Simon Business School

Faculty Profile


Prof. Johnson’s research discusses the market for online display advertising. His research measures ad effectiveness, examines the welfare implications of consumer tracking, and quantifies consumer demand for ads.

Teaching Interests

Marketing Channels, Internet Marketing

Research Interests

Internet Marketing, Industrial Organization, Auctions


Northwestern University - 2013
Ph D
University of British Columbia - 2007

Current Research Programs

Location, Location, Location: Repetition and Proximity Increase Advertising Effectiveness*
Yahoo! Research partnered with a nationwide retailer to study the effects of online display advertising on both online and in-store purchases. We measure the impact of frequency of advertising exposure using a simple randomized experiment: users in the `Full' treatment group see the retailer's ads, users in the `Control' group see unrelated control ads, and users in the `Half' treatment group see an equal probability mixture of the retailer and control ads. We find statistically significant evidence that the retailer ads increase sales 3.6% in the Full group relative to the Control. We find strong benefits to repeated exposures among the 80% of users who see up to 50 ads per person, as revenues increase approximately linearly with a marginal benefit of around 5¢ per exposure. We find especially high ad effectiveness for the retailer's best customers and those who live closest to its brick-and-mortar locations; these findings are consistent with a model of advertising in a Hotelling model of differentiated firms. *Previously released as "Add More Ads? Experimentally Measuring Incremental Purchases Due To Increased Frequency of Online Display" Advertising
The Impact of Privacy Policy on the Auction Market for Online Display Advertising
The advent of online advertising has simultaneously created unprecedented opportunities for advertisers to target consumers and prompted privacy concerns among consumers and regulators. This paper estimates the financial impact of privacy policies on the online display ad industry by applying an empirical model to a proprietary auction dataset. Two challenges complicate the analysis. First, while the advertisers are assumed to publicly observe tracking profiles, the econometrician does not see this data. My model overcomes this challenge by disentangling the unobserved premium paid for certain users from the observed bids. In order to simulate a market in which advertisers can no longer track users, I set the unobserved bid premium’s variance to zero. Second, the data provider uses a novel auction mechanism in which first-price bidders and second- price bidders operate concurrently. I develop new techniques to analyze these hybrid auctions. I consider three privacy policies that vary by the degree of user choice.