The Naked Future (16 page)

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!
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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.
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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.
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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.
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“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
.
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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.
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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.
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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.
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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.

In March 2013 the company released a new product: Audience Propensities. A propensity is a prediction, hedged by a probability score, about a specific consumer behavior, such as how a customer will respond to a particular offer. For instance, let's say you have a discounted insurance product and you want to reach only those potential customers who would be extremely unlikely to buy that product at full price. Acxiom executive vice president Phil Mui and his team showed how Acxiom identifies the people with this propensity in a live demo at a product briefing in March 2013. In a manner of minutes the Acxiom system crunched 700 million rows of data and outputted a number. Mui revealed to the audience that if they were looking for someone with that propensity, “there are 275,012 people that you can reach out to.” Mui was careful to point out, “This is live. You can buy this audience
today
.” There are three thousand such propensities Acxiom can model.
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In September 2013, after a spate of unfavorable press and inquiries from the U.S. House of Representatives, Acxiom took a bold move in the right direction and opened a Web site called
AbouttheData.com, to give consumers a partial window into the sort of data the company had on them in its databases, afford consumers the power to make amendments to their profile (these are tracked), and even opt out of being in the Acxiom database. The move was not without risk for Acxiom. As company CEO Scott Howe told
New York Times
reporter Natasha Singer, “What happens if 20 percent of the American population decides to opt out? It would be devastating for our business.”
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The database is a good first stop for anyone looking to better understand how she looks to marketers.

Careful record keeping and a bit of calculation can give you an idea of what companies often refer to as your “lifetime customer value.” But this is only an idea. Past behavior doesn't dictate future results. You may have a cat today, but what if you fall in love with a dog person, or a ferret person? (Don't do that.)

The big data present can give retailers a good understanding of your future buying as your future exists right now, but the naked future is one that's always moving. To gain an understanding of what that movement looks like, you need more than a snapshot; you need to understand how the subject you're observing is evolving, where she goes, what she does, what she encounters. You need to know who she talks to, who can influence her, and whom she can influence.

The days of planting RFID tags in cosmetics are long gone. Such cheap tricks are no longer necessary. Today, consumers give that information away eagerly.

If you were on Facebook sometime between August 14 and October 4, 2010, you probably played a role in an experiment. Facebook turned 253 million users into test subjects to study contagion. No, Mark Zuckerberg didn't release a hostile virus into the New York water supply (yet). The contamination event that the Facebook Data Science Team was monitoring was related to information, specifically URLs and how they spread between people.
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Here's how this experiment worked: You were randomly placed into one of two groups. If you were in the first group, then when one of your friends posted a story you saw it in your News Feed as you normally would. If you were in the second group, the same story would appear far lower down in your News Feed where it was much less likely to be seen.

The objective of the experiment was to examine the probability of a user's sharing a news article, video, or link even if that user didn't know anyone else who had shared it. Facebook's interest in information contagiousness goes beyond curiosity. A customer's friends' Facebook posts are an indicator of—among other things—how likely a customer is to abandon a company or brand or pick up a new habit.

The head of the experiment for Facebook was Eytan Bakshy, one of the star players on the Data Science Team. In person Bakshy seems very young to have such a coveted job, with access to a user base of hundreds of millions of people to experiment on. He bears a strong resemblance to the character of Leonard Hofstadter, the experimental physicist character played by Johnny Galecki on the geek-beloved television show
The Big Bang Theory
, but Bakshy comes off as a bit more serious . . . and a bit smarter.

The Facebook team knew that if they could show the likelihood of a user's sharing an item when none of her friends had shared it, then the team could show how much more likely a user is to share a link that comes to her from someone in her network. And getting people to share information within a growing network is the entire value of Facebook. The ability to prove that the Facebook News Feed and the information shared in it can cause a behavior change is an extremely important aspect of the Facebook business model. Bakshy and his team found that you're 7.37 times more likely to share a link that one of your friends has shared than to share that same article with no social signal.

The experiment also gave Facebook insight into a far more difficult question, one with a more potentially lucrative answer: how
your relationship with different people influences the likelihood that you will like what they like.

When it comes to purchasing behavior, understanding who is influencing whom is a murky question because of a phenomenon called homophily, which is the tendency of similar people to exhibit similar behavior. If you and I both attended a liberal arts college, are of the same income, work in similar professions, and share some other overlapping demographic characteristics, there's a good chance that we'll both post a big article that appears in the
New York Times
to our Facebook page independently of each other. If the article shows up in my News Feed before it shows up in yours, it's not clear that I influenced you to post it. You might have just happened to see the same article that I did later in the day. The Data Science Team's experiment provided a formula for determining who in a network is inspiring who to share what. But the study's most surprising revelation was that the people you're closest to don't necessarily influence your online behavior more than the people you're only nominally friends with, folks with whom you have only a casual off-line relationship if any at all.