Have you ever looked up flights or hotels on an app on your phone, only to open your laptop and see different prices?
That’s exactly what happened to me recently. I was using Orbitz’s iPhone app to research a vacation package to New York City. Settling on a hotel, I accessed Orbitz’s website on my laptop to book the package. That’s odd, I thought, realizing that the package on my laptop — identical flights, hotel, room type — was $117 more (6.5% more) than the price on Orbitz’s app. A quick scan found that prices of identical vacation packages often differ between Orbitz’s app and website.
I then did a side-by-side app test of the same package with a friend who was sitting next to me. Her Orbitz app price was $50 (2.8%) more than my app price. Amazingly, Orbitz knew something that I regularly give my friend good-natured grief about: She overpays for almost everything.
When I shared my results with Expedia (the parent company of Orbitz), its spokeswoman explained that the Pricing differences I found between the app and website can be due to the fact that its suppliers allow different prices to be offered to mobile customers as well as members (no fee to join) who are logged in.
With regard to the side-by-side app comparisons, Orbitz attributed the price differences to the A/B tests that it employs or other anomalies that occur when setting millions of prices that regularly change due to dynamic pricing. Orbitz told me that it does not offer different prices based on device, browser type, or number or type of searches.
The bottom line, though, is that based on a few characteristics (app or web, signed in as a member or not), a rudimentary type of Personalized Pricing is occurring: Some customers are receiving different prices than others.
The reason why retailers try to offer a personalized price goes back to the downward sloping demand curve highlighted in Economics 101. This fundamental concept illustrates that, for most products, some customers are willing to pay more than others. To exploit that, pricing managers employ techniques that try to discern — and charge — the exact price that each customer is willing to pay. Outsize profits can be extracted from “top of the demand curve” customers, who value the product highly. Meanwhile, if discounts can be discreetly offered to customers with a lower willingness to pay, additional sales (and profit) are reaped. The result is a more profitable customer base, with some shoppers paying more than others.
Personalized pricing can be found at most auto dealerships. The goal of salespeople is to determine how much each customer is willing to pay for a car through individualized negotiation. Prices are tailored by noting each customer’s characteristics and observing their actions. How shoppers dress, the car they currently drive, and answers to seemingly innocuous questions (Where do you live? What do you do for a living?) provide clues. Salespeople also observe actions, such as the other cars people are looking at and how they behave in negotiations (passive or aggressive). Evaluating each shopper’s characteristics and actions creates a pricing profile. Think of a profile as a polygraph test that suggests the highest amount each shopper will pay.
Web retailers can similarly profile their shoppers. Just as someone’s clothing can provide pricing clues, so can the manner in which a customer accesses an online store. Is a shopper using a laptop, app, desktop, or internet on their smartphone? What operating system are they using? Where are they located? A customer’s actions also provide pricing clues: What other products are they looking at? How many times have they visited the site? Much like car salespeople, Web Retailers can electronically evaluate the characteristics and actions of each shopper to create a profile that generates a personalized price.
A key question is whether personalized pricing, on the web or in-store, is ethical. Efforts to tailor prices may inadvertently lead to unfair results. A study by ProPublica found that the Princeton Review’s strategy of levying different prices based on zip code resulted in Asians being twice as likely to be charged a higher price. In a similar vein, a classic economics study on car negotiation found that the markup on final prices for black women was triple the prices offered to white men.
Whether personalized pricing catches on with web retailers is now up to consumers. Will shoppers be comfortable knowing that the prices they are offered may be higher than those presented to others? Will buyers relish “electronically bargaining” to outwit sellers? Retailers first “negotiate” with each customer by personalizing prices based on their profile. In response, savvy shoppers will “bargain” by checking prices on different devices, clearing caches, using the app, conducting multiple searches, asking friends in different cities to see what price they’re quoted, and so on. Or will they become fed up and steer clear of web retailers that price profile? Amazon is on the record as stating that all of its customers see the same prices — will other retailers be so clear-cut?
As the fate of electronic price profiling shakes out, one issue is clear: It is truly a caveat emptor environment for shoppers who use the web.