C&B Notes

Pricing in the Internet Age

The internet has revolutionized retail across a large swath of categories partially because it promotes vastly superior price discovery and comparison shopping.  These benefits have mostly accrued to the consumer, but e-commerce retailers are also using the data trail from online shopping to try to optimize their sales and profits through superior price discrimination, something that cannot be done nearly as effectively in physical stores.

In retrospect, retailers were slow to mobilize. Even as other corporate functions — logistics, sales-force management — were being given the “moneyball” treatment in the early 2000s with powerful predictive software (and even as airlines had fully weaponized airfares), retail pricing remained more art than science.  In part, this was a function of internal company hierarchy.  Prices were traditionally the purview of the second-most-powerful figure in a retail organization: the head merchant, whose intuitive knack for knowing what to sell, and for how much, was the source of a deep-seated mythos that she was not keen to dispel.  Two developments, though, loosened the head merchant’s hold.

The first was the arrival of data. 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.”…

By the early 2000s, the amount of data collected on retailers’ internet servers had become so massive that it started exerting a gravitational pull.  That’s what triggered the second development: the arrival, en masse, of the practitioners of the dismal science.  This was, in some ways, a curious stampede.  For decades, academic economists had generally been as indifferent to corporations as corporations were to them…

At first, the newcomers were mostly mining existing data for insights.  At eBay, for instance, Tadelis used a log of buyer clicks to estimate how much money one hour of bargain-hunting saved shoppers. (Roughly $15 was the answer.)  Then economists realized that they could go a step further and design experiments that produced data. Carefully controlled experiments not only attempted to divine the shape of a demand curve — which shows just how much of a product people will buy as you keep raising the price, allowing retailers to find the optimal, profit-maximizing figure.  They tried to map how the curve changed hour to hour. (Online purchases peak during weekday office hours, so retailers are commonly advised to raise prices in the morning and lower them in the early evening.)  By the mid-2000s, some economists began wondering whether Big Data could discern every individual’s own personal demand curve — thereby turning the classroom hypothetical of “perfect price discrimination” (a price that’s calibrated precisely to the maximum that you will pay) into an actual possibility.

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This could be seen as the final stage of decay of the old one-price system. What’s replacing it is something that most closely resembles high-frequency trading on Wall Street. Prices are never “set” to begin with in this new world. They can fluctuate hour to hour and even minute to minute — a phenomenon familiar to anyone who has put something in his Amazon cart and been alerted to price changes while it sat there.  A website called camelcamelcamel.com even tracks Amazon prices for specific products and alerts consumers when a price drops below a preset threshold.  The price history for any given item — Classic Twister, for example — looks almost exactly like a stock chart. And as with financial markets, flash glitches happen.  In 2011, Peter A. Lawrence’s The Making of a Fly (paperback edition) was briefly available on Amazon for $23,698,655.93, thanks to an algorithmic price war between two third-party sellers that had run amok.  To understand what happened, it seemed sensible to talk to the man who helped develop the software they were using.

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