For some recent examples:
Sony, and the hacking breaches.
Target, and the credit card scandals.
That isn't an example of Big Data. That's an example of "using the databases necessary to process transactions."
This is big data (quite a good read, though it meanders at times; I highly recommend reading through it all):
http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?pagewanted=allSo, summary version:
1. Target wanted to influence shoppers to permanently change their habits to shop at Target, rather than their competitors; as is the goal of all companies.
2. Studies show that during periods of great change in your life, you are much more accepting of changes to secondary, unrelated behaviors.
3. Target chooses to target pregnancy in particular, since it is both one of the larges period of change in a consumer's life, and influences a high-value family unit consisting of several individuals, rather than just one. In particular, they target mothers.
4. However, this requires Target to determine who is pregnant
before they give birth, since that is when they are in the period of maximum change, and they want to preempt competitors targeting new mothers using public birth records.
5. Using statistical methods (simple Bayesian Artificial Intelligence methods), Target is able to predict to a high degree of accuracy who is pregnant, many months in advance, and even before they begin to appear visibly pregnant.
6. Target sends out coupon catalogs in the mail; these have been custom tailored to the individual, with coupons for baby supplies and other things pregnant women are likely to be thinking about buying.
7. [BACKLASH] Consumers get creeped out by targeted advertising which reveals that Target knows they are pregnant (many months before they give birth even), in a seeming violation of privacy. Since not many people understand just how easy Bayesian AI methods can extract information from seemingly unrelated data, it seems really creepy because it implies Target has access to their medical records, or similar; which isn't true, but it's the only logical answer to the average consumer.
The example given is quite interesting; a teenage girl receives such ads; the parent comes into a Target store absolutely livid that his daughter was getting pregnancy ads despite being a teenager... The parent later apologizes after finding out that his daughter was actually pregnant.
Target knew about her pregnancy before she actually told anyone about it, based simply on basic Bayesian AI techniques applied to her purchases over the preceding period of time.
8. Target modifies targeted advertising, sending the same coupons for things pregnant women are likely to be thinking about, but interspersed with random unrelated items like lawnmowers, bicycles, ect. And thus, they avoid creeping out consumers while still doing exactly the same things behind the scenes.
Welcome to modern big data marketing.
Because birth records are usually public, the moment a couple have a new baby, they are almost instantaneously barraged with offers and incentives and advertisements from all sorts of companies. Which means that the key is to reach them earlier, before any other retailers know a baby is on the way. Specifically, the marketers said they wanted to send specially designed ads to women in their second trimester, which is when most expectant mothers begin buying all sorts of new things, like prenatal vitamins and maternity clothing. “Can you give us a list?” the marketers asked.
“We knew that if we could identify them in their second trimester, there’s a good chance we could capture them for years,” Pole told me. “As soon as we get them buying diapers from us, they’re going to start buying everything else too. If you’re rushing through the store, looking for bottles, and you pass orange juice, you’ll grab a carton. Oh, and there’s that new DVD I want. Soon, you’ll be buying cereal and paper towels from us, and keep coming back.”
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.
As Pole’s computers crawled through the data, he was 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.
One Target employee I spoke to provided a hypothetical example. Take a fictional Target shopper named Jenny Ward, who is 23, lives in Atlanta and in March bought cocoa-butter lotion, a purse large enough to double as a diaper bag, zinc and magnesium supplements and a bright blue rug. There’s, say, an 87 percent chance that she’s pregnant and that her delivery date is sometime in late August. What’s more, because of the data attached to her Guest ID number, Target knows how to trigger Jenny’s habits. They know that if she receives a coupon via e-mail, it will most likely cue her to buy online. They know that if she receives an ad in the mail on Friday, she frequently uses it on a weekend trip to the store. And they know that if they reward her with a printed receipt that entitles her to a free cup of Starbucks coffee, she’ll use it when she comes back again.
In the past, that knowledge had limited value. After all, Jenny purchased only cleaning supplies at Target, and there were only so many psychological buttons the company could push. But now that she is pregnant, everything is up for grabs. In addition to triggering Jenny’s habits to buy more cleaning products, they can also start including offers for an array of products, some more obvious than others, that a woman at her stage of pregnancy might need.
“If we send someone a catalog and say, ‘Congratulations on your first child!’ and they’ve never told us they’re pregnant, that’s going to make some people uncomfortable,” Pole told me. “We are very conservative about compliance with all privacy laws. But even if you’re following the law, you can do things where people get queasy.”
Using data to predict a woman’s pregnancy, Target realized soon after Pole perfected his model, could be a public-relations disaster. So the question became: how could they get their advertisements into expectant mothers’ hands without making it appear they were spying on them? How do you take advantage of someone’s habits without letting them know you’re studying their lives?
“We have the capacity to send every customer an ad booklet, specifically designed for them, that says, ‘Here’s everything you bought last week and a coupon for it,’ ” one Target executive told me. “We do that for grocery products all the time.” But for pregnant women, Target’s goal was selling them baby items they didn’t even know they needed yet.
“With the pregnancy products, though, we learned that some women react badly,” the executive said. “Then we started mixing in all these ads for things we knew pregnant women would never buy, so the baby ads looked random. We’d put an ad for a lawn mower next to diapers. We’d put a coupon for wineglasses next to infant clothes. That way, it looked like all the products were chosen by chance.
“And we found out that as long as a pregnant woman thinks she hasn’t been spied on, she’ll use the coupons. She just assumes that everyone else on her block got the same mailer for diapers and cribs. As long as we don’t spook her, it works.”
And the economic results for Target?
Soon after the new ad campaign began, Target’s Mom and Baby sales exploded. The company doesn’t break out figures for specific divisions, but between 2002 — when Pole was hired — and 2010, Target’s revenues grew from $44 billion to $67 billion. In 2005, the company’s president, Gregg Steinhafel, boasted to a room of investors about the company’s “heightened focus on items and categories that appeal to specific guest segments such as mom and baby.”
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