<\/p>\n
Contrary to initial speculation, in the latest Google update, Google’s Medic Update has ramifications that go way beyond the health industry and establishing author expertise.<\/p>\n
A year from now we may look back at August 2018 as a pivotal shift in how SEO works, on par with the introduction of Google Panda and Penguin (if less cutely named).<\/p>\n
As more hard data emerges, we’re starting to see a clearer picture of which factors played a role in rankings changes, and how the big Medic Update was about much more than just Expertise, Authority, and Trust.<\/p>\n
A lot of great websites lost rankings after the Medic update. If you’re among those affected and trying to understand why, we hope this list of influential factors will help you focus your search. If you need further help, let me know! Recovery from the Google Medic Update is possible.<\/p>\n
Note: This is an edited version of research we put together for CanIRank Full Service<\/a> clients. Part 1 explained <\/em>What is a broad core algorithm update?<\/strong><\/a>, this is part 2, <\/em>What changed in in the Google Medic Update?<\/strong>, and part 3 (coming soon!) explains <\/em>How to Recover from the Google Medic Algorithm Update<\/strong>.<\/em><\/p>\n
As a team that combines marketers with data scientists and engineers, we believe a key part of an SEO consultant\u2019s job is to help clients understand how Google\u2019s high-level objectives like \u201cmake great content\u201d or \u201cbecome the leading authority in your field\u201d translate into actual machine-readable signals, and how you can improve your content marketing processes to strengthen those signals while still balancing other business objectives.<\/em><\/p>\n
This 3-part series on Google\u2019s latest core algorithm update (\u201cMedic Update\u201d) is our attempt to help elevate the conversation around algorithm updates to more of an evidence-based exploration focused on specific algorithmic signals rather than intangible high-level objectives.<\/em><\/p><\/blockquote>\n
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Now that we have an idea for what Google was trying to accomplish with the Medic Update<\/a>, and how they might have gone about making those changes, we can dive into better understanding how the update changed the importance of various ranking factors.<\/p>\n
In order to identify which algorithmic signals are correlated with gains and losses in Google\u2019s big Medic update, we identified approximately 100 sites that had seen significant changes in one direction or the other. Most of the examples are medium-large sites due to data<\/a> availability, but we found sites impacted of all sizes, business models, and industries, including health, financial services<\/a>, travel, SaaS, coupons, auto, and ecommerce.<\/p>\n
Using CanIRank\u2019s SEO Competitor Analysis tool<\/a>, we analyzed keywords with large fluctuations to see how gaining URLs differed from losing URLs. Based on that preliminary analysis and initial reports in the SEO blogosphere, we came up with a list of 27 potential culprits to analyze further, such as the presence of contact information, quality of an author\u2019s credentials, and link velocity.<\/p>\n
The preliminary statistical findings are based on complete data for about a dozen URLs each from 51 different websites, 33 of which saw significant and sustained declines beginning August 1st, and 18 that saw significant and sustained growth during that period. For the remaining sites analyzed our data is still incomplete, but there were already clear enough patterns at 51 completed that we thought it worth publishing before more businesses waste time and money chasing the red herring of Expertise \/ Authority \/ Trust.<\/p>\n
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- Study Limitations<\/a><\/strong><\/li>\n
- Ranking Factors Associated with Traffic Gains or Losses<\/a><\/strong><\/li>\n
- Other Influential Factors<\/a><\/a><\/strong><\/li>\n
- Factors that didn\u2019t seem to make a difference<\/a><\/strong><\/li>\n<\/ul>\n
<\/div><\/div><\/div><\/div>\nStudy Limitations<\/h2>\n
Apart from the small sample size, the study has a number of additional limitations, and as such should be looked at more as an aggregation of anecdotes than statistically valid research (by comparison, CanIRank\u2019s ranking models are based on analysis of almost 600,000 websites).<\/p>\n
We\u2019re not trying to imply weighting or even utilization of specific signals — our objective is to identify possible areas for further analysis and experimentation so that we can guide our clients (and you!) in how best to take advantage of Google\u2019s new ranking algorithm.<\/p>\n
As we\u2019ve stressed before<\/a>, correlation is a terrible way to gauge the relative impact of specific SEO ranking signals because different URLs rank for different reasons. Each of those 10 blue links on page 1 are playing the game by a different set of rules. Smart SEOs ignore those \u201cGoogle\u2019s 200 Ranking Factors Finally Revealed!\u201d clickbait posts and instead spend a lot of time on competitive analysis to understand which factors matter for the specific keywords they\u2019re interested in<\/i>.<\/p>\n
As applied to diagnosing and reversing traffic declines, this means that there was no single \u201csmoking gun\u201d that caused all the changes we see here (not even something high-level like E-A-T or content quality). Instead, we took the time to understand what the likely culprit was for most of the declining sites (and there was almost always at least one glaring issue).<\/p>\n
For example, SelfHacked.com scored very high on measures of content quality, expertise, and authority, but they have some fundamental technical SEO issues. It\u2019s hard to beat LonelyPlanet for content quality and expertise in the travel industry, but they have some hard to distinguish sponsored content that could be seen as deceptive, as well as thin UGC.<\/p>\n
Situations like that don\u2019t lead to strong correlations for any one factor; that\u2019s the nature of a \u201cbroad\u201d update.<\/p>\n
Some of the factors we looked at were subjectively evaluated by our team of SEO consultants<\/a> using common guidelines similar to Google\u2019s Search Quality Evaluator Guidelines<\/a>. Subjective evaluations are a recipe for bias, but given our team has no particular interest in any one factor \u201cwinning\u201d, at least it\u2019s plausible that we were all inaccurate in a randomly distributed way.<\/a><\/p>\n
Lastly but most importantly, since none of these sites are CanIRank clients, we\u2019re estimating the change in organic search traffic using 3rd-party SEMRush<\/a> data, which unfortunately isn\u2019t entirely accurate, especially for small websites and during times of high volatility. We did source some gainers\/ losers by reading comment threads where webmasters claimed they were impacted, but apart from that we have no knowledge about the organic traffic performance of these websites beyond the publicly-available SEMRush estimates.
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