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Washington University in St Louis Olin Business School

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Taylor Bentley, PhD, Marketing 2015

Research Interests:
Substantive: Online Advertising, Digital Media Advertising, Social Media, Sales Force Management, New Product Design, Healthcare.

Methodological: Dynamic Structural Models, Empirical I.O., Search Models, Choice Models.

Job Market Paper Title: “The Value of Informational Organic Links for Sponsored Search Advertising” with Tat Y. Chan and Young-Hoon Park

Abstract: This paper studies the impact of informational organic keyword search results on the performance of sponsored search advertising. We show that, even though advertisers can target consumers who have specific needs and preferences, for many consumers this is not a sufficient condition for search advertising to work. By allowing consumers to access content that satisfies their information requirements, informational organic results can reduce consumers’ uncertainty and subsequently increase their interest for advertisers’ offerings. We estimate a dynamic model of consumer search and learning using a unique dataset of search advertising in which commercial websites are restricted in the organic listing, allowing us to identify consumer clicks as informational (from organic links) or purchase oriented (from sponsored links). With the estimation results, we show that consumer welfare is improved by 29%, while advertisers generate 19% more sales, and search engines obtain 18% more paid clicks, as compared to the scenario without informational links. We conduct counterfactuals and find that consumers, advertisers, and the search engine are significantly better off when the search engine provides “free” information about the keyword. When the search engine provides information on advertisers, however, there are fewer paid clicks and advertisers at high ad positions will obtain lower sales. We further investigate the implications on the equilibrium advertiser bidding strategy. Results show that advertiser bids will remain constant in the former scenario. When the search engine provides advertiser information, advertisers will increase their bids because of the increased conversion rate; however, the search engine still loses revenue due to the decreased paid clicks. The findings shed important managerial insights on how to improve the effectiveness of search advertising.

Other Completed Papers (Under Review)
Title: “Testing Signaling Theory Using Data on Search Advertisements” with Tat Chan and Young-Hoon Park

Abstract: Using a dataset of travel-related keywords which is obtained from a search engine, we test to what extent consumers are searching and advertisers are bidding in accordance to the signaling theory of advertising. We find that consumers are more likely to not only click on an advertiser listed at higher positions but also choose such link as the last one to click in their search. On the advertiser side, we find that advertisers’ decisions of increasing bid amounts to acquire higher positions are positively correlated with their improved match value for consumers, and that firms with higher match value typically obtain higher positions. Our findings support predictions from the signaling theory of advertising in the literature. We then tested and ruled out several alterative explanations for the results. We also find through an extension that consumers with high search costs are more likely to rely on the signal and advertisers can generate clicks when competing against advertisers with higher match value, due to an informational externality.

Title:
“Solving the Similarity and Dominance Problems: The Elimination-By-Aspects (EBA) Demand Model for Differentiated Products” with P.B. Seethu Seetharaman

Abstract: This paper shows that the mixed logit model, which is widely believed to be a highly flexible characterization of brand switching behavior, is not well designed to handle non-IIA substitution patterns. The probit allows only for pair-wise inter-brand similarities, and ignores third-order or higher dependencies. In the presence of similarity and dominance effects, the mixed logit model and the probit model yield systematically distorted substitution patterns and thus distorted marketing mix elasticities. The authors propose a more flexible demand model that is an extension of the elimination-by-aspects (EBA) model (Tversky 1972a, 1972b) to handle marketing variables. The model vastly expands the domain of applicability of the EBA model to aggregate scanner data. Using an analytical closed-form that nests the traditional logit model as a special case, the EBA demand model is estimated with marketing variables from aggregate scanner data in 9 different product categories. It is compared to the mixed logit and probit models on the same datasets. In terms of multiple fit and predictive metrics (LL, BIC, MSE, MAD), the EBA model outperforms the mixed logit and the probit in a majority of categories in terms of both in-sample fit and holdout predictions. The results show significant differences in the estimated price elasticity matrices between the EBA model and the comparison models. In addition, a simulation shows that the retailer can improve gross profits up to 34.4% from pricing based on the EBA model rather than the mixed logit model.

Faculty Advisors:

Previous Employment:
  • Consultant, Essence Healthcare (2011)
  • Movie Trailer Producer - The Cimarron Group, Hollywood, CA, 2006-2007
Education:
  • BA Mathematics, 2005, Principia College
  • MBA, 2010, Washington University
Hometown: Los Angeles, CA, USA





Curriculum Vitae | Email