Black Friday shopper or Cyber Monday shopper? The biggest difference may not be what you expect

By Jennifer Priestley If you are a Black Friday shopper (and you know who you are), you…

Georgia (Dec 2, 2014) — By Jennifer Priestley


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If you are a Black Friday shopper (and you know who you are), you will likely arrive in the dark, wee hours of Friday morning, shivering with kindred discount warriors, waiting for the doors of your favorite retailer to spring open. Then, those like-minded souls with whom you shared hot coffee and glazed doughnuts will engage you in battle for the same "must have" items at that coveted "low, low" price.

If you are a Cyber Monday shopper (and you know who you are), your experience could not be more different … but for reasons that you might not expect.

The online buying experience has been hyper-personalized – in some cases down to the individual. That is good news, right? Well, maybe. The Black Friday shoppers who arrive by bus, minivan, taxi or luxury car all compete for the same items at the same low price. But the Cyber Monday shopper who "arrived" at a retail site using a Safari browser on an Apple iPad will likely see a different price for the same item than will a shopper who "arrived" at the same site using an Internet Explorer browser on a PC. And not everyone will see a "low, low price." Why?

Put simply, because the online retailers can offer online shoppers the highest price the retailers think they would be willing to pay. And they do.

Using online behavior, including every website that you visit (tracked through "cookies"), your purchases, your abandoned carts, even your social media activity, data scientists working for retailers can, fairly easily, determine how price sensitive you are and determine the "optimal" (read here "highest") price that you would be willing to pay for an item. And whether or not you require free shipping to purchase said item (yep, it's not just the price of the item that varies, it's also the price of the shipping). Some of the factors that contribute to this price modeling process include the device that you use to search and make purchases (e.g., a PC, a tablet, a phone … and the brand of each), the other sites that you've visited prior to "arriving" at the retailer's site (i.e., were you price comparing? Did you click on an advertisement or a coupon?) and the browser that you used (e.g., Safari, Chrome, Internet Explorer).

Another factor that can contribute to the price you see on the screen includes your physical location (which can be determined from your IP address and/or from GPS locator tracking). Why would physical location be important? Consider online shopping with a "Big Box" retailer. If you live in close physical proximity to the retailer, you may see a price that is consistent with the price that would be shown in your local store – consistent with what your Black Friday comrades might see. But if you do not live anywhere near a physical outlet, your online quoted price for a particular item may be dramatically different from what the price would be in the physical store.

Do you tend to only make online purchases when you receive free shipping? Do you respond to online coupons or advertised discounts? Guess what? All of this online shopping behavior is being collected and maintained and contributes to what you eventually pay for your item when shopping online.

But, what is good for the data scientist can also be good for the shopper. Using data to manipulate prices works both ways. Unless you swear off all electronic devices (like that's going to happen), there is not much you can do to completely prevent your online activity from being tracked. However, there are some things you can do to potentially influence (decrease) the price that you see when shopping online:

1. Compare prices for the same item. Few items that you can buy online are only available from one outlet. Comparing prices not only provides you with valuable information, but it establishes an online pattern of price sensitivity that will be factored into the price that you see.

2. Use different browsers. If you typically use Safari, try using Chrome, Firefox or Internet Explorer.

3. Use different hardware devices. If you typically use a MacBook (which has been shown to contribute to higher pricing), try using a friend's PC – but be sure to clear any financial transaction information from any devices you do not control.

4. If possible, only make purchases when discounts are offered and/or free shipping is offered. If you consistently make purchases at full cost with shipping charges, there will be no reason a retailer would incent you with these offers.

5. Consider occasionally abandoning a cart (you did not hear that from me). See what incentives the retailer has in its virtual toolbox to encourage you to come back and complete the purchase. If you do this, pay attention to the ads that will "appear" when you are browsing other sites – you will likely see the items in the abandoned cart in those ads.

Of course, you can always "opt out" of the data-driven pricing game altogether and camp out in the cold dawn of Friday morning to ensure that you are receiving the lowest price. Don't forget the doughnuts.

Jennifer Lewis Priestley, Ph.D., is a professor of statistics and data science and director of the Center for Statistics and Analytical Services in the College of Science and Mathematics at Kennesaw State University.



A leader in innovative teaching and learning, Kennesaw State University offers undergraduate, graduate and doctoral degrees to its nearly 43,000 students. With 11 colleges on two metro Atlanta campuses, Kennesaw State is a member of the University System of Georgia. The university’s vibrant campus culture, diverse population, strong global ties and entrepreneurial spirit draw students from throughout the country and the world. Kennesaw State is a Carnegie-designated doctoral research institution (R2), placing it among an elite group of only 6 percent of U.S. colleges and universities with an R1 or R2 status. For more information, visit