Big Data

How Online Retailers Use Big Data to Personalize Their Prices

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Have you visited a website to check the price of an item and wondered, “Am I getting the best deal?” Or has a friend told you she snagged a certain product at a discount, but when you go to purchase it, the price is higher? Or maybe you visited a site at work, then returned later to make a purchase from home only to find that the price changed. 

Turns out, you might be on to something. Researchers from Northwestern University in Boston, Mass., took notice as well. In an ACM study, “Measuring Price Discrimination and Steering on E-commerce Web Sites,” a group of researchers explored the complex and changing world of online pricing and e-commerce sales tactics. 

The price is right … or is it? 

By surveying retail, car rental, and hotel-booking sites, this study reveals how companies use big data to steer consumers towards preferred purchases and customize the prices of these purchases based on factors like computer operating system, mobile use, location and whether the potential buyer is a returning user with a login. 

Overall, the research experiments did not show ubiquitous price steering or price discrimination across all 16 e-commerce sites. But there were enough examples of these practices to give the researchers pause. 

For instance, if a potential buyer is not logged in as a member of popular travel-booking websites Orbitz or CheapTickets, that person may be charged an average of $12 more each night of the stay. 

In another example, researchers found that booking site Travelocity priced hotels for users on Apple’s mobile iOS operating system about $15 lower than those using other operating systems. Researchers hypothesized that the huge growth in mobile users may be the reason for this discount. Travelocity may be trying to incentivize purchases from this growing population of customers. 

This type of personalization is not limited to travel sites. Home Depot shows not only more search result options to iOS users but also actually shows entirely different results to those tethered to a desktop computer. According to the study, “On most days, there is close to zero overlap between the results served to desktop and mobile browsers.” 

Furthermore, the options shown to Home Depot’s mobile users were more expensive products, which effectively encourages those on smartphones to spend more money. 

The researchers concluded that they “found cases of sites altering results based on the users’ OS/browser, account on the site and history of clicked/purchased products.” They also concluded that two travel sites “steer users towards more expensive hotel reservations.” They pointed out that algorithms can be changed at any time; therefore, companies that previously did not engage in these practices may soon test the waters. And we shouldn’t expect a lot of transparency when they do. 

Modern coupon clipping 

I wanted to see for myself whether I could find any evidence of price steering in a smaller e-commerce site, so I decided to do some holiday window shopping at some businesses in my region. York, Pennsylvania’s Apfelbaum, Inc. appears to deliver the same prices to both desktop and mobile users. I found that the same is true for CJ Pony Parts, which is admirable, considering how easy it would be to steer prices on their featured gift recommendations. In fact, I’d expect that these practices would only become more common with the holiday season upon us. 

With the larger e-commerce sites, though, all bets are off. I made a point of checking prices for several products on Amazon.com, and I even compared notes with friends. There’s little doubt now that Amazon makes slight price adjustments on certain items (I noticed it with kitchen appliances, specifically), though discovering the precise criteria they use could be a bit of a challenge with the number of variables in play. 

So, for now, the solution to finding the best options from online retailers and booking sites may be to test the purchase on a variety of platforms. Check it out on a PC at your office that’s running Windows, as well as your Mac back home, your iPhone running iOS, and your friend’s Android or Windows phone. Even better: ask a friend or family member living in another part of the country to check the same product or hotel, since location also factors into the price steering. Sure, it sounds like a lot of work, but this might well become the online version of extreme coupon clipping. 

This approach to savvy online shopping will only become more important as our collective participation in e-commerce grows. Cyber Monday, the designated day for online shopping after the brick-and-mortar hullaballoo on Black Friday, saw a large uptick in 2013, with online shopping rising 21 percent over the previous year. 

Online shopping has grown at a faster rate than in-person retail, but it’s still a relatively small slice of the overall shopping picture. As our online shopping rate continues to increase along with mobile usage and in-app purchases (worldwide, over 5 billion people will have mobile phones by 2017), these pricing strategies could proliferate. 

Pricing transparency 

Perhaps at the heart of this study is the question of transparency. Consumers are accustomed to witnessing price discrimination in the grocery store checkout line every day; if one shopper brought in a coupon for a loaf of bread and another did not, one person would pay more for the bread. But that coupon is public: it’s available to most, if not all, potential consumers. 

In the case of online shopping’s price discrimination and price steering, the discounts are neither transparent nor obvious to consumers. Instead, they are based on algorithms and standards known only to the company. Consumers must be savvy enough to know that these behind-the-scenes price manipulations can occur. 

So there you have it. As an individual, as you do your holiday shopping this year, consider yourself warned. And as a business, consider how you can use big data and the mobile phenomenon to grow your business and revenues. 

Daniel Faris is an independent journalist, blogger and ghostwriter whose work has been published in Forbes and The London School of Economics. Visit his blog, The Byte Beat, for a look at emerging tech trends or join him at Only Slightly Biased for political commentary. Follow him on Twitter. 

 

 

 

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