How to Analyze Google’s Algorithm: The Math & Skills You Need

Ready to analyze Google's search algorithms and learn all the secrets of ranking? Here's what you will need to do it correctly.

“Do you want to spend money on ads or solve this black box?”

That (rough) question helped to determine the path of my career 10+ years ago into becoming the SEO I am today.

I chose this path because I love working at challenges and looking under the hood for what causes something to happen.

Seeking to solve the answer to life, the universe, and everything given with the help of Google Deep Thought as 42 and then double-checking that I had the right question (spoiler: it’s six times nine) is what excites me with SEO.

And what got me to work on this article was a great discussion on Jeff Ferguson‘s post about whether we had the math to decode Google’s algorithm, and if so, what was it that the industry needed?

The Two Things Needed

So, for those that know me, you won’t be surprised to see that I stand against the view that a basic correlation analysis, even with the use of Spearman’s coefficient, is sufficient for analyzing Google’s algorithm.

Since my 2011 SMX East presentation, I have publicly advocated for the use of multi-linear regressions as the minimum for how one should analyze what matters.

Other advanced statistical methods, be it Machine Learning or Neural Networks, have their role to play.

But for this article, I’m focusing on regressions.

An important caveat to the use of statistical methods is that a tool by itself or tacked on at the end does not in of itself qualify as a good study.

That’s where having the right data analysis skills with SEO experience comes into play.

As seen repeatedly with COVID-19 analyses, just having a data analyst background is not sufficient to claim one can solve challenges in a Medium or Twitter post over epidemiology experts.

And while a few might seem to help provide valuable ideas to share, the predominant majority go without a strong caution with humility allowing misinformation to spread.

Need I remind the industry what happens when hire Local SEO company.

The ‘I’m Not a Statistician, But…’

OK, so what gives me the right to point in the direction of advanced statistics for the studies?

A Master’s in International Relations with an International Economics concentration where I learned Econometrics and got the pleasure of tearing apart Econometrics papers on China’s economy.

There’s a reason why you’ll find me on Twitter tearing apart SEO correlation studies as they come out.

So, Why Regressions?

First and foremost, it’s no longer about analyzing a single measure in isolation.

Instead, it’s around multiple measures that also may interact with each other on what can impact rankings.

That mandates the use of a multi-linear regression at a minimum just on this point alone.

Beyond that, moving away from focusing on single metrics and instead of talking about the multiple factors push SEOs to think more broadly about a comprehensive set of metrics to work on to improve rankings.

And on the flip side, this prioritizes the work as 1,000 metrics may seem daunting, but if 900+ barely move the needle 0.1%, the certainty for which ones to work on speeds up the optimization tasks.

Further, the use of time series with regression analyses (where one analyzes the factors over a set period of time rather than at a specific point) can help smooth out the daily or weekly changes to focus in on the core areas, while providing insight into what major algorithm updates shifted.

And for agencies looking to gain credibility, look to the scientific fields for how they run regression analyses on complicated areas. For example:

  • Sea level rise.
  • Material science for superconductivity.
  • Or, if you want something closer to SEO, what drives organic traffic to retail sites.
  • And while rare, specific submissions for SEO research papers do come up to enter in.

Good Analyzing Skills Matter

Logically, giving someone a tool without the right training doesn’t mean this will automatically lead to good results.

And that’s why having the right inquisitive mindset willing to delve deep (like a power user) and put the data through the ringer will complement the advanced statistical tool.

That mindset will work to determine:

What data to collect.

What has directional quality.

Which to remove before one even begins an analysis.

It’s a fundamental standard that requires some SEO experience especially for recognizing in advance what metrics may be the underlying cause and how to avoid bias around demographics, seasonality, buyer intent, etc.

And having that SEO experience also means the analysis has a better chance for including worthwhile interaction effects to analyze, especially when an isolated optimization may not be seen as spam unless done in conjunction with other tactics. (For example, white text in a large paragraph on a white background without a way for the user to see it)

Furthermore, knowing that Google isn’t using a single monolithic algorithm means any analyses will need to include categories or groups, be it by:

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