If you are planning to go out tonight to play billiards at a bar in Montreal called Sharx, you might want to pay with cash. Predictive analytics from Canadian Tire demonstrated that Sharx customers are the more likely to default on credit than patrons of any other drinking establishment in Canada.
The analysis by Canadian Tire, now a decade old, marked the beginning of a wave of predictive analytics that now has companies trying to guess your next move — no matter how private it may be. Are you going to get married? Are you having a baby? Are you likely to get a divorce? Might you run into financial problems? Are you changing jobs or moving cities? Who are you going to vote for? Who are you likely to buy something from? What big purchase are you likely to make next? When will you get sick? How soon might you die?
If there is value in the answer to any of these questions, there is likely a company that is actively trying to predict the answer based on the data they have on you. As credit cards and loyalty cards have become omnipresent, more and more companies are now able to mine deeply personal data patterns to predict private behavior. According to the New York Times, “Almost every major retailer, from grocery chains to investment banks to the U.S. Postal Service, has a ‘predictive analytics’ department devoted to understanding not just consumers’ shopping habits but also their personal habits, so as to more efficiently market to them.”
Here are 11 real examples of how companies are trying to predict your next life event:
1. Predicting Pregnancy (Target): Target uses a statistical model to score every female customer on the likelihood that they are pregnant. It can accurately predict when a shopper is pregnant early in the pregnancy and her rough due date. As reported in the New York Times, Target’s data scientist is ”able to identify about 25 products that, when analyzed together, allowed him to assign each shopper a ‘pregnancy prediction’ score. More important, he could also estimate her due date to within a small window, so Target could send coupons timed to very specific stages of her pregnancy.” According to a Target data scientist who was quickly banned by the Company from talking to the press, “We knew that if we could identify them in their second trimester, there’s a good chance we could capture them for years . . . As soon as we get them buying diapers from us, they’re going to start buying everything else too.”
2. Predicting Divorce (Credit Card Companies): In the book Super Crunchers, a Yale professor describes how a major credit card provider uses purchase data to predict divorce, which in turn, helps the company predict potential future credit problems.
3. Predicting Financial Problems (Canadian Tire): According to the Daily Beast: “Cardholders who purchased carbon-monoxide detectors, premium birdseed, and felt pads for the bottoms of their chair legs rarely missed a payment. On the other hand, those who bought cheap motor oil and visited a Montreal pool bar called ‘Sharx’ were a higher risk. ‘If you show us what you buy, we can tell you who you are, maybe even better than you know yourself,’ a former Canadian Tire exec said.”
4. Predicting Your Next Vote (Obama & Romney Campaigns): Both major parties maintain broad voter databases appended with detailed demographic information. Using psychographic profiling, they are able to predict who you will vote for, how likely you are to go to the polls, and the potential for them to change your vote. Using this data, they are able to drive targeted media strategies and send volunteers to the right doors to maximize impact on the election. As reported in the Washington Post, “If you use Spotify to listen to music, Tumblr to consume content or Buzzfeed to keep up on the latest in social media, you are almost certainly a vote for President Obama. If you buy things on eBay, play FarmVille or search the web with Bing, you tend to favor former Massachusetts governor Mitt Romney.”
5. Predicting When You Will Switch to Fedex (UPS): UPS uses data analytics to predict when customers are at risk of abandoning the company and switching to one of its competitors. Whenever a potential switcher is identified, the company tries to prevent the loss with a phone call from a salesperson.
6. Predicting How Influential You Are (The Palms): Third party companies like Klout have built complex algorithms for assessing the social media impact of an individual. If you complain online, it’s your Klout score that will often determine the response. But now, companies like The Palms and Gilt Groupe are using these social media influence predictors to differentiate between customers. According to AdAge, “The Palms’ chief marketing officer, Jason Gastwirth, is currently building out ‘The Klout Klub,’ which ‘will allow high-ranking influencers to experience Palms’ impressive set of amenities in hopes that these influencers will want to communicate their positive experience to their followers.’ The Palms is already pulling in data from Klout and referring to it as part of their reservations process.”
7. Predicting How Much Money You are Willing to Lose (Harrah’s): According to the Daily Beast, “With its ‘Total Rewards’ card, Harrah’s casinos track everything that players win and lose, in real time, and then analyze their demographic information to calculate their ‘pain point’—the maximum amount of money they’re likely to be willing to lose and still come back to the casino in the future. Players who get too close to their pain point are likely to be offered a free dinner that gets them off the casino floor.”
8. Predicting Where You Will Be in 24 Hours (Researchers): According to Slate, “A team of British researchers has developed an algorithm that uses tracking data on people’s phones to predict where they’ll be in 24 hours. The average error: just 20 meters. That’s far more accurate than past studies that have tried to predict people’s movements. Studies have shown that most people follow fairly consistent patterns over time, but traditional prediction algorithms have no way of accounting for breaks in the routine. The researchers solved that problem by combining tracking data from individual participants’ phones with tracking data from their friends—i.e., other people in their mobile phonebooks. By looking at how an individual’s movements correlate with those of people they know, the team’s algorithm is able to guess when she might be headed, say, downtown for a show on a Sunday afternoon rather than staying uptown for lunch as usual.”
9. Predicting Whether You Will Have an Accident (All Auto & Fleet Insurers): Every auto insurer uses a wide variety of data to predict which customers are at the greatest risk of having an accident and filing a claim. In the trucking industry, real-time driving data has provided a rich source of predictive data to help fleet managers prevent accidents. According to Trucking Info, when one company first installed the new tracking system, “the predictive analytics gave a warning that its star driver was at the highest risk in the company for an accident. Officials thought it must be a glitch and ignored the warning. A couple of weeks later, he was in a high-speed crash on I-10. What no one at the company knew was that this driver’s home had been severely damaged by Hurricane Katrina. His time in the FEMA trailer was running out. He was working on the house over the weekend when he hurt his ankle. So he was driving not only distracted by his problems, but he was also in pain.” By tracking driving data and correlating it to patterns before other accidents, Fleet managers are now able to predict many accidents before they happen.
10. Predicting Your Future Health (All Health Insurers): All major health insurers now use predictive analytics to predict your future health problems and likely treatment costs. According to the American Journal of Managed Care, “statistical tools might detect the diabetic patients with the highest probability of hospitalization in the following year based on age, coexisting chronic illnesses, medication adherence, and past patterns of care.” Health care providers use past claim data, known health issues, and even surveys and electronic medical records.
11. Predicting Death (The Life Insurance Industry): This may be the oldest and most established use of predictive analytics. The actuarial industry was partially built on the science of predicting an individual’s life expectancy based on known facts. While highly regulated, every life insurance company uses as much data as it can legally touch to build models predicting how long individual policyholders are likely to live.
While this list represents a small fraction of the ways that companies are using data to predict our behavior, a coming proliferation of human data is likely to drive a rapid increase in the business of prediction. As more of our movements, browsing patterns, purchase history, and social media interactions become electronically measurable, more companies will find ways to use this data to predict our activities and make money.
So the next time I go out to play billiards in Montreal, I’m definitely paying in cash.