Uncover the Power of Applied Mathematics in Search, Validating or Challenging Your SEO Approach
The mention of math might conjure up memories of unfinished exams and complex equations. However, the math we’reabout to explore will affirm much of what you intuitively know about SEO.
As SEOs, we often have hunches about factors that influence rankings. You might have noticed that pages with more backlinks rank higher or that faster-loading sites perform better in search results.
In this article, we’ll explore mathematical tools that can validate (or sometimes challenge) these hunches. By the end, you’ll see how these tools can help you separate SEO fact from fiction and boost your confidence in recommending strategies.
The Value of Applied Mathematics in SEO
In the 1985 study “Usefulness of Analogous Solutions for Solving Algebra Word Problems,” researchers found that students often struggled to apply mathematical concepts to similar problems, let alone to real-life situations where these concepts could be beneficial.
This difficulty arises because these concepts are typically learned in isolation. By seeing how these concepts are appliedin specific, real-life contexts, students can begin to recognize more opportunities to use them practically.
Today, by examining these tools in the context of SEO, we can start to identify other SEO scenarios that may benefit from applying mathematical concepts.
At my agency, we apply correlation analysis in several critical areas:
- The Role of Quality vs. Quantity of Referring Domains: We analyze whether the number or the quality of referring domains has a more significant impact in a given industry.
- The Relationship Between Content and Traffic: We determine if the quantity of content is a crucial factor in driving traffic within a specific industry.
- The Importance of Various Ranking Factors in Specific SERP Result Pages: We assess how important different ranking factors, such as referring domains, are to specific search results.
By leveraging these mathematical tools, we can better understand and optimize our SEO strategies, making data-driven decisions that enhance performance.
The Promise and Limitations of Correlation Analysis in SEO
If we’re confident that the Google algorithm has certain ranking features, can we simply use correlation analysis of search results to see their influence?
Like most SEO questions, the answer is “it depends.”
Identifying the role of ranking factors and their importance for a SERP is tricky because different ranking factors may not correspond to rankings in a linear or consistently increasing/decreasing way.
For example, consider the impact of page load speed on rankings. A website might see significant ranking improvements when reducing load time from 10 seconds to three seconds, but further improvements from three seconds to one second might yield diminishing returns.
In this case, the relationship between page speed and rankings isn’t linear — there’s a threshold where the impact becomes less pronounced, making it challenging to accurately assess its importance using simple correlation methods.
Before analyzing specific ranking factors for a SERP, we need to understand the basics of correlation and determine which method will give us the best results for which ranking factors. You’ll quickly learn that even though we use mathematics, domain expertise and our expectations about data play a critical role in using mathematics effectively.
Applying Correlation to SEO Ranking Factors
Using correlation, we can develop a basic ranking heuristic for a given search result. For example, consider a simple formula like this:
Ranking=w1×(Referring Domains)+w2×(Content Length)+w3×(Site Speed)+…Ranking=w1×(Referring Domains)+w2×(Content Length)+w3×(Site Speed)+…
We can start making better guesses about the weights (w1w1, w2w2, w3w3, etc.) of these factors based on correlation analysis. By doing so, we can better understand how different factors influence rankings and refine our SEO strategies accordingly.
The Multitude of Ranking Factors
Google’s algorithm is incredibly complex, with hundreds of ranking factors at play. As SEOs, we often find ourselves trying to decipher which of these factors are the most crucial.
Over time, through a combination of experience, testing, and official Google statements, we typically develop a list of 10-20 factors that we believe are the most impactful.
This list might include elements like:
- Content quality and relevance
- Backlink profile (quantity and quality)
- User experience signals
- Page speed
- Mobile-friendliness
- Keyword usage and optimization
- Content freshness
- SSL security
- Schema markup
While this list isn’t exhaustive, it provides a starting point for our correlation analysis, helping us prioritize and optimize our SEO strategies effectively.
Types of Ranking Factors and What We’d Expect
Let’s dive deeper into how different types of ranking factors might behave in our analysis.
Increasing Factors
These are factors where we generally expect that more is better. For example, with referring domains, we’d typically expect that sites with more high-quality backlinks would rank higher.
Expected Correlation: As the number of referring domains increases, ranking position decreases (improves). We’d see a strong negative correlation if this factor is significant.
Linear Ranking Factors
These factors tend to have a more straightforward relationship with rankings. Content length could be an example here. If it’s a significant factor, we might see a consistent relationship where longer content correlates with better rankings, up to a point.
Expected Correlation: As content length increases, ranking position decreases (improves) in a relatively consistent manner.
Decreasing Ranking Relationships
These are factors where lower values are generally better. Site speed is a classic example. We’d expect faster-loading sites to rank higher.
Expected Correlation: As page load time decreases, ranking position decreases (improves).
Binary Ranking Factors
These are yes/no factors, like whether a site has SSL or not. For these, we might look at the proportion of top-ranking sites that have the factor compared to lower-ranking sites.
Expected Pattern: A higher proportion of top-ranking sites would have the factor compared to lower-ranking sites.
Threshold-Based and Non-Linear Factors
These are perhaps the trickiest to analyze with simple correlation. Keyword density is a good example. If it’s too low, the page might not be seen as relevant. Too high, and it might be seen as keyword stuffing.
Expected Pattern: This might show an “upside-down parabola” shape, where there’s an optimal range for the factor, and values outside that range are less effective.
The Difficulties of Using Correlations
While correlation analysis can be incredibly useful, it comes with several challenges that are crucial to understand.
Factors in Isolation vs. in Tandem
When we examine ranking factors individually, we risk overlooking important interactions between them.
For instance, consider a website with high-quality content but fewer backlinks. It might still outrank a site with more backlinks but lower content quality. This highlights the necessity of looking at multiple factors together to get a truepicture of what influences rankings.
Example of Google Ranking Factors in Parallel
Imagine you are evaluating the impact of various ranking factors on your website’s performance. Let’s say you consider content quality, backlink quantity, and mobile-friendliness. While each of these factors individually contributes to your ranking, their combined effect is what truly matters.
A website that excels in content quality and mobile-friendliness but has fewer backlinks might still perform well due to the synergy between high-quality content and a user-friendly mobile experience.
Overpowering Ranking Factors
It’s also crucial to understand that some ranking factors can greatly overpower others. For example, if a website has an exceptionally high number of authoritative backlinks, this might significantly boost its rankings even if its content quality is moderate.
This dominance can make it challenging to see the impact of smaller factors, such as page load speed. Because the effect of the stronger factor overshadows the weaker one, a site with excellent backlinks might not need to focus as heavily on improving load speed to see ranking improvements.
Quadratic Nonlinear Relationships
Some factors have what we call an “upside-down parabola” shape. Keyword usage is a perfect example. Let’s say we’reanalyzing the keyword density of “best running shoes” in product reviews:
- 0% density: The page likely won’t rank at all for the term.
- 0.5% density: This might be ideal, helping the page rank well.
- 1% density: Still good, maybe ranking slightly lower.
- 2% density: Starting to look like keyword stuffing, rankings drop.
- 5% density: Likely seen as spam, rankings plummet.
Understanding these complexities is vital for accurately interpreting correlation analysis and making informed SEO decisions.
What Is a Strong Correlation in a SERP Result?
Obviously, a 0.99 correlation is great, but given the interplay of so many variables, when should we really take notice of a ranking factor and its importance?
In the messy world of SEO, a 0.99 (or -0.99) correlation would be suspiciously high. More realistically, we should start paying attention to correlations around 0.2 to 0.5, especially if they’re consistent across multiple analyses.
As a result, when correlations emerge in SEO analysis, they tend to be much smaller than we might expect in more straightforward relationships. This doesn’t diminish their importance, however. Even these smaller correlations can provide valuable insights into the factors influencing search rankings, especially when viewed as part of a broader pattern rather than in isolation.
Here’s when you should really take notice:
- Repeatability: If you’re seeing similar correlations for a factor across different keywords, time periods, or industries, it’s more likely to be important.
- Alignment with SEO Knowledge: If the correlation aligns with what we know about SEO best practices or Google’s stated preferences, it’s more likely to be meaningful.
Where Can Correlation Help Beyond Our SEO Intuitions?
Now, you might be thinking, “This is all well and good, but how does it actually help me in the real world? Couldn’t I justeyeball the search results and see the factors that matter?”
Great question! Here are some practical applications where correlation analysis can give us additional insights that gobeyond our gut feelings.
Ruling Out the Influence of Some Factors
Sometimes, what we think matters… doesn’t. For example, you might believe that using exact-match keywords in H2 tags is crucial for ranking. But when you run a correlation analysis, you find no significant relationship between H2 keyword usage and rankings. This doesn’t mean H2 tags are useless, but it suggests they might not be as important as you thought.
Unveiling Industry-Specific Ranking Factors
Correlation analysis can help identify which factors are particularly important in your specific industry. Different niches may have unique ranking factors that are not as influential in others.
Prioritizing SEO Efforts
By identifying which factors have the strongest correlations with higher rankings, you can better prioritize your SEO efforts. This ensures you focus on the elements that will have the most impact.
Measuring the Impact of Algorithm Updates
If you monitor how correlations change with algorithm updates, it can help point out which underlying factors may have changed. This can provide valuable insights into what Google is prioritizing after an update.
While correlation analysis is a useful first step in understanding ranking factors, more advanced techniques can better handle the multivariate nature of ranking factors and the various types of relationships they may have with scoring.
- Regression Analysis: This method helps determine the relative importance of multiple factors simultaneously, providing insights into how each factor contributes to rankings.
- Decision Trees: These models can capture non-linear relationships and interactions between factors, offering a more nuanced understanding of their impact on search rankings.
- Machine Learning at Scale: By combining correlation techniques with machine learning, SEOs can uncover complex patterns across large datasets, revealing deeper insights into ranking dynamics.
Using Correlation Analysis to Inform Your SEO Strategy
Correlation analysis remains a powerful tool for SEOs aiming to understand the relative importance of various ranking factors. However, it’s essential to approach this analysis with a solid grasp of statistical concepts, awareness of its limitations, and strong domain expertise.
By integrating correlation analysis with advanced techniques and always grounding our interpretations in SEO best practices, we can derive actionable insights to enhance our strategies and decision-making processes.
If you still find it all difficult and confusing, check out our monthly SEO packages and let the experts help you.
