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Retailers regularly show users online ads based on their web browsing activity, benefiting both the retailers, who can better reach potential customers, and providers of web content, who can better monetize their traffic. Many such ads rely on third-party information brokers that maintain detailed personal information, prompting legislation such as do-not-track that would limit or ban the practice. We gauge the potential economic costs of such privacy policies by analyzing anonymized web browsing histories of 14 million individuals. With respect to advertisers, we find that about 3% of retail sessions are initiated by third-party advertising, a number that holds across market segments and also for online-only retailers. For content providers, we find that 32% of aggregate traffic comes from websites that display third-party advertising, and for online publishers (e.g., news outlets) XX% of traffic comes from such sites. Finally, for sites that display third-party advertising, we estimate that most of the top 10,000 could generate comparable revenue by switching to a “freemium” model, in which loyal site visitors are charged about $2 a month (the typical user frequents 2{3 of these sites); outside the top 10,000, such sites do not typically receive enough loyal traffic to implement such a strategy.

Joint work with: Ceren Budak, Sharad Goel, Justin Rao and Giorgos Zervas (BU, Consulting Researcher at MSR)

Paper can be found at: http://justinmrao.com/dnt.pdf

Bio:

Justin M. Rao is a researcher at Microsoft Research in New York City. He previously was at Yahoo! Research in Santa Clara, CA for two years after receiving his Ph.D. in Economics from UCSD in 2010. He is an empirical economist with a focus on e-commerce, decision making and writing about himself in the 3rd person.