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QUANTITATIVE MARKETING ABSTRACTS"Brand History, Geography, and the Persistence of Brand Shares"
BART J. BRONNENBERG, CentER, Tilburg University We study persistence in the geographic variation in market shares of branded goods in consumer packaged goods industries across 50 U.S. city-markets. We match scanner data on local market shares and survey data on local quality perceptions for the largest brands in 34 consumer packaged goods industries. These data are then matched with historic information on the year and US city-market in which each brand was first launched. We find that these consumer brands have persistently higher market shares in markets closest to their respective cities-of-origin than in markets farthest from their respective cities-of-origin, where they were typically launched later. For 6 of the 34 industries, we collected more complete historic entry data with which we can determine the local order of entry among the top brands in each of the 50 U.S. city-markets. We find a persistent effect from differences in the order-of-entry of competing brands on their current relative brand shares and quality perceptions across US cities. The historic order of entry also appears to correlate with the current rank-order of brand shares across cities, leading to large asymmetries across markets in brand shares and in quality perceptions. This persistence is particularly striking since many of the brands studied herein originated during the mid-to-late 19th and early 20th centuries, roughly a century prior to the sample period of the market share and quality perception data. "Taste Heterogeneity, IIA, and the Similarity Critique"
THOMAS J. STEENBURGH, Harvard Business School
The purpose of this paper is to show that allowing for taste heterogeneity does not address the similarity critique of discrete-choice models. Although IIA may technically be broken in aggregate, the mixed logit model allows neither a given individual nor the population as a whole to behave with perfect substitution when facing perfect substitutes. Thus, the mixed logit model implies that individuals behave inconsistently across choice sets.
MICHAEL BRAUN, MIT Sloan School of Management Dyadic data is generated by observed interactions between any two individuals in a population. Examples include records of telephone calls and incidents of online music sharing. The interest for marketers in this type of data is in understanding the patterns of how customers interact with one another, and how to predict future interactions. For dyadic data with $N$ individuals, modeling who contacts whom, and how often, involves computing $\binom{N}{2}$ likelihoods. Without making rather restrictive assumptions (such as homogeneity of latent customer types), this problem becomes intractible for all but the smallest datasets. We present a framework for modeling dyadic data that manages this scalability problem, while accounting for interdependent variation of unobserved customer traits. By exploiting the discreteness of the Dirichlet process (which we use as a nonparametric Bayesian prior on individual characteristics), we dramatically reduce the number of likelihood computations at each iteration of an MCMC algorithm. We demonstrate the need for, and effectiveness of, our model using a dataset of call records from a major Chinese cell phone service provider. | |||||||||||
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Solicitation of Abstracts
This Journal publishes abstracts of working papers as well as papers accepted for publication in various areas of quantitative marketing, including analytic studies of marketing phenomena and problems, decision support systems, and/or investigations of methodological issues. In general this journal will include papers that would be appropriate for the Marketing Science or Quantitative Marketing & Economics or the methodological/analytical research in Journal of Marketing Research. Their focal issue would be normative or methodological. The primary contribution of the research would be either the presentation of a new method, an analytical model of a marketing phenomena, or a tool to assist managerial decision making.
Distribution ServicesIf your Institution is interested in learning more about increasing readership for its research by becoming a Partner in Publishing or starting a Research Paper Series, please email: Management@SSRN.com. Distributed by:DirectorsMKTG SUBJECT MATTER EJOURNALS ALVIN J. SILK Please contact us at the above addresses with your comments, questions or suggestions for MKTG-Sub. Advisory BoardQuantitative Marketing HUBERT GATIGNON DIPAK C. JAIN WAGNER A. KAMAKURA GILLES LAURENT GARY L. LILIEN JOHN D.C. LITTLE JAGMOHAN S. RAJU RAM C. RAO BRIAN T. RATCHFORD JOHN ROBERTS PETER E. ROSSI SUBRATA K. SEN STEVEN M. SHUGAN RICHARD STAELIN WILFRIED R. VANHONACKER
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