Song Yao
Associate Professor of Marketing at Olin Business School
Biography
Olin Business School
Song Yao is an Associate Professor of Marketing at the Olin Business School, Washington University in St. Louis. His current research interests lie at the intersection of marketing analytics and empirical microeconomics, focusing on pricing, advertising, new technology, and social media. Professor Yao has won the American Marketing Association’s Paul Green Best Paper Award and the John Howard Dissertation Award. He was the runner-up for Dick Wittink Best Paper Award in 2018, the finalist for INFORMS Long Term Impact Award in 2017 and 2019, the Frank Bass Outstanding Dissertation Award in 2011 and 2012, the John Little Best Paper Award in 2009 and 2011. He has also been selected by the Marketing Science Institute (MSI) as one of the MSI Young Scholars of 2017. He serves on the Editorial Boards of the Journal of Marketing Research, Marketing Science, and Quantitative Marketing and Economics. He is also the Associate Editor at Service Science. His publications appear in leading academic journals, including Management Science, Marketing Science, the Journal of Marketing Research, and Quantitative Marketing and Economics. Prior to joining Olin, Professor Yao taught “Digital / Internet Marketing,” “Customer Analytics,” and “Marketing Management” at the University of Minnesota (Carlson), Northwestern University (Kellogg), and Duke University (Fuqua).
Area of Expertise:
Economics, Information Technology, Statistics/Econometrics, Advertising, Consumer Products Marketing, Marketing, Marketing Management and Strategy, Marketing Research, Pricing
Research Interests:
Quantitative Marketing, Empirical Microeconomics, Advertising, New Technology, Auctions, Competitive Strategy, Customer Analytics
Selected Publications:
- "Risk Transfer versus Cost Reduction on Two-Sided Microfinance Platforms", Quantitive Marketing and Economics, Issue 3, 251-287, with B. Bollinger, 2018
- "Does Online Word-of-Mouth Increase Demand? (and How?) Evidence from a Natural Experiment", Marketing Science, Issue 6, 838-861, with S. Seiler, W. Wang, 2017
- "The Impact of Soda Taxes: Pass-through, Tax Avoidance, and Nutritional Effects", Journal of Marketing Research, with S. Seiler, A. Tuchman
- "The Impact of Advertising along the Conversion Funnel", Quantitive Marketing and Economics, Issue 3, 241-278, with S. Seiler, 2017
- "TV Channel Search and Commercial Breaks", Journal of Marketing Research, Issue 5, 671-686, with W. Wang, Y. Chen, 2017
- "Sequential Search with Refinement: Model and Application with Click-stream Data", Management Science, Issue 12, 4345-4365, with Y. Chen, 2017
- "How Do Firms Make Money Online?", Marketing Letters, Issue 25, 331-341, with A. Lambrecht, A. Goldfarb, A. Bonatti, A. Ghose, D. Goldstein, R. Lewis, A. Rao, N. Sahni, 2014
- "Determining Consumers' Discount Rates with Field Studies", Journal of Marketing Research, Issue 6, 822-841, with C. Mela, J. Chiang, Y. Chen, 2012
- "A Dynamic Model of Sponsored Search Advertising", Marketing Science, Issue 3, 447-468, with C. Mela, 2011
- "Sponsored Search Auctions: Research Opportunities in Marketing", Foundations and Trends in Marketing, Issue 2, 75-126, with C. Mela, 2009
Honors and Awards
Finalist, Long Term Impact Award, INFORMS Society for Marketing Science, 2017
MSI Young Scholar, 2017
Finalist, John D.C. Little Best Paper Award, INFORMS, 2009
AMA John A. Howard Award for "A Dynamic Model of Sponsored Search Advertising", 2009
Faculty Impact Award for MBA teaching Excellence, 2010
Finalist, John D.C. Little Best Paper Award, Marketing/Management Science, 2011
Finalist, Frank M. Bass Dissertation Paper Award, INFORMS, 2011
Finalist, Frank M. Bass Dissertation Paper Award, INFORMS, 2012
Paul Green Best Paper Award, American Marketing Association, 2012
Management Science Meritorious Service Award, INFORMS, 2013
McManus Faculty Research Chair, Northwestern University, 2012-2013, 2014-2015
Videos
Song Yao, Kellogg School of Management: Advertising Along the Conversion Funnel
Song Yao, Univ. of Minnesota: The Impact of Soda Taxation
Song Yao: Does Online Word of Mouth Increase Demand?
Read about executive education
Cases
Yao, Song, Yuxin Chen and Wenbo Wang. Forthcoming. TV Channel Search and Commercial Breaks. Journal of Marketing Research.
This paper investigates the implication of time lapses that interrupt product consumption on consumer welfare. The preeminent examples of such time lapses are commercial breaks during television or radio shows. In the case of TV commercial breaks, for example, conventional wisdom dictates that consumers prefer watching TV shows without the disruption of commercial breaks. However, we argue that breaks may improve the welfare of consumers under certain conditions. In particular, when there is so much uncertainty that the consumer is unclear about the exact utility levels of different products, she has to engage in a costly search to resolve the uncertainty before choosing a product. In the context of TV programming, breaks lower the opportunity cost of search, allowing the consumer to sample alternative channels without further interrupting the viewing experience on her current channel. Using data from the Chinese TV market, we estimate a sequential search model to evaluate our conjecture. The data contain a quasi natural experiment: the Chinese government banned all in-show commercial breaks for episode-based TV series on January 1, 2012. This new policy on commercial breaks created exogenous variations in the data that allow us to separately identify heterogeneous consumer preference and search cost. Based on the analyses, we have found evidence that the intended improvement in TV viewing experience was limited. Many consumers were worse off due to the ban on commercial breaks, because they could no longer sample alternative channels that were preferable to their current product choices. We also investigate how the timing of breaks affects TV channels' viewership, which offers managerial insights about how firms should strategically adjust the timing of breaks.
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