Song Yao (姚松)
Associate Professor of Marketing, Carlson School of Management, University of Minnesota

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I am an Associate Professor of Marketing at the Carlson School of Management, University of Minnesota.  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 teach elective course “Digital / Internet Marketing at the Carlson School of Management. Prior to joining Carlson, I taught Customer Analytics at the Kellogg School of Management, Northwestern University, and Marketing Management at Duke University.


 

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

  (612) 625-2903
  Carlson School of Management, University of Minnesota
        321 19th Ave South, Suite 3-150, Minneapolis MN 55455
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 30-40% price increase for unhealthy beverages such as soda. Demand in the taxed area decreases dramatically by 42%. However, cross-shopping outside of Philadelphia completely offsets the reduction in sales within the taxed area, and there is little substitution to untaxed beverages (water and natural juices). Consequently we find no significant reduction in calorie and sugar intake, and the city's soda tax revenue falls short. MarketWatch and National Review have reported this paper.

Protect consumers privacy while 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: December 2018