2012 INFORMS Marketing Science Conference June 7

More documents

Recommendations

Info

TB01 MARKETING SCIENCE CONFERENCE – 2011 Thursday, 10:30am - 12:00pm ■ TB01 Legends Ballroom I Marketing Science Institute II Cluster: Special Sessions Invited Session Chair: Don Lehmann, Columbia University, Columbia Business School, New York, NY, United States of America, drl2@columbia.edu 1 - Research on Branding: Issues and Outlook Jan-Benedict Steenkamp, C. Knox Massey Distinguished Professor of Marketing, University of North Carolina at Chapel Hill, Kenan-Flagler Business School, Chapel Hill, NC, 27599, United States of America, JBS@unc.edu Branding is clearly at the core of marketing. In fact, it is difficult to envisage marketing activities that do not directly or indirectly affect the firm’s brands. Therefore, it is not surprising that for the last quarter century, branding has been a research priority of the Marketing Science Institute, where the specific aspects of branding that were singled out for meriting special research attention reflected the dynamics in the marketplace. A milestone in branding research was the 1988 MSI working paper “Defining, Measuring, and Managing Brand Equity,” written by Lance Leuthesser. MSI-sponsored research on brand equity changed the way we think about brands, and was recognized with multiple awards. In the current list of research priorities, MSI calls for research on managing brands in a marketplace that is transformed by competitive threats emanating from (1) brands that originate in emerging markets, (2) store brands, and (3) economic downturns. In this presentation, I will do three things. First, I will provide a concise overview of past branding research, with special emphasis on MSI research priorities and publications. Second, I will provide a reflection on past research. What are some key learnings? What are strengths and limitations of previous research efforts? Going back in history is done too rarely in our field. To quote philosopher George Santayana: “Those who cannot remember the past are condemned to repeat it.” Finally, I will outline some issues in branding research that I believe are in urgent need of future research, and relate them to MSI’s current branding priorities. 2 - Research on Brands: Latest Findings and Future Opportunities Marc Fischer, Universitaat Passau, Passau, D-94030, Germany, Marc.Fischer@uni-passau.de A brand is an important asset for many firms that contributes to sustainable superior performance. Unsurprisingly, researchers from different schools of thought have been dealing with brand related questions for many years. Although the field has produced many papers and research reports, some more and some less influential, there is still vital interest in brand research. A number of questions with high practical relevance have not been answered, yet. This presentation summarizes the latest findings on brands and brand management. Because even the latest research findings are so numerous, the presentation focuses on those results with special relevance for practitioners. Particularly, the author covers findings that help explain why brands are important to customers, which is a prerequisite for gaining management relevance. In addition, he summarizes latest findings on the relevance of brands to firms and their stakeholders. The review includes important questions of how to integrate brands into the financial management and controlling system of firms. The presentation finishes with an outlook on future priorities in brand research. The author identifies several underresearched areas and questions that have not been anwered, yet. Again, the suggested agenda for future research focuses on topics that are not only interesting from an academic standpoint but also have practical relevance. 3 - The Evolution of Research on Marketing Metrics Dominique Hanssens, University of California-Los Angeles, 110 Westwood Plaza, Los Angeles, CA, 90095, United States of America, dominique.hanssens@anderson.ucla.edu “What you can measure, you can manage”, is a popular saying that applies particularly well to the marketing discipline and that has motivated the Marketing Science Institute to invest substantial research and dissemination resources in marketing metrics over the past few decades. By the 1950s, marketing had established an effective vocabulary, with root concepts such as segmentation, the four P’s and the product life cycle. It then naturally turned to the definition of relevant metrics to quantify these and other constructs. Among the early contributors was the PIMS initiative (e.g. Buzzell, Gale & Sultan 1975) which, among other things, highlighted the importance of market share, advertising/sales ratios, return to marketing investment and the like. Since then, a substantial number of new metrics have been advanced, covering marketing assets (such as brand equity), marketing activities (such as innovation activity) and marketing outcomes (such as sales revenue and firm value), see Lehmann and Reibstein (2005) for a review. That trend has arguably led to “metricflation”, with some publications listing as many as 103 key marketing metrics (Davis 2007). Perhaps as a defensive reaction to this proliferation, senior management has embraced “metric simplification”, all the way down to “the one number you need to know”, Net Promoter Score (Reichheld 2003). Where do we go from here ? In my view, we need to end up somewhere between “1” and “103”, and probably closer to the former. To do so, we will want to formulate key conditions for metrics to be declared strategically relevant. I propose three such critera. First, unlike finance, whose metrics are largely unidimensional (in the monetary 10 dimension), marketing metrics need to bridge the attitudinal domain and the financial domain. As an illustration, consumer preference for a brand is an attitudinal metric that ultimately needs to be translated in monetary terms. Second, I propose that metrics should be forward looking. For example, market share may be a useful metric of past achievement, but what does it mean for a brand’s expected future performance? Third, metrics need to be standardized, so that comparisons can be made across industries and across firms in any sector. I will review several recent research contributions that have made substantial progress in these directions. ■ TB02 Legends Ballroom II Empirical Modeling Contributed Session Chair: Eric Schwartz, The Wharton School, 3730 Walnut Street, JMHH 700, Philadelphia, PA, 19103, United States of America, ericschw@wharton.upenn.edu 1 - Modeling Online Visitation and Conversion Dynamics Chang Hee Park, Cornell University, Ithaca, NY 14853, United States of America, cp247@cornell.edu, Young-Hoon Park This paper develops a multi-event timing model that captures the lumpy shopping patterns of online customers and infers the formation of latent visit clusters constituting the arrival processes. Because the start and the end of each visit cluster are unobserved, we employ a changepoint modeling framework and statistically infer the cluster formation on the basis of customer visit patterns through data augmentation in Bayesian approach. Our model provides a set of novel inferences about the patterns underlying online shopping behavior, including (1) the number of visits constituting a visit cluster, (2) the arrival rate within a visit cluster, (3) the time length of a visit cluster, (4) the number of visit clusters in a given time period, and (5) the arrival rate between visit clusters, at the individual customer level. We apply our modeling framework to the data of customer visits and purchases at VictoriasSecret.com. The main results suggest strong empirical evidence of lumpy shopping patterns by online customers with significant heterogeneity in the extent of the lumpiness. As a result, the proposed model offers excellent fit and predictive performance. By linking customer visits to purchases, we find that on average a visit cluster with purchase conversions contains more visits than one without purchase, the likelihood of purchase conversions is greatest for a customer’s second arrival in a visit cluster, and a considerable portion of purchase conversions occur in later visits in clusters. We also demonstrate how our cluster-based inferences can be utilized for attribution management in online marketing. 2 - Speed of Product Updates in Online Games Paulo Albuquerque, Assistant Professor, University of Rochester, Simon Graduate Business School, Carol Simon Hall 3-110 R, Rochester, NY, 14627, paulo.albuquerque@Simon.Rochester.edu This paper proposes an empirical model studying the managerial decision regarding the speed of product innovation in the online computer gaming industry. The demand for game content is modeled as a function of consumer forward looking expectations and response to new content, as well as their previous participation in the game. The launch of new content involves a trade-off. Consumers value content innovation, which leads to more participation, but faster product updates increase firm costs and make old content obsolete, which may lead to lower product usage as well. We use individual-level data collected online from the popular computer game “World of Warcraft” to empirically test our model, and provide recommendations about the scheduling of product updates. 3 - Estimating Preferences from Configured Choice Sanjog Misra, William E. Simon School of Business, University of Rochester, Rochester, NY, 14627, United States of America, sanjog.misra@simon.rochester.edu The traditional choice framework imagines consumers picking from a small set of well defined alternatives. The current online choice environment, on the other hand, allows consumers to be active participants in both the supply and demand aspects of choice by giving them configuration tools to help them design their own optimal products. In this paper I describe, model and estimate preferences from data that is generated by product configuration tools. The paper generalizes exiting methods and approaches by explicitly modeling interactions across design elements and allowing for consumer heterogeneity in them. A novel Bayesian econometric algorithm is presented that allows estimation under configured choice regimes that may often be characterized by intractable likelihoods. I demonstrate the use of this methodology first using simple (toy) examples and then with real data. The paper concludes with a discussion of extensions and other potential applications. 4 - Test and Learn: A Reinforcement Learning Perspective Eric Schwartz, The Wharton School, 3730 Walnut Street, JMHH 700, Philadelphia, PA, 19103, United States of America, ericschw@wharton.upenn.edu Firms run experiments to test and learn about marketing actions. However, running many so-called A/B or multivariate tests can be slow and costly, and the results from one test do not give necessarily inform what the next test should be. We frame this as an optimization problem, known as the multi-armed bandit problem, involving the tradeoff between exploring uncertain actions and exploiting what has been learned so far. The problem occurs in a range of domains from personalized website design to targeted direct marketing. For instance, in customer relationship management,
marketers may predict future customer value, but which actions they should select for which customers and in which contexts or channels? To provide new insight on this problem, we use a model-based algorithm to determine an optimal policy of how to run a sequence of tests to learn about the effectiveness of those actions in the most cost-efficient way. To do this, we introduce the reinforcement learning framework to the marketing literature. This approach generalizes methods commonly used in marketing to solve dynamic optimization problems. Unlike prior methods, our approach accommodates a selection among many different actions. By considering the similarity of those actions, the firm may learn about the effectiveness of action without before ever testing it. As one example, we investigate firms with a customerbase engaging in activity for free with the goal of converting some of them into paying customers. Finally, we discuss the design of field experiments for implementation of the optimal sequential marketing tests. ■ TB03 Legends Ballroom III Social Networks and Profitability Cluster: Special Sessions Invited Session Chair: Michael Haenlein, Associate Professor, ESCP Europe, Paris, France, mhaenlein@escpeurope.eu Co-Chair: Barak Libai, Leon Recanati Graduate School of Business Administration, Tel Aviv, Israel, libai@post.tau.ac.il 1 - How Customer Word of Mouth Affects the Benefits of New Product Exclusivity to Distributors Christophe Van den Bulte, Associate Professor, University of Pennsylvania, Philadelphia, PA, United States of America, vdbulte@wharton.upenn.edu, Renana Peres Marketing executives often face the decision whether and for how long to grant exclusivity to distributors of their new products. Using an agent based model, we assess how word of mouth among customers and the competition among structurally equivalent distributors with partially-overlapping customer bases influence the profitability of granting exclusivity for new products. Our results show that the presence of communication spillovers among customers of different distributors can make exclusivity undesirable for the distributors and the industry overall. This reversal of the conventional wisdom occurs because, though exclusivity protects the favored distributor from market share losses to competitors, it also precludes him from benefiting from the positive word of mouth generated by customers of other distributors. The effect is magnified by both the level of cross-distributor communication among customers and the level of structural equivalence among distributors. The forces at work and the result we obtain apply not only to exclusivity in distribution but also to selling to original equipment manufacturers (OEMs). 2 - Evolving Viral Marketing Strategies William Rand, University of Maryland, College Park, MD, United States of America, wrand@umd.edu, Forrest Stonedahl Viral marketing is based on the idea that consumer discussions about a product are more powerful than traditional advertising. However, who to seed in a viral marketing campaign in order to maximize profit based on the amount and rate of product adoption is not obvious. Given an arbitrary network and a limited seeding budget choosing the optimal seeding strategy has been shown to be computationally intractable (NP-Hard). Furthermore, it is not clear what the proper seeding budget should be for a particular network since additional seeds cost more but also encourage more rapid adoption, and thus can directly affect the profitability of the campaign. In order to address these problems, we define a strategy space for consumer seeding based upon network characteristics. We measure strategy effectiveness by simulating adoption using a Bass-like agent-based model. We examine six different social network structures: four classic theoretical models (random, lattice, small-world, and preferential attachment) and two empirical datasets (extracted from Twitter friendship data and an alumni social networking website). We use an evolutionary algorithm to simultaneously optimize the seeding strategy and budget. Our results show that a simple strategy (ranking by node degree) is near-optimal for the four theoretical networks, but that a more nuanced strategy performs significantly better on the empirical networks. Moreover, we find that the exact same strategy appears to work well on different empirical networks even when collected from very different sources. Finally, when we look at all of the networks together, we find a correlation between the optimal seeding budget for a network (the number of individuals to seed), and the inequality of the degree distribution. 3 - Determinants of Social Influence on Adoption in Customer Ego Networks Hans Risselada, University of Groningen, Groningen, Netherlands, h.risselada@rug.nl, P.C. (Peter) Verhoef, Tammo Bijmolt Traditionally, customer value management is concerned with individual customers analyzed in isolation. The recently introduced concept of customer engagement behavior broadens the scope of customer value management by capturing post transaction behavior of a customer that potentially influences the behavior of others in his/her ego network. However, in the literature little is known about the MARKETING SCIENCE CONFERENCE – 2011 TB04 11 determinants of actual social influence that is exerted over others. In this study we analyze determinants of social influence on adoption within the ego network of a customer while we control for traditional variables that are known to affect adoption behavior. We use three sources of data: 1) communication-based network data to determine the ego network of adopters of a mobile phone service, 2) personality traits and customer-firm relationship data of those adopters, obtained by an online survey, and 3) customer and customer-firm relationship data of those in the ego networks. We estimate a multilevel hazard model for the adoption behavior of the individuals in the ego networks of the initial adopters. This allows us to analyze the social influence that an initial adopter exerts over the others in his/her ego network and by which factors this influence is determined. We contribute to the customer management literature in two ways. First, we are among the first that empirically investigate determinants of actual social influence on adoption within a customer’s ego network; characteristics of the initial adopter, characteristics of the customer-firm relationship, and the attitude towards the service. Second, by showing that customers influence others in their ego network we illustrate that customer value management is truly enriched by a network oriented concept like customer engagement. 4 - Customer Acquisition in a Connected World: Revenue vs. Opinion Leaders Michael Haenlein, Associate Professor, ESCP Europe, Paris, France, mhaenlein@escpeurope.eu, Barak Libai A fundamental principle of informed customer acquisition is that firms should give priority to attracting customers that will supply the most value. In doing so, companies face a fundamental dilemma: On the one hand, firms today have an increasing ability to assess the lifetime value of their customers and to understand how it is distributed. This information can be used to assess which are the best potential customers to acquire and to focus on potential clients with high expected customer lifetime value (revenue leaders). On the other hand, customers provide the firm value not only through what they buy, but also in the way they affect others via social influence such as word of mouth. Firms might therefore be well advised to attract clients with a high number of social connections (opinion leaders), which have been shown to exert a disproportional effect on others. While the acquisition of revenue leaders results in higher direct value, the acquisition of opinion leaders leads to higher social value. Our study analyzes the tradeoff between focusing on the acquisition of higher lifetime value customers (revenue leaders) and higher social influence customers (opinion leaders). Using an agent-based model, we highlight the complexity of this trade off, esp. in situations where both sources of value are not independent, and show under which conditions focusing on revenue leaders can lead to higher value than focusing on opinion leaders. ■ TB04 Legends Ballroom V Bayesian Econometrics II: Methods & Application Contributed Session Chair: Sudhir Voleti, Assistant Professor, Indian School of Business (ISB), 2118, AC2 L1, ISB Campus, Gachibowli, Hyderabad, AP, 50032, India, sudhir_voleti@isb.edu 1 - Simultaneous Scaling of Multiple Domains: Application to Country-of-origin Effects in Asia Luming Wang, University of Manitoba, Asper School of Business, Winnipeg, Canada, wang4@cc.umanitoba.ca, Giana Eckhardt, Terry Elrod Recent years have seen a proliferation of applications of market structure analysis, especially studies for inferring market structure from consumer preference and choice data. The analyst infers brand positions in an (intangible) attribute space from the data, given a market in which consumers have heterogeneous tastes for these attributes. Two critical issues have drawn the authors’ attention. First, most market structure analyses are two-mode (i.e., brands and consumers) on existing brands within a single product category. However, brands often are extended beyond their original categories to reduce the cost and risk of entering a new product category. Therefore, a macro-view market structure analysis across product categories on both served and unserved markets may provide more insight into the cross-category competition situation and suggest further strategic moves. Second, the interpretation of map dimensions is a two-step approach (i.e., generating dimensions first and then labeling them) with some limitations in term of data usage efficiency and measurement error management. An integrated method is preferable. The current research presents a probabilistic spatial model on partially overlapped domains (a) to provide a flexible approach to dealing with more complex market structures, (b) to both examine the served and explore the unserved marketplaces, and (c) to make the choice map self-explainable (by simultaneously scaling spaces with possibly different natures). The authors demonstrate the proposed method using country of origin as an application area due to its complexity in term of modes involved (e.g., consumer, country, product) and interrelated domains (e.g., the preference map and the perception map).
Page 1 and 2: 2011 33 RD ANNUAL INFORMS MARKETING
Page 4 and 5: ii 33 rd AnnuAl MArketing Science c
Page 6 and 7: INFORMS MaRkeTINg ScIeNce cONFeReNc
Page 8 and 9: ReSTauRaNTS IN HOuSTON (continued)
Page 10 and 11: ReSTauRaNTS IN HOuSTON (continued)
Page 12 and 13: SIgHTSeeINg IN aNd aROuNd HOuSTON x
Page 14 and 15: SuMMaRy ScHedule xii 33 rd AnnuAl M
Page 16 and 17: INTeRcONTINeNTal HOTel & cONveNTION
Page 18 and 19: TA09 - Founders III Promotions I Ch
Page 20 and 21: TB09 - Founders III Promotions II C
Page 22 and 23: TC09 - Founders III Promotions III
Page 24 and 25: TD09 - Founders III Retailing I: Ge
Page 26 and 27: FA09 - Founders III Retailing II: G
Page 28 and 29: FB09 - Founders III Retailing III:
Page 30 and 31: FC09 - Founders III Retailing IV: C
Page 32 and 33: FD09 - Founders III Retailing V: Lo
Page 34 and 35: SA09 - Founders III Retailing VI: A
Page 36 and 37: SB09 - Founders III International M
Page 38 and 39: SC09 - Founders III International M
Page 40 and 41: TA02 ■ TA02 Legends Ballroom II G
Page 42 and 43: TA06 MARKETING SCIENCE CONFERENCE -
Page 44 and 45: TA10 2 - Stars, Leaders, Free-rider
Page 46 and 47: TA13 MARKETING SCIENCE CONFERENCE -
Page 50 and 51: TB05 MARKETING SCIENCE CONFERENCE -
Page 52 and 53: TB08 4 - Evaluating Financial Risk
Page 54 and 55: TB11 ■ TB11 Champions Center I Re
Page 56 and 57: TC01 MARKETING SCIENCE CONFERENCE -
Page 58 and 59: TC04 3 - Is it a Fad or Necessity?
Page 60 and 61: TC08 3 - Optimal Mailing in a Beta-
Page 62 and 63: TC11 MARKETING SCIENCE CONFERENCE -
Page 64 and 65: TC14 ■ TC14 Champions Center VI C
Page 66 and 67: TD03 MARKETING SCIENCE CONFERENCE -
Page 74 and 75: FA02 ■ FA02 Legends Ballroom II U
Page 76 and 77: FA06 2 - Investigating the Relation
Page 78 and 79: FA10 MARKETING SCIENCE CONFERENCE -
Page 80 and 81: FA13 4 - Modeling the Impact of Vis
Page 82 and 83: FB02 MARKETING SCIENCE CONFERENCE -
Page 90 and 91: FB15 4 - Price Pressure and Supplie
Page 92 and 93: FC03 MARKETING SCIENCE CONFERENCE -
Page 94 and 95: FC07 4 - Price Competition in the S
Page 96 and 97: FC10 ■ FC10 Founders IV Segmentat
Page 98 and 99:
FC14 MARKETING SCIENCE CONFERENCE -
Page 100 and 101:
FD02 ■ FD02 Legends Ballroom II U
Page 102 and 103:
FD05 ■ FD05 Legends Ballroom VI G
Page 104 and 105:
FD09 MARKETING SCIENCE CONFERENCE -
Page 106 and 107:
FD12 MARKETING SCIENCE CONFERENCE -
Page 108 and 109:
FD15 ■ FD15 Champions Center V CR
Page 110 and 111:
SA04 MARKETING SCIENCE CONFERENCE -
Page 112 and 113:
SA07 ■ SA07 Founders I Entertainm
Page 114 and 115:
Page 116 and 117:
Page 118 and 119:
SB03 MARKETING SCIENCE CONFERENCE -
Page 120 and 121:
SB06 ■ SB06 Legends Ballroom VII
Page 122 and 123:
SB09 ■ SB09 Founders III Internat
Page 124 and 125:
SB14 MARKETING SCIENCE CONFERENCE -
Page 126 and 127:
SC03 3 - Does Higher Transparency L
Page 128 and 129:
SC09 2 - Buy-now Prices at Entertai
Page 130 and 131:
SC14 2 - Analyzing the Dynamics of
Page 132 and 133:
Author Index A Abou Nabout, Nadia F
Page 134 and 135:
Lee, So Young TD15 Leeflang, Peter
Page 136 and 137:
Session Index Thursday, 8:30am - 10
Page 138 and 139:
S A T U R D A Y Auctions and Consum
show all

2012 INFORMS Marketing Science Conference June 7

Create successful ePaper yourself

Delete template?

Save as template?