We are at ClickZ this week reporting some of the most interesting panels. And we start from one of the most popular topic: Google’s updates and penalties
Chris Boggs is first on the stage talking about the history of Google updates as well as the differences between Panda and Penguin updates
Fist Google algorithm was Boston followed by Panda in 2011. Penguin came out a couple of years later.
3 “Ps” of Google updates:
- Panda hitting weak and (nearly) duplicate content
- Penguin hitting unnatural links
- Pigeon hitting local businesses
You can refer to both Google Analytics and Google Webmaster Tools to identify what kind of the penalty hit your site. Google Analytics is good for identifying algorithmic penalties (refer to the actual dates they were introduced and see if you got hit on that specific day). Google Webmaster Tools notify you of any manual penalties.
When analyzing your traffic drop, don’t forget to pay attention to the seasonality, site updates and downtimes, etc which can result in natural traffic spikes and drops without Google’s actions having anything to do with it.
Be proactive (track your backlinks, identify your on-page content issues, monitor your traffic) with identifying possible site issues (which can result in penalties) but don’t go crazy.
Jordan Koene formerly of eBay and now of Search Metrics is on stage next talking about his experience with Google’s updates.
Pigeon is a unique animal because it allows Google to adapt to our behaviors.
Trip Advisor is the biggest Pigeon winner!
Penguin vs Panda:
Penguin = drop of traffic and it’s an isolated event.
Panda = may be a slow decline (not as harsh) + tons of fluctuations (this behavior is also similar to a partial penalty).
What is Google Looking for?
Relevance and Quality
They need human reviewers to tell if they are doing a good job at providing both.
eBay has gone through many updates and filters.
Bottom line: You can be a big or a small site but you should be always looking at your content and making it better
So HOW do You Avoid the Zoo?
- Understand the data (review it constantly)
- Find the right support
- Build in a review process
- Test and learn