Dr. Robert Phillips is Director of Pricing Research at Amazon where he leads a team of data scientists and economists who develop the analytic approaches underlying pricing for Amazon’s retail and marketplace businesses.  Prior to joining Amazon, Dr. Phillips held positions as Director of Marketplace Data Science at Uber, Professor of Professional Practice at Columbia Business School, Director of the Columbia Center for Pricing and Revenue Management and a Visiting Scholar at the Stanford Business School.

Dr. Phillips is also the founder and a member of the Board of Directors of Nomis Solutions; a software and consulting company that helps financial service companies better manage pricing and profitability.  Dr. Phillips previously served as CEO of Decision Focus Incorporated, a consulting company specializing in the application of business analytics in business and government and as CEO of Talus Solutions, a revenue-management software and services company.  During his 25-year career in industry, he was a pioneer in the application of pricing and revenue optimization in many different industries including passenger airlines, cruise lines, rental cars, automotive, hotels, air cargo, trucking, container shipping, and financial services.  

Dr. Phillips is author of the books Pricing and Revenue Optimization and Pricing Credit Products  as well as co-editor of The Oxford Handbook of Pricing Management. He is an INFORMS Fellow and a recipient of the INFORMS Impact Prize.
 
Dr. Phillips holds a Ph.D. in Engineering-Economic Systems from Stanford University and Bachelor Degrees in Economics and Mathematics from Washington State University.

Pricing and Analytics - Challenges and Research Opportunities

In this webinar, I will discuss some of the most important pricing challenges facing on-line retailers and marketplaces.  The rise of on-line commerce has both accelerated the velocity of pricing changes and vastly increased the amount of available data.   Automated pricing algorithms are required to deal with the sheer volume and velocity of pricing changes and machine learning approaches are increasingly used to exploit the immense volume of available data.  I will discuss some of the current challenges of on-line pricing and the implications for research, drawing upon examples from multiple on-line marketplaces.   Specific topics include the use of reinforcement learning for dynamic pricing,  the challenges of conducting on-line pricing experiments, and estimation of the implications of pricing on long-term customer behavior.  I will also discuss areas of potential research that would be most impactful for practice.