From The Atlantic:
As christmas approached in 2015, the price of pumpkin-pie spice went wild. It didn’t soar, as an economics textbook might suggest. Nor did it crash. It just started vibrating between two quantum states. Amazon’s price for a one-ounce jar was either $4.49 or $8.99, depending on when you looked. Nearly a year later, as Thanksgiving 2016 approached, the price again began whipsawing between two different points, this time $3.36 and $4.69.
We live in the age of the variable airfare, the surge-priced ride, the pay-what-you-want Radiohead album, and other novel price developments. But what was this? Some weird computer glitch? More like a deliberate glitch, it seems. “It’s most likely a strategy to get more data and test the right price,” Guru Hariharan explained, after I had sketched the pattern on a whiteboard.
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The right price—the one that will extract the most profit from consumers’ wallets—has become the fixation of a large and growing number of quantitative types, many of them economists who have left academia for Silicon Valley. It’s also the preoccupation of Boomerang Commerce, a five-year-old start-up founded by Hariharan, an Amazon alum. He says these sorts of price experiments have become a routine part of finding that right price—and refinding it, because the right price can change by the day or even by the hour. (Amazon says its price changes are not attempts to gather data on customers’ spending habits, but rather to give shoppers the lowest price out there.)
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It may come as a surprise that, in buying a seasonal pie ingredient, you might be participating in a carefully designed social-science experiment. But this is what online comparison shopping hath wrought. Simply put: Our ability to know the price of anything, anytime, anywhere, has given us, the consumers, so much power that retailers—in a desperate effort to regain the upper hand, or at least avoid extinction—are now staring back through the screen. They are comparison shopping us.
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“I don’t think anyone could have predicted how sophisticated these algorithms have become,” says Robert Dolan, a marketing professor at Harvard. “I certainly didn’t.” The price of a can of soda in a vending machine can now vary with the temperature outside. The price of the headphones Google recommends may depend on how budget-conscious your web history shows you to be, one study found. For shoppers, that means price—not the one offered to you right now, but the one offered to you 20 minutes from now, or the one offered to me, or to your neighbor—may become an increasingly unknowable thing. “Many moons ago, there used to be one price for something,” Dolan notes. Now the simplest of questions—what’s the true price of pumpkin-pie spice?—is subject to a Heisenberg level of uncertainty.
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The Quakers—including a New York merchant named Rowland H. Macy—had never believed in setting different prices for different people. Wanamaker, a Presbyterian operating in Quaker Philadelphia, opened his Grand Depot under the principle of “One price to all; no favoritism.” Other merchants saw the practical benefits of Macy’s and Wanamaker’s prix fixe policies. As they staffed up their new department stores, it was expensive to train hundreds of clerks in the art of haggling. Fixed prices offered a measure of predictability to bookkeeping, sped up the sales process, and made possible the proliferation of printed retail ads highlighting a given price for a given good.
Companies like General Motors found an up-front way of recovering some of the lost profit. In the 1920s, GM aligned its various car brands into a finely graduated price hierarchy: “Chevrolet for the hoi polloi,” Fortune magazine put it, “Pontiac … for the poor but proud, Oldsmobile for the comfortable but discreet, Buick for the striving, Cadillac for the rich.” The policy—“a car for every purse and purpose,” GM called it—was a means of customer sorting, but the customers did the sorting themselves.
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Thomas Nagle was teaching economics at the University of Chicago in the early 1980s when, he recalls, the university acquired the data from the grocery chain Jewel’s newly installed checkout scanners. “Everyone was thrilled,” says Nagle, now a senior adviser specializing in pricing at Deloitte. “We’d been relying on all these contrived surveys: ‘Given these options at these prices, what would you do?’ But the real world is not a controlled experiment.”
The Jewel data overturned a lot of what he’d been teaching. For instance, he’d professed that ending prices with .99 or .98, instead of just rounding up to the next dollar, did not boost sales. The practice was merely an artifact, the existing literature said, of an age when owners wanted to force cashiers to open the register to make change, in order to prevent them from pocketing the money from a sale. “It turned out,” Nagle recollects, “that ending prices in .99 wasn’t big for cars and other big-ticket items where you pay a lot of attention. But in the grocery store, the effect was huge!” The effect, now known as “left-digit bias,” had not shown up in lab experiments, because participants, presented with a limited number of decisions, were able to approach every hypothetical purchase like a math problem. But of course in real life, Nagle admits, “if you did that, it would take you all day to go to the grocery store.” Disregarding the digits to the right side of the decimal point lets you get home and make dinner.
Link to the rest at The Atlantic and thanks to Nirmala for the tip.