Why You Might Pay More Than Your Neighbor for the Same Bottle of Salad Dressing

This article was originally published in Quartz. Image by Mike Blake, Reuters.

Retailers don’t really talk about how to compete with Amazon—they talk about how to avoid being decimated by it.

Up until now, companies have used algorithms to chase competitors’ online prices. But lately, stores are starting to refine their strategies by focusing on a select batch of products. Expansive profiles, created by data analytics companies, that track how competitors adjust pricing give retailers a greater more options when responding in real-time.

“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, at the National Retail Federation convention in New York this week. 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 on other lines to sit back until Amazon slashes prices further.

The other shift is toward personalizing prices.

“Retailers are moving away from low prices to relevant prices,” said Punit Kulkarni, director of marketing at Symphony Analytics.

“Relevant” is 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 track which products you’re examining so that Kroger can send you a coupon for tortilla chips as you’re still roaming the salsa aisle. Or, if you buy cat food toward the end of the month, you’ll be emailed a special offer on the 25th, rather than showered with ads all the time.

Retailers are positioning themselves to charge customers based on their willingness to pay—so that my Burberry-dressed neighbor pays more than my thrifty neighbor for the same product. The reams of personal data now available enable retailers to differentiate prices approaching this level, known as first-degree price discrimination. A 2012 study (pdf) found that some retailers already modify prices according to customers’ browsing paths, which can reveal if someone is budget-conscious, while a Wall Street Journal investigation found Staples modifying prices based on zip codes. The challenge remains translating all the different data points that determine our willingness to pay, into an actual number, but the precision will only increase.

These strategies haven’t been adopted on a wide scale yet, in part, 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 for the same items, even if we see the same prices while browsing. 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: it offers a lower rate to subscribers that grant access to their browsing histories.)

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.