Understanding Black Friday

  |  December 1, 2009   |  Comments

A Coremetrics analysis of 500 retail sites on Black Friday makes a strong case for customized and targeted landing pages. Here's why.

What seemed like minutes after Black Friday ended, I received a great report titled, "Coremetrics Benchmark Black Friday Report 2009," which analyzed how this year's Black Friday compared to last year's, and found some interesting trends. For those outside of the U.S., Black Friday is known as the biggest single shopping day of the year in the U.S. and falls the day after Thanksgiving. It's known as Black Friday because it can make or break a retailer's year, often pushing them into the black (profit) for the year.

Coremetrics, a Web analytics tool provider, takes data from more than 500 leading U.S. retailers (aggregated and anonymized) to understand general patterns in what it calls "peer-level benchmarking." It has the data across a number of different retail industries and is able to quickly look at changing trends. In its report, it uncovered a few industry trends out of its retail client base.

The most interesting included the following:

  • "The average dollar value that consumers spent per online order rose 35.0 percent year over year, led by apparel retailers." With the current economy, this is surely at the expense of offline sales. It's not surprising that more people are looking for deals, especially on Black Friday, and turning to online sources to find the best promotions.

  • "Consumers are buying more items per order than they did last year -- by 18.3 percent." It would be interesting to understand the "why" behind this. Is it primarily related to the first bullet point? Do consumers want to reduce shipping costs by buying more products from single retailers?

  • "Consumers are spending considerably less time browsing retailers' sites, suggesting they had done their research prior to Black Friday and that they are shopping from lists."
    • "Browsing sessions were down by 5.4 percent."

    • "The number of people who left a site after viewing only one page (also known as a "bounce" rate) was up by 39.4 percent."

    • "Page views per session declined by 30.4 percent."

The last set of bullets indicates that consumers know what they're looking for and enter the site with specific intent, and if they don't find what they're looking for at the right price, they quickly move on. The biggest surprise to me was the jump in the bounce rate from 22 percent to nearly 31 percent. That means that nearly one in three visits to the site, landed on the site and immediately left without viewing more than one page. While that might be a common number for sites with heavy media driven traffic (arguably less qualified), what's surprising is the change from last year.

So now, if you don't generate interest when consumers land on your site they will leave faster than ever. Consumers know they have choices and can very easily find alternatives. If this isn't a good argument for the importance of customized and targeted landing pages, I'm not sure what is.

Getting this data quickly and seeing some of the trends got me thinking about a number of other things:

  • Speed of analysis. Coremetrics turned this report around in less than 12 hours. I wonder how many retailers were able to get this type of comparison of this year versus last year and the differences in behavior by Saturday morning? (Or even at all?) If these trends continue to hold true in the days following Black Friday, what are you as a marketer for a large retailer doing to try to improve your chances of success during the shopping season? Anything? It isn't always easy, but this report does a great job of showing how you can quickly use Web analytics data to understand changing behaviors and potential opportunities.

  • Social impact. I wonder what impact social media has had on all the results, given the explosion of Twitter and Facebook since last year, as well as all the other social sites sharing and ranking Black Friday deals. It would be interesting if Coremetrics could look at that and share some of the findings across the retail industry. But as marketers, we can analyze that based on our own sites by looking at referrers from some of those sites and seeing how those visitors act differently.

  • Importance of relative performance. This is another great example of the importance of benchmarking (Coremetrics Benchmark) and understanding the competitive landscape (Compete, comScore, Hitwise, etc.). The report shared that "Apparel & Jewelry" retailers reported the biggest jump in the average dollar value per online order between 25 to 30 percent. A jewelry retailer might have looked at Black Friday data and been ecstatic that the average order value was up 8 percent from last year. But once they saw the industry data and found that their competitors nearly tripled that increase, they would have a completely different outlook and would want to start finding ways to improve for the rest of the holiday shopping period. You must understand your performance, but in doing so, you need to understand your relative performance.

I'd like to thank Coremetrics for sharing this and for pulling it together so quickly. As mentioned, this is a great example of taking a very important day/campaign/initiative and quickly analyzing what's happening and sharing it. While this was done at a macro level (across hundreds of companies and shared to the public), it's a great example of what retailers everywhere should've been seeing this weekend based on their own companies' performance. But that analysis, of course, would have been more focused on recommendations to improve things going forward.

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Jason Burby

As President of the Americas at POSSIBLE, Jason is responsible for leading the long-term stability and growth of the region. With more than 20 years experience in digital strategy, he is a long-time advocate of using data to inform digital strategies to help clients attract, convert, and retain customers. Jason supports POSSIBLE's clients and employees in driving new engagements and delivering great work that works. He is the co-author of Actionable Web Analytics: Using Data to Make Smart Business Decisions.

Follow him on Twitter @JasonBurby.

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