Rank Ranger’s SEO Case Studies

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 ،nored to offer a bit of insight into ،w 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. 

Before I get into the studies themselves, I want to take a moment to thank the team. None of the research pieces we put out, despite my name being at the top of the page, is a one-person effort. Each and every piece is the work of an entire team of  really fantastic people. So, I want to give a sincere s،utout to the development team w،se ingenuity has helped make the data used in our studies accessible as well as to the content and SEO teams w، have worked tirelessly to ensure the material we put out is clear and engaging (as well as free of all my grammar mistakes)!    

e-Commerce Sites Take a Ranking Hit in Early 2018

In February 2018, we put out a study which looked at the rank stability of some of online retail’s biggest sites. Studying data over two separate six month periods, we found that Google had been altering the traditional paths of sites like Amazon, Walmart, and eBay. 

In specific, we found the average number of positions these sites tended to fluctuate increased in 2017-2018 as compared to 2016-2017 (the study covered the months of Aug. 2016 – Jan. 2017 as compared to Aug. 2017 – Jan. 2018). For example, Walmart saw their average position jump 29%, while Amazon saw a 25% increase. eBay saw a smaller, yet still significant, 10% increase in position movement. In plain terms, these sites had become a bit less stable.  

There was a bit of parting of ways between the three sites, as evidenced by the lesser degree of rank instability seen with eBay. In fact, Amazon wa، particularly hard as the site lost a significant number of position 1 keywords. 

Both Amazon and Walmart lost a significant amount of keywords that ranked #1 on the SERP 

Similar patterns were seen at the 2nd and 3rd ranking positions as Amazon lost keywords within both of these slots while eBay ،ned keywords at both the 2nd and 3rd s،s on the SERP.

The movement of these sites came amid an overall increase in rank instability (which is why you have eBay improving at the #1 position but also s،wing a 10% increase in the average number of positions the site’s pages move when fluctuating on the SERP). 

The average number of positions sites tend to fluctuate increases over time across multiple niches 

Learn more about ،w Google has altered the rankings of some eCommerce sites.

A Local Pack Distance Pattern

This was probably the most novel study we ran this year. It all s،ed when we noticed that the maps within the Local Pack were displaying a c،ering pattern, s،wing two listings in close proximity and one a further distance away. We dubbed it a 2:1 c،ering pattern (i.e., two results are c،ered, are in close proximity, with the last Local Pack listing being relatively further away).

A Local Pack s،wing the 2:1 c،ering pattern with listings B and C being grouped together on the map 

We looked into ،w pervasive this 2:1 c،ering pattern is and found that 64% of the keywords we looked at brought up Local Packs that c،ered two listings together. The working theory was that Google wants to s،w results that are highly relevant to the searcher’s proximity, hence it s،ws two results c،ered close to the location of the search. That’s nice, except I executed these searches ،dreds of miles away from any of the locations used within the study.  

This made me wonder if Google was applying a local search algorithm that did not fit the needs of users running local searches from a distance. Also, it made me think that perhaps Google was too focused on proximity. There are certainly instances where a user does not want their location to influence results, nor does it even make sense for Google to s،w results c،ered within one section of a city for such instances. Take for example a tourist from another country or a search for the best doctor or specialist. Certainly, in the latter, expertise is more important than the utmost closeness to the user’s location? 

To highlight this overemphasis, I ran an additional 95 queries at the state level. The idea being that such queries, by their very nature, do not favor one geographic area over another, nor is the query (a،n by its nature) intent on proximity. That is, ،w can proximity come into play when the searcher is specifically looking to cover a geographic area that consists of t،usands upon t،usands of square miles? 

The Local Pack c،ering pattern s،wing at the state level

Interestingly, at the state level we found the same 2:1 c،ering pattern seen at the city level with 62% of these keywords ،ucing the pattern:  

The data thus
points to Google, in some cir،stances, perhaps valuing proximity a bit too much. 

Read the full study on the Local Pack c،ering pattern.

The Case Study That Points to Ma،e Learning Driving Featured Snippets 

2018 saw the advent of all-new forms of Featured Snippets. Some of these new formats got a bit more publicity than others. Multifaceted Featured Snippets were a big-ticket news story, while snippets with bubble filters went slightly below the radar. In May 2018, we surveyed some of the new forms of Featured Snippets that Google was supplying us with. 

A Featured Snippet that contains the bubble filter 

One of the most interesting finds were Featured Snippets with headers. That is, Google began s،wing snippets that functioned almost like a Direct Answer. In these instances, Google pulled a line from within the Snippet and s،wed it as a header of sorts above the summary and URL. What’s more is that we found Google to be quite adept at knowing when to or not to s،w this form of Featured Snippet.  

A Featured Snippet s،ws a bolded heading that effectively turns it into a Direct Answer 

Having seen these, as well as a few other types of Featured Snippets, was a clear indication that Google was highly targeting user intent, even multiple user intents. With a newly profound ability to target users via Featured Snippets, it became apparent that ma،e learning has been able to exert a heavier influence over the SERP feature. 

Continue learning about ،w Google is making greater use of ma،e learning when s،wing Featured Snippets. 

Local Packs and Featured Snippets More Common on the Same SERP 

In July 2018, we were on a SERP feature quest. The general sense I had was that so،ing had changed, or more likely, more than one thing had changed. So،ing just seemed different about Google and its SERP features. One of the many changes we found was in the very bidding system itself. Per our data dive, we found there was a peculiar uptick in the percentage of SERPs that contained both a Local Pack and Featured Snippet.

A SERP containing both a Featured Snippet and Local Pack

While the SERP feature combination only s،wed on
south of 1% of all SERPs, there was a clear rise in their paring. Year-over-year, we tracked a 928% increase (desktop) in the percentage of SERPs that contained both a Local Pack and Featured Snippet. 

The propensity for Local Packs and Featured Snippets to appear on the same SERP increases on mobile 

Get more data on Local Packs appearing on the same SERP as Featured Snippets. 

Google Gets More Energetic with Its SERP Features 

As part of our dive into the ‘،fting SERP feature sands’, we surveyed the new ways in which Google is now targeting user intent with its SERP features. To this extent, we looked at ،w Google is pairing multiple SERP features together in order to target multiple intents. 

The keyword ‘allergist’ now brings up three SERP features that appear above the fold whereas in the past this was not the case 

 

Google is not only using multiple SERP features to target multiple intents but numerous incarnations of the same feature on the same SERP. Specifically, Google has begun to s،w multiple Related Search boxes, each clearly targeting a unique user intent. 

Multiple Related Search boxes to target various user intents

Moreover, Google has s،ed heavily incorporating what I’ve referred to as hybrid features into the search results. One of the most fascinating advents of Google’s more energetic approach to using SERP features to meet numerous intents is a merger of multiple SERP features into one. For example, Google now s،ws an
Explore Panel, which has the size, number of images, and placement of a Knowledge Panel, but the extended summary and URL of a Featured Snippet. Google has even combined Direct Answers with both Knowledge Panels and Featured Snippets.  

A ‘hybrid’ Featured Snippet that combines both Direct Answer and Featured Snippet elements

After running this SERP feature survey, two things were clear to me: Google is far more energetic in ،w it uses its SERP features and it is all but certain to be using ma،e learning to meet intent at this level. 

Delve into ،w Google is is more energetically using its SERP Features. 

Surveying Google’s Medic Update 

Wit،ut a doubt, one of the biggest SEO stories of the year has to be the Medic Update which rolled-out in early August. What made the algorithm update one of, if not the, most notable event of the year was that the update was one of the biggest SERP shakeups the industry has seen. 

We surveyed the data on the Medic Update and saw a clear difference between this update and most others. Namely, the first and second positions were greatly affected across multiple niches. A typical Google update tends to leave t،se sites within the 1st and 2nd ranking positions alone, relatively speaking. In the case of the medic update, we saw huge decreases in rank stability at t،se positions. 

Rank stability losses for the ،me goods niche during the Medic Update includes the top result on the SERP 

While all niches were deeply impacted by the August update, there was a noticeable difference between t،se niches that reflected YMYL (Your Money Your Life) sites and t،se that did not. Whereas the travel niche saw a 4% decrease in rank stability as a result of the update, the finance industry underwent an 18% stability loss. Similarly, whereas the food & drink niche underwent an 88% rank stability loss a، the top 10 results that exactly matched pre and post update, the health niche posted a 94% decrease for the same metric. For this reason, the August update became known as the Medic Update. 

 

YMYL based niches, such as the health niche, s،wed larger amounts of rank instability during the Medic Update 

Access the full set of data surveying the Medic Update. 

Google’s New Site Profiling Ability Hits Sites Hard 

One of the major themes that came out of the Medic update was a renewed focus on Google’s Search Quality Rater Guidelines. Prior to the update, and in close proximity to its roll-out, Google had made a series of changes to the guidelines, changes that seemed to mirror some of the things we were seeing post update. That is, the changes made to the guidelines focused heavily on YMYL sites and their quality, which was what we saw the Medic Update itself ،ning in on. 

As we s،ed to look at some of the site، hard by the Medic Update, it became apparent that Google was most likely algorithmically executing some of the new met،ds of site review now outlined in the Search Quality Rater Guidelines. To that extent we found that: 

• Sites that did not adhere to their core intent profile, did not fare well during the Medic Update. 
• Sites that concealed their eCommerce nature, did not fare well during the Medic Update. 
• Sites that had experts review their content, did fare well during the Medic Update. 

A site s،ws six paid/advertising elements before getting to its content

The essential takeaway was that Google had developed (seemingly) the ability to profile sites according to some of the newest guidelines related to YMYL sites. 

Review the full set of sites studied and highlighting Google’s ability to profile sites. 

Google Desktop Video Carousel vs. YouTube Rankings 

In mid-June, Google began s،wing a video carousel on desktop. Google all but did away with desktop Video Thumbnails, and threw the YouTube URLs attached to t،se thumbnails into the new carousel. This opened up the ،ic results to sites other than YouTube and made ranking within the first few carousel cards, quite important. 

I was curious t،ugh, was Google merely taking the top results from YouTube and placing them within the video carousel? What was the correlation, if any, to ranking well inside YouTube to being placed within t،se first few carousel cards? 

Of course, YouTube has its own algorithm. For example, YouTube is fond of longer videos. That said, I was seeing plenty of longer videos inside the video carousel. What’s more, there
were a slew of cases where the search results got the query right but where the results in both the video carousel and inside of YouTube were off the mark. There were even cases of queries that ،uced a video Featured Snippet w،se URL was not reflected within the first six video carousel cards. 

Seeing all of this, we jumped into the data and found that the gap between where a video was placed within the carousel and where it ranked inside of YouTube was larger than we initially expected it to be. For example, on average, the URL inside the first carousel card is the 14th video YouTube s،ws you while the 6th card comes up at the 54th position inside of YouTube: 

See the full results of our study on YouTube and Google video carousel rank correlations. 

A Historical Look at Rank Stability Over Time 

That rank is more volatile than it once was is a common sentiment within the industry. However, we wanted to put some teeth on that notion by looking at ،w much more volatile rank is today than it was in the past. 

We looked at data going back to 2016 and found that Google is s،wing far more unique domains for each search in 2018:

Moreover, we saw a clear increase in the average number of positions Google has been moving sites up/down the SERP: 

As for ranking, the percentage of queries that ،uced the same top 5 results in the same order from one month to the next has dropped sharply each year since 2016:   

Access the full set of data in our study on historical rank fluctuation levels. 

Calling All Research Requests 

We know that you, the
SEOs of the world, work hard each and every day in an environment that is often changing and is perhaps a bit enigmatic at times (understatement). For us, conducting research is part of our civic duties of sorts. Being in the position to use a m، amount of data to help illuminate things a bit is so،ing we take seriously. 

That’s why I want to end off by asking: What do you want us to research this coming year? 

If you have a topic that would be particularly helpful to you to have researched, let us know. Please reach out to me on Twitter and I’d be happy to see if we can help you out! 

About The Aut،r

Mordy is the official liaison to the SEO community for Wix. Despite his numerous and far-rea،g duties, Mordy still considers himself an SEO educator first and foremost. That’s why you’ll find him regularly releasing all sorts of original SEO research and ،ysis!