Most businesses usually end up picking one price that everyone pays. But sometimes they are aware that the members of a certain group will, on average, pay more. This is why movie theaters charge more to middle-aged customers—at the height of their earning power—than to students or senior citizens and why airlines often charge more to last-minute purchasers. They price discriminate.
Big Data may allow businesses to get substantially better at learning what customers are willing to pay—and thus gouging certain groups of people. Optimal Decisions Group was a pioneer in using data science to predict how much consumers are willing to pay for insurance. How did they do it? They used a methodology that we have previously discussed in this book. They found prior customers most similar to those currently looking to buy insurance—and saw how high a premium they were willing to take on. In other words, they ran a doppelganger search. A doppelganger search is entertaining if it helps us predict whether a baseball player will return to his former greatness. A doppelganger search is great if it helps us cure someone’s disease. But if a doppelganger search helps a corporation extract every last penny from you? That’s not so cool. My spendthrift brother would have a right to complain if he got charged more online than tightwad me.
Gambling is one area in which the ability to zoom in on customers is potentially dangerous. Big casinos are using something like a doppelganger search to better understand their consumers. Their goal? To extract the maximum possible profit—to make sure more of your money goes into their coffers.
Here’s how it works. Every gambler, casinos believe, has a “pain point.” This is the amount of losses that will sufficiently frighten her so that she leaves your casino for an extended period of time. Suppose, for example, that Helen’s “pain point” is $3,000. This means if she loses $3,000, you’ve lost a customer, perhaps for weeks or months. If Helen loses $2,999, she won’t be happy. Who, after all, likes to lose money? But she won’t be so demoralized that she won’t come back tomorrow night.
Imagine for a moment that you are managing a casino. And imagine that Helen has shown up to play the slot machines. What is the optimal outcome? Clearly, you want Helen to get as close as possible to her “pain point” without crossing it. You want her to lose $2,999, enough that you make big profits but not so much that she won’t come back to play again soon.
How can you do this? Well, there are ways to get Helen to stop playing once she has lost a certain amount. You can offer her free meals, for example. Make the offer enticing enough, and she will leave the slots for the food.
But there’s one big challenge with this approach. How do you know Helen’s “pain point”? The problem is, people have different “pain points.” For Helen, it’s $3,000. For John, it might be $2,000. For Ben, it might be $26,000. If you convince Helen to stop gambling when she lost $2,000, you left profits on the table. If you wait too long—after she has lost $3,000—you have lost her for a while. Further, Helen might not want to tell you her pain point. She may not even know what it is herself.
So what do you do? If you have made it this far in the book, you can probably guess the answer. You utilize data science. You learn everything you can about a number of your customers—their age, gender, zip code, and gambling behavior. And, from that gambling behavior—their winnings, losings, comings, and goings—you estimate their “pain point.”
You gather all the information you know about Helen and find gamblers who are similar to her—her doppelgangers, more or less. Then you figure out how much pain they can withstand. It’s probably the same amount as Helen. Indeed, this is what the casino Harrah’s does, utilizing a Big Data warehouse firm, Terabyte, to assist them.
Scott Gnau, general manager of Terabyte, explains, in the excellent book Super Crunchers, what casino managers do when they see a regular customer nearing their pain point: “They come out and say, ‘I see you’re having a rough day. I know you like our steakhouse. Here, I’d like you to take your wife to dinner on us right now.’ ”
This might seem the height of generosity: a free steak dinner. But really it’s self-serving. The casino is just trying to get customers to quit before they lose so much that they’ll leave for an extended period of time. In other words, management is using sophisticated data analysis to try to extract as much money from customers, over the long term, as it can.
We have a right to fear that better and better use of online data will give casinos, insurance companies, lenders, and other corporate entities too much power over us.
On the other hand, Big Data has also been enabling consumers to score some blows against businesses that overcharge them or deliver shoddy products.
One important weapon is sites, such as Yelp, that publish reviews of restaurants and other services. A recent study by economist Michael Luca, of Harvard, has shown the extent to which businesses are at the mercy of Yelp reviews. Comparing those reviews to sales data in the state of Washington, he found that one fewer star on Yelp will make a restaurant’s revenues drop 5 to 9 percent.
Consumers are also aided in their struggles with business by comparison shopping sites—like Kayak and Booking.com. As discussed in Freakonomics, when an internet site began reporting the prices different companies were charging for term life insurance, these prices fell dramatically. If an insurance company was overcharging, customers would know it and use someone else. The total savings to consumers? One billion dollars per year.
Data on the internet, in other words, can tell businesses which customers to avoid and which they can exploit. It can also tell customers the businesses they should avoid and who is trying to exploit them. Big Data to date has helped both sides in the struggle between consumers and corporations. We have to make sure it remains a fair fight.
THE DANGER OF EMPOWERED GOVERNMENTS
When her ex-boyfriend showed up at a birthday party, Adriana Donato knew he was upset. She knew that he was mad. She knew that he had struggled with depression. As he invited her for a drive, there was one thing Donato, a twenty-year-old zoology student, did not know. She did not know her ex-boyfriend, twenty-two-year-old James Stoneham, had spent the previous three weeks searching for information on how to murder somebody and about murder law, mixed in with the occasional search about Donato.
If she had known this, presumably she would not have gotten in the car. Presumably, she would not have been stabbed to death that evening.