The smartphone era has introduced a new list of rules for shopping.
First, find a product and check the Internet to compare the store’s price against that of a nearby competitor or online retailer. If the current location wins, make the purchase. If not, either take the trip across town or press the checkout button on your virtual shopping cart.
There’s little doubt that the instant accessibility of the Internet has helped consumers find the goods they need for the prices they’re willing to pay. However, with Internet searches adding to digital profiles designed to glean as much information about consumer lives and habits as possible, is the best price one that has been predetermined by corporations based on a shopper’s history?
Differential pricing — the economic term for setting different prices for the same product for different customers — has long been a feature of commerce in one form or another. Senior citizens’ and veterans’ discounts, airline tickets and negotiated rates on car lots all fall under the umbrella. Dynamic pricing, in which businesses change prices based on algorithms that take into account competitor pricing, supply and demand, and other external factors, has grown more sophisticated over time.
The catch is that in the past the practice was geared toward specific demographics or in response to broad market conditions. But digital data that tracks nearly every online action could give companies the opportunity someday to track the exact price an individual consumer is willing to pay for a good or service — a form of personalized pricing on steroids.
“The more a merchant knows about you, the more they can predict your maximum willingness to pay for a good,” said Alessandro Acquisti, Carnegie Mellon University professor of information technology and co-director of the university’s Center for Behavioral Research.
“All of the trails of data that you leave as you browse around the Internet are being studied to create a picture of you which can be used not only to show you a certain, particular advertisement, but also to show you a certain, particular price.”
Mr. Acquisti, a renowned privacy researcher who will explore the issue as part of a two-year fellowship with the Carnegie Corporation of New York this year, said he was first drawn to the idea by a paper he wrote with former colleague Hal Varian in 2001 predicting the phenomenon. Since it was published more than a decade ago, there has been exponential growth in dynamic pricing services such as Amazon Prime or loyalty rewards programs that offer discounts to certain customers.
What hasn’t been seen as often is targeted pricing based on purchasing history, even though there’s some evidence it has occurred. A 2012 Wall Street Journal investigation revealed that Staples, Home Depot and several other retailers gave different consumers different prices for the same products based on location data from consumers’ cell phones that indicated how close the consumers were to a competitor’s store.
Differential pricing is not illegal unless the reason for the difference is based on reliance on a category such as race, religion, national origin or gender. The practice could also be illegal if it violates antitrust or price-fixing laws.
But consumers who are aware that haggling is the norm in bricks-and-mortar places such as car dealerships may not be aware that the same thing goes on online.
A car salesperson might figure that a customer in a $3,000 suit driving a 1-year-old Mercedes is a good bet for selling at a higher price, but an Internet retailer can tell by that same customer’s online buying habits with greater accuracy that he’s likely to pay more for an espresso machine than the customer with a history of shopping around for the best price. And while the well-dressed customer can haggle as readily as his grungier counterpart on the sales room floor, it’s harder to bargain with an Amazon shopping cart.
Since consumers don’t regularly compare prices with consumers from across town or across the country buying the same goods, it’s difficult to tell how often the practice actually occurs, said Ali Lange, policy analyst for the Washington, D.C.-based nonprofit Center for Democracy and Technology.
“It’s definitely a concern for consumers. The way companies can assume things about people based on their data is something I don’t think people fully grasp,” she said.
In an attempt to get ahead of the issue, the White House in February issued the report “Big Data and Differential Pricing.” Its ultimate conclusion was that personalized pricing was probably scarce because companies aren’t sure they can target customers’ needs accurately and because of a fear of backlash against the practice.
The report agreed with Mr. Acquisti in one regard: raising awareness of the potential for danger in order to to protect consumer rights.
“Given the speed at which both the technology and business practices are evolving, commercial applications of big data deserve ongoing scrutiny, particularly where companies may be using sensitive information in ways that are not transparent to users and fall outside the boundaries of existing regulatory frameworks.”