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We read with great interest a July 9th Washington Post Wonkblog analysis by Andrew Van Dam, “What We Buy Can Be Used to Predict Our Politics, Race or Education—Sometimes with More Than 90 Percent Accuracy.” The article took a look at a new paper from the National Bureau of Economic Research, “Coming Apart? Cultural Distances in the United States over Time,” in which economists Marian Bertrand and Emir Kamenica input a variety of sources—including MRI data—to “teach” computers to figure out gender, race, income and other qualifiers using such inputs as media consumption, consumer behavior and time spent data.
The authors of both the research and the WaPo analysis point out that our cultural divide is—and has been—wide and can be demonstrated by such things as the shows we watch and the items we buy, or, as Van Dam states, “It turns out that people are separated not just by gun ownership, religion and their beliefs on affirmative action—but also by English muffins, flashlights and mustard.” Some key findings mentioned included watching Rudolph the Red-Nosed Reindeer and buying Stove Top Stuffing as key indicators of whiteness; owning an iPhone as a prime indicator of wealth (an honor previously held by Grey Poupon mustard); and not eating at Arby’s and not buying Dockers as strong predictors that a person is liberal.
We certainly agree that all of these preferences can tell us much about a person—marketers have always known this, and we have been producing analyses on such topics for decades. That said, taking up the economists’ claim that such findings have remained remarkably constant over time, we thought it would be useful to take a look back at topline findings from analyses we have conducted, some of which use the same MRI data in the National Bureau of Economic Research report. We also include research we conducted in partnership with Next Generation Research in the early-2000s, as well as more recent research we have culled independently. Not shown here, but also part of our body of research has been profiles of individual brands, programs and mindsets, as well as an in-depth study of how all of these relate to ad receptivity. We have long-found that doing deep dives into datasets such those generated by MRI, Simmons and others, can yield thought-provoking findings about the types of people who buy your products, what they like, what they watch, and what they do. Clearly, consumers are more than their demographic profiles and going beyond such basics is key to more effectively targeting your best prospects.
