The Naked Future

by Patrick Tucker

  “Let’s say we have a sixty-year-old woman who lives in a comfortable suburb of Memphis,” Loveman told journalist Robert Shook in 2003. “She visits our Tunica property on a Friday night, briefly plays a dollar slot machine, and goes home. Based on traditional casino methods, she’d have a low theoretical worth, perhaps a few dollars. Consequently, she wouldn’t be a likely prospect to pursue, and little effort would be made to get her to come back. And in all likelihood, she wouldn’t respond to it. Our present system draws distinctions between the observed worth of a customer and what we predict their worth is.”10

  Your predicted worth is a number representing how much money the Harrah’s system has calculated you can be persuaded to lose—er, gamble with—when you go to one of their locations. It’s based on your ZIP code, what you play, how you play, and other indicators of wealth and willingness to gamble. Reportedly, the system is 90 percent accurate at predicting how much a customer can be persuaded to drop at a casino.11

  These customer-worth scores serve a greater function, predicting exactly what offer potential customers respond to and when to issue it. If you’re in the casino and you’re losing heavily (as measured by the account activity on your card), Harrah’s will dispense a “luck ambassador” with a coupon for something the system has calculated you’ll like, anything from a free drink to show tickets. But these offers don’t just come to you while you’re on the floor. If you go to Harrah’s on your birthday, on your annual vacation, on the seventh day of the seventh month of the year, the company will send you an offer timed to encourage you to do that sort of thing again.

  Do you go to the casino when your wife is out of town? As Christina Binkley of the Wall Street Journal discovered, Harrah’s knows that, too. She observed a Harrah’s-hired telemarketer named Mr. Salvador go through the process of contacting repeat customers with special offers and saw firsthand the wealth of data Harrah’s can use to turn any customer encounter to the company’s favor. “On a recent list was a thirty-four-year-old man who hadn’t been to a Harrah’s Entertainment Inc. casino since November 2003. Before then, according to the data, he had made trips to the Rio in Las Vegas, as well as casinos in Tunica, Miss., and East Chicago. ‘This is a customer who can only play when his wife is on vacation or when he’s on a trip,’ says Mr. Salvador.’”12

  Where did that information come from? There are several ways to infer it. This thirty-four-year old man might have indicated on a customer satisfaction survey form his marital status and that he was traveling alone. Alternatively, perhaps when he entered that Harrah’s hotel in November 2003 a friendly desk clerk or casino cashier asked if he was in town on a special occasion and he mentioned that the special occasion was being away from his wife. She simply complied with the company guidelines and typed this answer into the form in front of her. Either way, what was a very forgettable exchange for this thirty-four-year-old man has become a piece of data that now follows him everywhere and influences how and when Harrah’s contacts him. Harrah’s has become a vast sense organ. As an institution, it’s constantly detecting and responding to new information on its customers. It remembers everything, weighs every interaction. It knows your limits better than you know them yourself, and it wants you to keep playing.

  Writers in the business press credit Loveman with completely changing the culture at Harrah’s. Read that to mean he fired a lot of people, mostly in marketing. In his interview with Greenfeld, Loveman explains his impatience with traditional marketing methods. “Testing and measuring is important to us. When our employees use the words ‘I think,’ the hair stands up on the back of my neck. We have the capacity to know rather than guess at something.”

  What does the history of the casino loyalty programs mean for the future of shopping, marketing, and advertising? Simply put, the retail world of 2013 has become a Harrah’s casino.

  Futurizing Tuna Steaks

  The date is April 25, 2012. I’m at an upscale gourmet grocery chain in a tony Toronto neighborhood. With me is Wojciech Gryc, the twenty-five-year-old creator of a software platform that automates customer analytics. His company, Canopy Labs, is looking to put the predictive analytics-crunching capabilities of Walmart and Target into the hands of small- and medium-size businesses. The Canopy Labs platform “predicts which customers are most likely to accept the offer you are going to be suggesting,” he explains. Though only four months old, Gryc’s company has already attracted funding from Silicon Valley angel investors. He’s riding the big data hype wave but his tech experience is deeply ingrained. He got his start in big data at IBM but his dad had him at the computer at the age of ten. Point to a product and he can tell you how to use data to sell it. “You can optimize any shopping experience,” he says. In this instance he means optimizing the experience for the seller, the clients he works for.

  We’re here to see what that looks like.

  Grocery stores are laid out according to a marketing science that’s actually been around for decades. Thousands of recorded customer decisions help such outfits as the middle-class gourmet grocery that I’m standing in to nudge customers where the store wants them to go. Past the cash registers we encounter cakes and cupcakes. These are prominently displayed because they’re potential impulse buys. They have a higher profit margin and longer shelf life compared to the fruits and veggies. They’re also the sort of thing people don’t put on a regular shopping list. The store wants to make sure we see them. This is an act of priming. Even if you demure the sweets, you still had to think about it. You’ll be less able to fight off the next impulse.

  Over the past decade no chain amassed more data to figure out these sorts of placement decisions than Walmart, which experiences more than 1 million customer transactions every hour.13 In 2004, in what is probably the most famous instance of a retailer using customer data to effect product placement, Walmart stacked the shelves of its stores standing in the path of Hurricane Francis with Pop-Tarts and beer. Their terabytes of customer data from a previous mega storm, Hurricane Charley, showed that these were the items most sought after in Walmart stores before hurricanes. Hurricane survivors, it seems, like treats that can be microwaved or ingested straight out of the package (only the latter is advisable in the case of beer).

  Discovering the reasons why customers pick up one item and discard another is the central challenge of predictive analytics in retail. This is what I call the Bear problem. My cat, Bear, rejects food randomly. I open a can of a type of food he liked yesterday, or a week ago, and he’ll either turn his nose up at it or eat it for reasons that aren’t obvious. He also spends part of the day running around outside. We have an inside cat who never rejects food. If we assume that Bear’s fickleness is caused by something he discovers outside (which is indeed just an assumption of causation based on an observed correlation), then determining why my cat rejects what I give him involves tracking a huge number of variables, such as where he goes on his excursions and what he encounters there.

  In figuring out why people pick up one thing and put down another, Walmart is basically faced with the same problem that I am. And they’ve employed some controversial tricks to get at it. In 2003 Walmart and Procter & Gamble ignited a firestorm of controversy when the Chicago Sun-Times revealed that the two companies were outfitting cosmetics with RFID tags. When customers picked up one of the chipped cosmetics items like Lipfinity lipstick, a surveillance system would follow the customer around the store to see what other items she considered buying but did not.14

  Both Walmart and P&G maintain that they used the tags solely for the purpose of tracking how the consumers handled the products inside the store and, it should be said, no one has found any evidence to the contrary. But from a retail perspective, data about how customers use products outside of a store are far more useful than knowing what they do inside the store. Does the average Lipfinity owner leave her lipstick on her dresser at home or carry it in her purse? Where does that purse go?


  Not of all Walmart’s tactics to triangulate customer behavior have been so controversial. In the fall of 2006 Walmart stepped away from the stereotype of the impersonal big-box retailer with the creation of its “store of the community” program, which gave managers much more leeway in terms of stocking their shelves and laying out their stores. The company took careful note of what worked in what market, to better understand why some strategies succeeded and others failed. This enabled Walmart to expand the number of planograms, or acceptable merchandise-layout configurations, from five to more than two hundred by 2010, an important step in customizing the retail experience to the individual. What was an impersonal big-box outlet began to metamorphose into a village store, better reflecting the purchasing habits of the community.15

  But altering the layout of particular outlets and the displays therein for every shopper will never be practical, even in the future. The challenge today is to re-optimize an experience designed for a statistically generic person into an experience for living individuals.

  How do you personalize something that was designed for the aggregate?

  “If you were the management of this store, how would you optimize tuna steaks to me?” I ask Gryc. We determine that one of the key variables of tuna steak pricing should be freshness. Tuna steaks are highly perishable. From the store’s perspective, it makes better economic sense to offer me a discount on the tuna if the alternative is throwing it out at the end of the day. In the vast majority of grocery stores around the world, the current method for accomplishing this is to hang a SALE sign on the tuna’s glass casing. A better method, Gryc explains, is notifying your customers who buy a lot of tuna and then offering them a time-sensitive coupon. These e-coupons can easily be delivered to a user’s phone on the basis of context, meaning where that user is and even what activity that user is engaged in, easily determined by other app usage or simply location. (Someone in a bowling alley is unlikely to be windsurfing.)

  The store can then track the number of those coupons that are redeemed. This is sometimes called one-to-one marketing at scale. It allows the store to order tuna with greater confidence that it’ll be able to move it at different price points. It’s less likely to have excess tuna and won’t need to offer as many deep discounts. A restocking decision that, perhaps, was once made at the district manager level can be made at the department manager level; instead of being made in a distant office, it’s made by the guy behind the counter. When people redeem the coupons the store gets data on which sorts of customers respond to which sorts of offers.

  Personalized, in-store coupons have existed in various forms for years. In 2006 the Stop & Shop chain outfitted the carts of three of its Massachusetts branches with a digital personal assistant called Shopping Buddy. You just swiped your card and received personalized discounts and offers while strolling the aisles. It was just like shopping at Harrah’s!16

  The drawback, of course, was that you had to be in the store to get the coupon; the program didn’t work to attract the sorts of people most likely to accept offers.

  Today, people carry their own shopping buddies in their pockets. The smartphone has become the essential shopping accessory. In 2012 more than a quarter of smartphone owners used their phones in stores to read reviews and to hunt for better prices on the goods.17 Store-sponsored smartphone apps respond to this trend by offering personalized coupons to customers where they are. Today, all sorts of stores offer a variety of different app-based personal shopping assistant programs for iPhone and Android, which interact with customer loyalty accounts.18

  UK-based retailer Tesco can track 80 percent of its sales through its club card and could provide more than ten coupon variations in 2012. They, too, tailor deals to individuals on the basis of inferred net worth. Even Walmart, with its business model of always having lower prices than its competitors, found enough wiggle room in its pricing structure to offer extra-special low prices to folks willing to give up a bit more in personal data. A couple of years ago, Walmart put together a customer loyalty program called eValues, which targeted specific deals to specific customers through e-mail and apps.19

  “It’s kind of like the eHarmony of couponing—we find the very best offers for the customer,” Catherine Corley, vice president of member program development at Walmart, told the Daily Herald.20

  These programs, and the individualized offers associated with them, would seem to be a victory for consumers. That quality of seeming is important. People who participate in customer loyalty programs actually spend more at stores they shop at than people who aren’t part of such programs—the same way people in Harrah’s Total Rewards wind up gambling, and losing, more at casinos than those who come to the casino with only cash.

  Before long, eValues customers were making twice as many trips to Walmart as people who weren’t in the program. Walmart was willing to slash its low prices even further for the same reason Harrah’s likes giving away hotel rooms to little old ladies. Both were looking at the long game, what your consumer behavior looks like over time so they can predict what sort of customer you will be in the decades ahead. Today, eValues is called Instant Savings and it’s available to Sam’s Club members (Sam’s Club is owned by Walmart). Various other aspects of the eValues program have been rolled into the Walmart and the Sam’s Club apps.

  Naturally, your customer data belongs to you first. You are the point of origin. And with just a little effort you can get a sense of how the stores that you shop at, such as Walmart, view you and your lifetime value as a customer. If you’re interested in performing this search on yourself, you can go to the investor relations portion of a company Web site, request an investor prospectus, and find a profile of an average customer to see how you compare. Publicly traded companies have to release annual sales figures, profits, and liabilities and these often include information on target demographics. You can also go to the Securities and Exchange Commission’s EDGAR database and search for a particular company’s 10-K form.

  For instance, the average Walmart customer spends $1,088 per year at the store, makes twenty-seven shopping trips in that year, and spends $40.30 per trip, according to the most recent publicly available information.21 If you spend more than that at Walmart, you have some idea how important you are relative to the average.

  Do you know if you’re part of a demographic that the store is going to court more aggressively? That can be a factor as well. Walmart (publicly) divides its shoppers into three groups: “brand aspirationals,” people without much money who don’t want to look cheap and so buy brand-name items at discount prices to cover that up; “price-sensitive affluents,” meaning cheap rich people; and “value-price shoppers,” regular cheap people.22

  Your ZIP code could also be a factor in how you’re scored. Big companies use geo-information services (GIS) to figure out the income levels for different neighborhoods. One company that provides both GIS software and GIS insights is the Environmental Systems Resources Institute (Esri). It can classify any particular neighborhood into sixty-five different segments on the basis of income, consumer habits, number of kids, average level of education, as well as dozens of other variables, and does this on a block-by-block basis. (The information comes from the U.S. Census.) Within these segments is a fair amount of nuance. People who fall into the “military proximity” group are twenty-eight years old on average, make $41,000 a year, don’t have pets but do have renter’s insurance, and go to places like SeaWorld on vacation. “Great expectations” are people who make $35,000, live primarily in the Midwest, and do most of their grocery shopping at Walmart. For big businesses looking to enhance customer targeting, this is immensely valuable information. Esri makes a lot of this data available to anyone through their premium Web product, the ArcGIS platform. It’s not free but Esri does offer free trials and extremely generous pricing for nonprofits. Keep in mind that your neighborhood score will be used differently depending on the business you�
��re looking to engage with. For instance, if you want to lower your insurance premiums, don’t buy a house in a “good” neighborhood if it’s also an expensive neighborhood. Instead, move next door to clerical workers.

  As mentioned earlier, if you use Verizon or AT&T, your phone company is also helping marketers much better target mobile ads to you. AT&T, for instance, offers a product called AdWorks that promises to “connect advertisers with their audiences across online, mobile and TV channels.” In other words, it helps advertisers stick particular ads in front of your face depending on where you are and what you’re doing.

  To do that, AT&T partners with data brokerage companies such as Acxiom. You’ve probably never heard of Acxiom but rest assured, the company has heard of you. Acxiom has information on more than 500 million people around the world, an average of 1,500 data points per individual, around 6 billion total pieces of information across all of Acxiom’s databases. This data could be anything from the magazines you subscribe to, to the sort of car you drive. It’s information you volunteered on surveys and when you opted in to various service contracts but much of it was just sitting in the public domain. Acxiom uses that to put you into one of seventy different customer classes based on income, education, and other factors. That’s important, because Acxiom, AT&T, and Verizon can’t sell advertisers access to you specifically; that would be a clear violation of privacy. They sell you as part of a group of people sharing certain characteristics. And no matter what group you are in, they are extremely skilled at finding you. Acxiom knows how many people in every one of its clusters are reachable via mobile phone, browser ad, or television ad at any given moment.23 Let’s say you don’t click an ad when it shows up on your phone or on the Web but you still want the product. You go into the store and buy the item there. Acxiom knows that as well. The ads you see don’t just follow you as you go from site to site, they follow you everywhere. But Acxiom isn’t just selling advertisers access to the people in your cluster, they’re also selling your future decisions.