Each and every year we're humbled by the additions we're able to offer to the SEO industry's understanding of the "search environment." Whether it be a deep dive into a Google algorithm update or a case study on Google's use of SERP features, we feel honored to offer a bit of insight into how Google is treating the SERP and so forth. With the year coming to a close, here's a review of the original research Rank Ranger has brought to the forefront of SEO consciousness.
There's this sentiment that rank has become increasingly more volatile. To a large extent, we've chalked this up to machine learning, or RankBrain in specific. That said, what does the current ranking landscape actually look like? How does it differ than the allegedly more stable past? What new ranking dynamics has machine learning left us with? What can we do about them?
Google's early August update was one the most drastic changes I've ever had the 'pleasure' of studying. No site within any niche was safe. Not even the top results on the page could withstand its algorithmic inertia. Amidst the waves of rank fluctuations, a peculiar site pattern emerged, one that had us wondering...
Can Google now profile your site?
What's Google showing in the new desktop carousel? Where do the carousel's YouTube URLs rank within YouTube itself? Is the SERP getting the best YouTube has to offer? Can you optimize for YouTube and the desktop video carousel simultaneously? Does top spot YouTube placement guarantee carousel priority?
I took 155 video carousel keywords and compared the rank of the top 6 cards in each carousel to the URLs rank inside of YouTube - these are my results.
I don't think I need to say that Google's August 2018 broad core update was a big deal. You can judge an update by the buzz it gets... and this update was thunderous. Of course, anyone and everyone
is grasping at straws trying to 'figure the update out'. How impactful was the update? Which sites did it hit? What kind of sites were affected? While I can't definitively explain the update (Can anyone?), I did some digging and came back with some small trinkets of algorithmic treasure. Here's what I found.
Have you felt it? Google's SERP features have bulked up and have moved from being a concern to sites ranking organically to being a competitive juggernaut that every SEO needs to constantly consider. Now, the search engine is going all-in with a new tactic: hybrid SERP features that combine elements from multiple features (so as to better hone in on a legion of different user intents). At the same time, it feels like Google is using its "traditional" features to offer a more powerful SERP feature punch these days.
Let's take a walk through the SERP as I've seen it and see what's perhaps going on.
The recent past has been an adventurous snapshot in time on the Google SERP. There seems to have been a shift in how Google uses its SERP features on a variety of levels. Due to the dynamism of these adjustments, I don't think we as an industry have fully been able to define the current construct, both in terms of the actual changes that have been made, and in terms of the unifying elements that make them a part of Google's larger strategy. With that, I'd like to present one of the many missing pieces of this puzzle, a new bidding system for Google's Local Pack and Featured Snippets.
Learn how machine learning changes the rules of the game for ranking on page one of the Google SERP. As Google becomes better at understanding intent, Google's machine learning properties have a greater impact on ranking itself and how we go about the optimization process. Get insight on how to identify the way Google sees user intent. At the same time, you'll better understand the role of niche ranking factor studies, and how to go beyond them with query specific analysis.
Two words can either make or break your day... Featured Snippets. For those that score them with consistency the SERP feature is a godsend, for those that don't... well that's another story entirely. In either case, combining the position zero boxes with yet another two-word term... machine learning... might send some of us into a tizzy. Yet, tizzies aside, that's precisely what I intend to do as I strongly believe that Google's machine learning properties are touching Featured Snippets in all new ways.
It's funny what you start seeing when you look at enough Local Packs. Stare at enough of them and you'll notice some interesting patterns that highlight Google's emphasis on search location within its local SEO algorithm. What happens however when this pattern is perhaps too prevalent? Is Google over-relying on search location when showing Local Pack results? We'll take a look at a Local Pack listing pattern that has not been previously discussed and delve into the implications.
How have the how major eCommerce sites fared on the Google SERP over the long haul? Rumor has it that some of the biggest players in online retail have suffered majorly in the rankings in the recent past. A notion, that as it turned out, is quite true
.... Let's have a look at the data for e-commerce and all-around retail juggernauts, Amazon, Walmart, and eBay.
Throughout 2017 we reported on what must have been nearly a dozen major SERP feature increases or decreases. These near-constant SERP feature gains and losses piqued my curiosity and made me wonder, just how stable are some of the most important features on the SERP? Is the perception that many features undergo significant fluctuations accurate? Just how volatile are SERP features likes Featured Snippets, Knowledge Panels, Local Packs, and AMP?