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Offsite Data and Advertising: Q&A with Bradley Shapiro
Marketing professor Bradley Shapiro discusses insights from his research on data sharing and ad targeting on Meta—including the implications for legislation.
- May 15, 2023
An expert in the economics and measurement of advertising, Bradley Shapiro is professor of marketing and the True North Faculty Scholar at Chicago Booth. His research focuses on empirical industrial organization and applied microeconomics. He also studies marketing in the health and pharmaceutical sectors, including how firm actions affect health choices by consumers. His research has appeared in the Journal of Political Economy, Econometrica, Marketing Science, and Management Science, among others.
In Spring Quarter 2023, Shapiro taught the Marketing Literature Seminar, a PhD course designed to familiarize students with current marketing literature and the process of generating research ideas. Recently, he participated in a tweet chat with Chicago Booth to discuss his career journey, his research on the value of offsite data to advertisers, and some of his personal interests outside of academia. Read an edited version of the conversation below.
Why did the field of marketing capture your interest as a researcher, specifically the economics of advertising?
I was interested in regulation when I started my PhD, particularly in how regulation affects firm incentives. I gravitated toward health economics, as the healthcare industry was significantly regulated, leading to all sorts of interesting firm behavior.
It quickly became clear that health economics had not yet developed a clear way to think about advertising. How much does it actually affect decisions? Are those effects good or bad for consumers and the society?
The basics of this problem were difficult to solve: how to estimate the impacts of advertising accurately, where to find the right data. How does advertising increase sales? How does that map onto consumer or social welfare? I just got hooked.
You have degrees in mathematics and economics. How did your career lead you to teaching marketing at Chicago Booth?
Marketing is a fundamentally applied field. I was not particularly interested in doing esoteric math research—I wanted to do something that had a clear connection to real practice. Within economics, I just found firm behavior to be very interesting to study.
I wrote the dissertation I wanted to write. It just so happened that academic marketing was a good home for that kind of research. Chicago Booth, in particular, wanted to hire me. Given how awesome of a place Booth is to do research, I couldn’t exactly say no!
What are some areas you’ve covered in the Marketing Literature course you taught in the Spring semester?
The PhD Marketing Lit course focused on measuring consumer preferences using survey methods. Marketing really was a pioneering field in this type of measurement, as firms needed ways to forecast demand for new products.
The term we use for this is “conjoint analysis.” We discussed the advances in survey measurement techniques in the past 50 years or so and challenged the students to think about modern applications in research and practice where it might be useful.
“The loss of third-party data is significant for many advertisers—the median advertiser finds it about 40 percent more expensive to acquire a customer via advertising if it doesn’t have access to third-party data.”
You have a working paper on the value of offsite data to advertisers on Meta. What are some of the biggest learnings from this research?
We ran a large-scale experiment across hundreds of thousands of advertisers on Meta. Some users were served ads from companies based on third-party data, and others were only served ads based on first-party data.
To be concrete, the main way Meta uses third-party data in ad targeting is to get information about consumer purchases on websites and use that information to find users on Meta who are “similar” to those who purchased on the external websites.
If third-party data is unavailable, Meta can only use data that is wholly contained on/owned by Meta (first-party data). So when purchase data isn’t available, the best Meta can do is to use ad clicks as the training data with which to find similar customers.
Our basic finding is that the loss of third-party data is significant for many advertisers—the median advertiser finds it about 40 percent more expensive to acquire a customer via advertising if it doesn’t have access to third-party data.
What types of advertisers benefit most from data sharing?
This is an excellent question, as there is a huge dispersion in the value of third-party data across different types of advertisers. The loss of ad efficiency when third-party data is restricted is considerably more pronounced among smaller-scale advertisers.
This is intuitive, as smaller-scale advertisers tend to have more niche products that are relevant to fewer users. If the targeting algorithm gets worse, the probability of serving the ad to users who find your product irrelevant increases.
The smaller the fraction of the population that finds your product relevant, the more important precise targeting is.
As an example, we find essentially no impact of data loss for very large corporations with mass appeal (such as companies like Coca-Cola). Since those products are largely relevant to everyone, changing the targeting rule largely doesn’t matter.
What implications might your research have for legislation on data sharing and ad targeting?
The short answer is it’s complicated. Much of the debate around privacy has focused on how much users value their privacy and has ignored any benefits that might be received from the use of that data.
Our paper brings this new angle to the privacy-policy discussion. Yes, users may value their privacy. On the other hand, they may also value learning about products that are “relevant” to them.
Beyond that, advertisers clearly stand to gain considerably from that data, as they can acquire new customers while spending less on advertising if it is well targeted.
Our research can conceivably open the conversation about compensating individuals for their data. Users value their privacy at some level. Firms value their data at some level. As long as the latter is more than the former, there could be gains from trade.
Things get really complicated for consumer welfare if we start talking about changing the set of products that can conceivably compete in the market. Lack of access to preferred products very well might outweigh consumer valuation of privacy.
Suffice it to say, our paper does not come close to answering all of these questions. In fact, it is likely that we open more questions than we close. These are important policy discussions that still need to happen seriously.
Outside of work, what are some hobbies you enjoy?
I’m a certified private pilot, although I have not flown in quite some time. It turns out that to fly airplanes you need both a lot of time and a lot of money. I flew until I ran out of money. Now that I have the money, I lack the time!
I’m a pretty avid (noncompetitive) runner. One of my favorite ways to experience new cities where I travel is to combine my morning jog with sightseeing.
My biggest, everyday hobby is cooking. It centers my mind and really brings me a sense of peace. Plus, it’s a great outlet for creativity outside of research. I cook dinner for my wife and me six days a week.
I’m also somewhat involved in my family’s wine importing business, Quigley Fine Wines, on the side. I’ve been able to live my love of food and wine through travel over the past decade or so. Eating and drinking just bring so many common experiences to very different people, and I love that.
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