People have a mental model of shopping that is based on experiences from brick-and-mortar stores. We intuitively understand how this process works: all available products are displayed around the store and the prices are clearly marked. Many stores offer deals via coupons, membership cards, or to special classes of people such as students or AARP members. Typically, everyone is aware of these discounts and has an equal opportunity to use them.
Many people assume this same mental model of shopping applies just as well to e-commerce websites. However, as we are discovering, this is not the case.
In 2010, shoppers realized that Amazon was charging different users different prices for the same DVD, a practice known as price discrimination or price differentiation. In 2012, the Wall Street Journal revealed that Staples was charging users different prices based on their geographic location. The paper also reported that travel retailer Orbitz was showing more expensive hotels to users browsing from Mac computers, a practice known as price steering.
These reports of price discrimination and steering provoked a great deal of negative publicity for the companies involved. The lack of transparency also raises many disturbing questions. How widespread are the e-commerce practices of manipulating search results and customizing prices? What customer information do companies use to do it? When e-commerce sites personalize prices or search results, by how much do prices change?
Price discrimination and steering in the wild
My colleagues and I at Northeastern University have taken an initial stab at answering these questions in a new study. We examined ten major e-retailers – including Walmart and Home Depot – along with six hotel/rental car sites – including Orbitz and Expedia – to determine if they implement price discrimination or steering, and if so, what user attributes trigger the personalization.
We recruited 300 people from the crowdsourcing site Mechanical Turk to run product searches on the 16 sites. We paired each of these real users, who each had their own real, idiosyncratic browser history, with an automated browser that ran the same searches at the same time as the real users, but did not store any cookies.
By comparing the search results shown to these automated controls and to the real users, we identified several cases of personalization. We saw price steering from Sears, with the order of search results varying from user to user. We saw price discrimination from Home Depot, Sears, Cheaptickets, Orbitz, Priceline, Expedia, and Travelocity, with product prices varying from user to user.
So what user attributes trigger personalization? The problem is that real users have a long history of browsed sites, searches, clicks, and online purchases that we as researchers don’t know. Thus, when we observe personalized results in our experiments, we can’t tease out the underlying cause.
What makes you seem like you want to pay more?
To figure out what user attributes drive e-commerce personalization, we conducted another round of testing using fake accounts that we created. All the accounts were identical except for one specific attribute that we changed. In particular, we tested for personalization based on browser (e.g. Chrome, Firefox, IE), platform (e.g. Windows, OSX, iOS, Android), logging-in to a user account, and purchase history (we had one account book cheap hotels and rental cars for a week, while another account booked expensive hotel rooms and rental cars).
Our fake accounts uncovered many different personalization strategies employed by e-commerce sites. For example, Travelocity reduced the prices on 5% of hotel rooms shown in search results by around US$15 per night for smartphone users. Interestingly, Cheaptickets and Orbitz gave unadvertised “Members Only” discounts of about US$12 per night on 5% of hotels rooms to users who were logged-in to their accounts on the site.
Expedia and Hotels.com conduct what marketers and engineers call A/B tests to steer a subset of their users toward more expensive hotels. By dividing visitors into different groups, companies are able to use A/B tests to see how users respond to new website features and algorithms. In this case, visitors to Expedia and Hotels.com were randomly assigned to groups A, B, or C based on the cookies stored on their computers. Users in groups A and B were shown hotels with an average price of US$187/night, while users in group C were shown hotels with an average price of US$170/night.
Home Depot served almost completely different products to users on desktops versus mobile devices. A desktop user searching Home Depot typically received 24 search results, with an average price per item of US$120. In contrast, mobile users receive 48 search results, with an average price per item of US$230. Bizarrely, products are also US$0.41 more expensive on average for Android users.
Why do sites do this?
Initially, we assumed that the sites would not personalize content, given the extremely negative PR that Amazon, Staples, and Orbitz received when earlier cases were revealed. To our surprise, this was not the case!
Unfortunately, the business logic underlying much of this personalization remains a mystery. None of the discounts we located in our experiments were advertised on sites’ homepages, so the deals do not appear to be part of marketing campaigns. When we spoke to representatives from Orbitz and Expedia, they confirmed our findings, but did not elaborate on the rationale for the design of their websites. Representatives from Travelocity confirmed that they do offer deals for mobile users, with the goal being to motivate them to use the site more and install the Travelocity app.
What’s a bargain-hunting shopper to do?
What is clear from our study is that price discrimination and steering on e-commerce sites is becoming more prevalent, and more sophisticated. As a user, it’s almost impossible to know if the prices you are being shown have been altered, or if cheaper products have been hidden from search results.
If you are looking for the best deals and are willing to work for it, we recommend searching for products in your normal desktop browser, an incognito or private browser window, and your mobile device. Of course, e-commerce companies are constantly experimenting with new personalization techniques, so in the future, an entirely different attribute may trigger personalization.
Ultimately, we hope that our study will encourage companies to be more transparent about how they personalize prices and search results. Rather than using opaque and creepy algorithms to secretly alter content, companies could stick to the kinds of real-world incentives that shoppers already know and love, like coupons and sales.
By Christo Wilson, Northeastern University
Christo Wilson receives funding from the National Science Foundation (NSF) under grants CNS-1054233 and CHS-1408345. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.