internet advertising is projected to surpass $100 billion by 2014, making it the second largest advertising medium behind TV. Display advertising (a.k.a. banner ads) is now an integral component of many online marketing campaigns. However, considerable uncertainty exists regarding the effects of online display media on consumer purchase behavior. This has made many managers reluctant to invest greater sums to date. Academic research on the effects of display advertising has also been limited, constrained by access to data and modeling challenges. Existing studies on display advertising have focused on brand affect, stated purchase intent, click through rates, site traffic, and purchase acceleration by existing customers. A limitation of most prior work is that all display ads are treated as equivalent. Among other things, this ignores the potential effect of ad platform. An ad platform determines where and how a display ad is served, how it may be targeted, and the cost to the advertiser. Using a unique dataset from a financial services firm and a continuous time hazard model, we examine the relationship between display ad impressions and individual purchase behavior while accounting for differences across advertising platforms. We do so for both potentially new and existing customers using a very large sample of long-lived tracking cookies for a twomonth period in 2010. Preliminary results show significant differences in ad effectiveness across platforms, despite identical ad copy. For advertisers, our results suggest that the costs associated with each platform can be weighed against effectiveness, leading to potentially more productive spending allocations. For ad platforms, there are implications for pricing and differentiation strategies. 3 - The Effect of Banner Exposures on Memory for Established Brands Titah Yudhistira, University of Groningen, Department of Marketing, Nettelbosje 2, Groningen, 9747AE, Netherlands, t.yudhistira@rug.nl, Eelko Huizingh, Tammo Bijmolt Although the focus of banner advertising has moved from click-through to brand building, there is little evidence of the effectiveness of banner advertising on brand memory for established brands. The purpose of our study is to provide new evidence on how banner exposures affect ad and brand memory as well as to propose and test a mechanism on how online exposures benefit established brands. Our study is unique since we (i) focus on established brands, (ii) emphasize the difference between ad memory and brand memory, and (iii) study the differential effects of three exposure variables, i.e frequency of exposures, time difference between the last exposure and measurement, and average time between exposures (spacing). We base our analyses on massive empirical data, i.e surveys and exposure data from 59.370 individuals in ten countries that were exposed to actual internet campaigns of 26 well-known brands. We find that exposure frequency and spacing have a beneficial effect and that time difference between the last exposure and measurement has a detrimental effect on ad memory. On the contrary, no effect of the three exposure variables on brand memory is found. However, the same effects of exposure frequency and time difference on brand memory are found by including mediating effects of ad recall and recognition. As a practical implication, this study shows that established brands can benefit from banner advertising by advertising more continuously, while assuring enough space between subsequent exposures. MARKETING SCIENCE CONFERENCE – 2011 TC03 19 4 - Website Ad Quantities: An Empirical Analysis of Traffic, Competition, and Business Model Laura Kornish, Associate Professor, University of Colorado-Boulder, Boulder, CO, United States of America, laura.kornish@colorado.edu, Jameson Watts In running a website, a firm balances two potential streams of revenue: sales of products, services, or information content to visitors; and sales of advertising space to other organizations offering their own promotional messages. Web-based businesses thus operate in “two-sided markets,” selling something of value to visitors and selling visitors’ attention. The question of how much advertising to sell affects both sides of the market – visitors may find the website less appealing with more advertising – and is therefore both complex and important. In this paper, we empirically test the competing theories about the determinants of ad quantities on websites. Recent theoretical literature, namely Katona and Sarvary (2008) [KS]; Godes, Ofek, and Sarvary (2009) [GOS]; and Kind, Nilssen, and Sorgard (2009) [KNS], have some contradictory predictions, and our goal is to resolve the debates through a data-driven analysis. We focus on three issues. Do sites that have more competition devote more space to advertising? GOS say no and KNS say perhaps yes. Do sites that have more traffic devote more space to advertising? KS say no, KNS say yes, and GOS say it depends. And how do the answers to those questions change depending on the business model of the site, i.e., whether it is an advertising-only business model vs. a hybrid of the two streams? We study these questions with a large sample of websites taken from the top million sites as ranked by quantcast.com. ■ TC03 Legends Ballroom III Internet: Social Influence Contributed Session Chair: Jun Yang, Assistant Professor, University of Houston Victoria, School of Business, 14000 University Blvd, Sugar Land, TX, 77479, United States of America, yangj@uhv.edu 1 - Successful Social Networkers: Impact of Activities and Network Positions Lucas Bremer, University of Kiel, Department of Marketing, Kiel, 24118, Germany, bremer@bwl.uni-kiel.de, Florian Stahl, Asim Ansari, Mark Heitmann The growth of online social networks and the decreasing effectiveness of traditional marketing have lead to a surge of interest in using such networks for marketing purposes. Actors embedded in such social networks are interested in understanding how to leverage the interactions and relationships among network members to increase their popularity and garner online success. In this paper we analyze how the network activities that an actor engages in influences the actor’s egocentric positions within the network and how such activities in combination with these network positions eventually impact marketing success. In particular, we study how a network of music artists can drive music downloads by engaging in active communication and building of friendship activities. Our sample consists of a set of 440 music artists who actively operate profiles on two independent online social network platforms at the same time. Personal network information on both platforms is tracked monthly over a period of one year. As network positions are endogeneous, and instrumental variables are not available readily, we use a latent instrumental variable approach to model the endogeneity of multiple network influence variables. Our results indicate that online success is determined by both the social network structure and networking activities of the music artists rather than by their outside popularity. Most importantly, the drivers of online success are not limited to the size of artist’s personal network. The findings of our study provide several insights into the use of personal online social networks for promoting products and services online. 2 - The Evolution of Switch Customers in E-Commerce: Understanding When and How Customers Switch Fan Zhang, Washington University in St.Louis, One Brookings Drive, Campus Box 1133, St.Loius, MO, 63130, United States of America, fanzhang@go.wustl.edu, Tat Chan, Qin Zhang With growing customer bases, many manufacturers start selling products directly to their customers, after initially only selling products through retail stores. This is particularly common in the context of online business. For manufacturers, selling through retailers has the trade-off between reaching more customers and receiving lower profit margins, particularly from the repeated customers. It is beneficial for manufacturers to know how they can utilize the existing customer base of the retailers to build its own customer base who shop directly from them. We propose a model to study this diffusion process. Particularly, we focus on the roles that the network effect and product assortments play on this process. We apply the proposed model to individual level purchase data from a Chinese online company which sells premium fresh fruit in its own online store after initially only selling the products through online retailers. We find that product assortment is a main factor for customers to switch from shopping at the online retailers to directly at the store of the manufacturer. We also find significant network effect in this diffusion process.
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