Data scientist Eric Siegel explains the brave, new, and surprising world of predictive analytics.
Whenever you go to a major merchandise retailer and pull items off the shelf, you create a little piece of information that the retailer stores in a database. As more people pull items off those shelves, the retailer has the opportunity to learn something about all of you, in real time, and can use that information to predict what you might be interested in buying next. With the emergence of extremely large databases and ever-better transaction records, the relationship between what we buy, where we go, and what we might do next is becoming ever more clear.
In his new book, Predictive Analytics, researcher Eric Siegel refers to this computerized semi-clairvoyance as “the prediction effect.” Siegel achieved some small notoriety in 2012, when New York Times writer Charles Duhigg interviewed him on a story about predictive analytics (PA). Siegel recalls that Duhigg “asked for interesting discoveries that had come from PA. I rattled off a few that included pregnancy prediction.” Siegel directed him to a video from one of the many PA conferences that Siegel runs.
The video was a keynote presentation by data scientist Andrew Pole of Target, discussing how Target used data from its massive baby-registry service to predict pregnancy through consumer habits. For instance, many women, upon discovering that they are pregnant, may put unscented skin lotion on their registries, since pregnancy can dry out skin and scented lotion can have a negative effect on a developing fetus. The switch to unscented baby lotion can serve as one of many predictors of pregnancy—an issue of keen interest to Target, since expectant mothers can become much more profitable customers.
The Target model, in the words of Siegel, “identified 30 percent more customers for Target to contact with pregnancy-oriented marketing material—a significant marketing success story.”
Duhigg’s piece, titled “How Companies Learn Your Secrets,” had a rather more skeptical take on Target’s use of analytics to predict consumers’ medical condition. In effect, he showed that Target was able to predict that one young customer was pregnant before she had informed her father, resulting in some rather awkward conversations when said father discovered coupons for baby goods addressed to his daughter in his mailbox.
After appearing on the front page of the New York Times, Duhigg’s article was picked up by the Daily Show,the Colbert Report, Fox and Friends, and numerous other outlets. His book, The Power of Habit (Random House, 2012), went on to become a best seller. For most viewers and readers, this was their first introduction to the field of predictive analytics, and the message conveyed was clear and alarming: Marketers will use your big data against you.
In Predictive Analytics, Siegel offers a defense of the field and a portrait that is far less menacing toward consumers. He also does an ample job of explaining how consumer data becomes predictive, with many surprising insights gleaned through PA. For instance, you’re more likely to buy plane tickets around 10 a.m., to go to a retail Web site at 8 p.m., and check your stocks at 1 p.m., just after lunch. Vegetarians who have preordered a special meal on an airline are much more likely to make their flight than other passengers. People in the Congo buy phone cards in anticipation of upticks in violence. And when you stop smoking, you increase the likelihood that your friends will quit by 36%.
Siegel also goes into considerable depth on predictive-analytics methodologies, particularly uplift modeling, defined as “a model that predicts the influence of an individual’s behavior that results from applying one treatment over another.” One example of this is the A/B test, which has been around for some time. In an A/B test for marketing, for instance, one group of people will be shown one ad, another group a second ad, and a third group—the control group—no ad at all.
To understand how predictive analytics has reached its current level of importance and power, consider that, just a few decades ago, A/B tests were the domain of big marketing firms that could afford months and sometimes years of focus group testing. Today, you participate in this sort of testing whenever you go online, and click on certain ads and not others. It is automatic and ubiquitous in an online environment. With just a small amount of training, you can get a good result, an actionable insight—or what Siegel calls a prediction effect.
Predictive analytics can be used unethically as well as ethically, but it will certainly become much more common. It’s an inexorable trend driven by the massive amounts of data we create as consumers (1.8 million megabytes a year in the United States) and what that data reveals about intent and probability.
We create the future through our actions, and we are increasingly doing so in a way that is measurable. Understanding the process by which that happens is useful and empowering, even for consumers. As we better understand some of the coercive tactics that marketers use against us, we can edit our behavior.
Whether you agree or disagree with the ethics of some of these tactics, they’ve become a part of the world and our future.
About the Reviewer
Patrick Tucker is the director of communications for the World Future Society and deputy editor of THE FUTURIST magazine. He is the author of the forthcoming book The Naked Future: What Happens in a World That Anticipates Your Every Move? (Current, 2014).
Originally published in THE FUTURIST, November-December 2013