Personal Pricing, Like Really Personal
This article was originally published in the Weekly Wonk.
The National Retail Federation held its annual convention in New York this week, drawing thousands of industry leaders keen to figure out how they’ll make margins in a world of hyper-informed consumers, evolving purchasing habits, and Amazon, whose formidable size gives it a unique power they cannot match. The “Big Show” featured all sorts of gadgets like touch-screen ads that let you customize outfits before trying them on, and IBM’s Watson-turned salesman that helps you cull a shopping basket when you ask it, “What do I need to take on a two-week backpacking trip through Patagonia?”
Less glitzy but more important than how retailers will jazz up consumer experience is how they’ll use big data to reconfigure pricing.
There are two components to this. One is how businesses price compete, especially with Amazon. (Although I learned that retailers don’t really talk about how to compete with Amazon so much as how not to be decimated by it. A presentation focused on strategies for living in an Amazon world offered as its top recommendation: “If you can’t beat them, join them.”) On this front, data analytics companies are creating profiles tracking how competitors adjust pricing and giving retailers a greater suite of options for responding in real-time. The trend is away from algorithms that automatically chase others’ prices and towards competing more sophisticatedly on a more select batch of products.
“Retailers learned the race-to-the-bottom model just wasn’t sustainable,” said Barry Sexton, vice president of operations for 360pi, which tracks competitor pricing and helps businesses create internal “rules” for how to respond to price changes. So now when Amazon drops the price on a Nikon camera, Best Buy might discount a comparable Canon instead – or decide it has enough of an edge in other lines to sit back until Amazon slashes prices further.
The other major component is how businesses will harness our data trails to personalize pricing. “Retailers are moving away from low prices to relevant prices,” said Punit Kulkarni, director of marketing at Symphony Analytics. What’s “relevant?” A discount tailored to the purchase you’re about to make. To gauge this retailers are investing in “predictive analytics,” which combines a smarter read of your purchasing history with real-time analysis of what you’re seeking to buy. In-store cameras that track which products you’re examining means Kroger can send you a coupon for chips as you’re still roaming the salsa aisle. Or if you buy cat food towards the end of the month, you’ll be e-mailed a special offer on the 25th, rather than showered with ads all the time. The hope is consumers will find the utility outweighs the creepiness.
By “relevant pricing” companies not only mean targeting you with the right coupon at the right time. The Holy Grail for retailers, of course, is the ability to charge each person according to his or her exact willingness to pay, so that my Burberry-dressed neighbor pays more for the same product than my thrifty neighbor does. The reams of personal data now available enable retailers to differentiate prices approaching this level (known as first-degree price discrimination), and one study found that some retailers already modify prices according to customers’ browsing paths, which can reveal if someone is budget-conscious. (In 2012 a Wall Street Journal investigation also found that a stapler on Staples.com cost more if you were browsing from a zip code where there were fewer rival stores.)
For the most part it seems retailers haven’t yet overtly adopted these strategies on a wide scale, partly because customers react badly, if they figure it out. Highly specialized discounts, though, could end up serving the same goal. “Coupons will be the doorway in to differential pricing,” said Scott Anderson, principal consultant at FICO, which provides data analytics and decision-making services. In other words we could all end up paying significantly different amounts – even if we see the same prices while walking or browsing around. The degree to which consumers permit retailers to mine personal data may limit how finely they can tailor discounts, but retailers could also simply charge a premium for opting out. (AT&T’s recent deal to Austin residents does just that: offers a lower rate to subscribers that grant it access to their browsing histories.)
The laws regulating pricing are patchwork. Congress passed laws prohibiting railroads from charging passengers different fares for the same passage in the late 19th century, part of a “common carriage” regime intended to keep gatekeepers to essential infrastructure from using their power to pick winners and losers. It also passed the Robinson-Patman Act in 1936 to prevent big manufacturers from giving favorable terms to big chains over small retailers, as an amendment to antitrust laws. The Justice Department and Federal Trade Commission haven’t enforced Robinson-Patman in decades, and the law primarily regulates transactions between wholesalers and retailers, not to end-consumers.
The technical capabilities and legal permission to charge us different prices for the same goods both exist. If left to retailers and tech companies, when and how we get there is just a matter of time and technique. The real question is whether the public and public officials will submit to this new world.