Song Yao (姚松)
Associate Professor of Marketing, Olin Business School, Washington University in St. Louis

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I am an Associate Professor of Marketing at Olin Business School, Washington University in St. Louis.  I have won the Paul Green Best Paper Award (2012) and the John Howard Dissertation Award (2009), both of which are sponsored by the American Marketing Association. I was the finalist for the INFORMS Frank Bass Outstanding Dissertation Award in 2011 and 2012, the John Little Best Paper Award in 2009 and 2011, the Long Term Impact Award in 2017, and the runner-up of Dick Wittink Prize in 2018. I was also selected by the Marketing Science Institute as one of the MSI Young Scholars of 2017.

I am serving on the Editorial Review Board of the Journal of Marketing Research, Marketing Science, and Quantitative Marketing and Economics. I am also the Associate Editor of the journal Service Science.

Prior to joining Olin, I taught “Digital / Internet Marketing,” Customer Analytics,” and Marketing Management at the University of Minnesota (Carlson), Northwestern University (Kellogg), and Duke University (Fuqua).


 

 PhD (Business Administration), Duke University, 2009
 MA (Economics), University of California, Los Angeles, 2004
 BA (Economics), Renmin University of China, 1999
    
 Contact Information

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Recent Research

Local soda taxes are mostly ineffective:
In our recent working paper (with Stephan Seiler and Anna Tuchman), we analyze the impact of a tax on sweetened beverages in Philadelphia, often referred to as a “soda tax.” We find that the tax leads to a 34% price increase for unhealthy beverages such as soda. Demand in the taxed area decreases dramatically by 46%. However, cross-shopping outside of Philadelphia substantially offsets the reduction in sales within the taxed area, and there is little substitution to untaxed beverages (water and natural juices). Furthermore, relatively healthier taxed products experience more reduction in sales. Consequently we find no significant reduction in calorie and sugar intake, and the city's soda tax revenue falls short. NPR, MarketWatch, and National Review have reported this paper.

Protect consumers privacy when exploring sensitive consumer data: A machine learning framework
My recent working paper (with Hyesung Yoo, Luping Sun, and Xiaomeng Du) studies how firms may use machine learning tools to explore consumer data while protecting consumers privacy.  We apply the federated learning and GRU neural network to predict each individual consumer's click-stream, and show the approach achieves a high level of accuracy. At the same time, the firm does not need to store or access the data directly.

Runner-up for Dick Wittink best paper award: 
The impact of advertising along the conversion funnel
My research published in Quantitative Marketing and Economics (with Stephan Seiler) has just won the runner-up for the Dick Wittink best paper award at the journal. In the paper we combine path-tracking data with advertising data, and analyze advertising effectiveness along the conversion funnel in brick-and-mortar supermarkets. My coauthor Stephan discusses the insights of the paper in the following interview.




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Last update: May 2019