Post Panda Social Engagement Measurements

Since the advent of the Panda update (aka Farmer) people have been scrambling to understand what happened, and how to move forward. Vanessa Fox has provided some great info on Panda as well as the latest on Panda from SMX West. I decided to dig a bit into some of the data on the winners […]

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Since the advent of the Panda update (aka Farmer) people have been scrambling to understand what happened, and how to move forward. Vanessa Fox has provided some great info on Panda as well as the latest on Panda from SMX West. I decided to dig a bit into some of the data on the winners and losers.

My particular focus was on social engagement metrics. To get access to this data on other sites I made use of the Pro level service from Compete.com. This allows me to look at metrics such as time on site, pages per visit, and visits per person.

Unfortunately, bounce rate was not available, but these still felt like three pretty good metrics to examine. I also decided to look at some of the larger content farms to see what the story was with them.

Here is the raw data:

Site Jan Visitors Time on Site Pages Per Visitor Visits Per Person Feb Visitors % Drop Fan Page Likes
ehow.com 43.4M 4:05 2.13 1.79 41.7M 4% 52.4K
howstuffworks.com 6.5M 5:06 6.52 1.36 6.2M 5% 38.3M
wikihow.com 8.5M 3:40 1.86 1.34 7.8M 9% 740K
suite101.com 8.6M 3:04 2.51 1.23 7.4M 14% 10.8K
doityourself.com 2.4M 3:09 1.91 1.22 2.0M 16% 34.8K
associatedcontent.com 14.2M 3:10 2.77 1.30 11.4M 20% 4.6K

What Does The Data Tell Us?

I’ve sorted the data based on the level of drop in the traffic reported by Compete from January to February. First, be aware that Compete.com uses a relatively small sample size of about 1% of US Internet users.

This means there is a fair amount of room for error in the data. For example, other sources such as the winners and losers article cited above indicated that eHow and WikiHow both gained traffic.

However, I still think the data is directionaly meaningful. While there may be some bias in the data due to the nature of the way it is collected, the key is to look at groups of sites that the bias would affect in a similar way, in this case, all high volume article sites.

Here are some interesting observations about what I see in the data above about the 3 sites showing less than a 10% drop in traffic. They had:

  1. The best time on site metrics
  2. The highest visits per person
  3. The highest number of fans for their fan pages

In contrast, the pages per visitor metric does not track the other three. The second and third highest pages per visitor sites were sites that lost 40% or more of their traffic. Nonetheless, in aggregate, my takeaway is still that the social engagement on the losing sites was lower than on the winning sites.

It is likely that the metrics Google is using may include some or all of these, but certainly includes many others, and it is also likely that the signals are being evaluated in aggregate — i.e., no single metric is being used as a signal, but a combination of metrics are being used.

This is important because no single metric provides a good enough signal to act upon, but as a larger group the data quality improves significantly.

How To Move Forward

I have done similar examinations in other market areas, and have seen similar data. When people come to us for help with Panda, one of the first areas we look at is the social engagement metrics for the site.

With analytics, we can examine which portions of the site have the worst metrics. We also look at crawl rate data to see what sections of the site had the biggest decline in crawl rate volume since the Panda update.

In addition, don’t overlook the simple concept of performing an objective review of the content of your site and making determinations as to where the weakest parts are. Each of these exercises can help you identify the areas that users value the least on your site. more advanced tools include ClickTale and AttenionWizard.

Whatever you end up doing, don’t get wrapped up in trying to find the edge of the algorithm.

As Vanessa Fox indicated in this article don’t try to identify a footprint that sites that focus on users have, instead “Focus on users!” Once you get used to thinking this way, it can be more productive.

Here are some of the thing you end up thinking about:

  1. What can we offer our users, that is related to our product or service, that is valuable that no one else does? Or at least very few people do?
  2. How do we present that in an engaging web experience that helps people find the value add quickly?
  3. What steps can we take to test and measure user engagement with my offerings and by website?
  4. If we can’t do the above steps in my current business how can we change it so we can?

Some publishers don’t want to have to work this hard, hopefully most of your competition.

But here is the reality:  you never are going to make tons of money doing only the easy stuff – everyone does that, so where is the differentiation? Focus instead on some of the hard things, and figure out how to make them easy.

That is where the big money is to be found. Doing this is not by itself guaranteed to make you piles of money, but it is something that at least gives you a shot at it.


Opinions expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.


About the author

Eric Enge
Contributor
Eric Enge is President of Pilot Holding. Previously, Eric was the founder and CEO of Stone Temple, an award-winning digital marketing agency, which was acquired by Perficient in July 2018. He is the lead co-author of The Art of SEO, a 900+ page book that’s known in the industry as “the bible of SEO.” In 2016, Enge was awarded Search Engine Land’s Landy Award for Search Marketer of the Year, and US Search Awards Search Personality of the Year. He is a prolific writer, researcher, teacher and a sought-after keynote speaker and panelist at major industry conferences.

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