Search is a highly complex ecosystem.
Any time a user enters a search query, the search engine applies a powerful algorithm to show the pages that best match the query – thus fulfilling the user’s need for information.
But how does the search engine determine which pages to show against a query, and in what order?
In other words, what is behind the algorithms that determine search rankings?
If one was able to crack Google’s algorithm, every search result for every query could be predicted.
Sound like magic?
All it takes is the application of advanced data science to SEO.
Understanding the Complexity of Search Algorithms
Irrespective of the query, search algorithms consider and score multiple attributes across many different parameters to arrive at a single definitive rank.
To be able to produce meaningful search results and rank pages accurately, search engines have to evaluate a myriad of parameters that span across:
- Interpretation of the query
- What is the intent behind the query? What is the user really looking for?
- Content quality and depth
- Does the webpage answer the user’s query clearly and correctly?
- User experience of the page
- Is it easy to find the necessary information?
- Does the page load quickly and offer a seamless experience?
- Expertise, Authority, and Trustworthiness (E-A-T)
- Is the webpage, domain/subdomain considered an authority and expert in the relevant topic?
- Can the information and the domain be trusted?
- Reputation of the brand/domain
Search engine optimization (SEO) emerged to address these issues and ultimately drive gains in search ranking.
In practice, SEO entails adding value to content, improving page quality, and enhancing search friendliness via technical improvements.
Historically, though, SEO has been more of a guessing game than an exact science.
Without being able to understand the key parameters behind search algorithms, SEO practitioners and website owners have struggled to optimize for search on a consistent, replicable basis.
The good news is that it is possible to make SEO predictable.
What this requires, however, is a keen understanding of the challenges inherent to measuring, reporting, and making a case for SEO.
Let’s look at the five most important ones.
Solving for Predictability: Challenges in Identifying & Evaluating Search Parameters
1. The Data Ecosystem Is Heavily Siloed
There are many enterprise SEO tools and browser extensions – both free and paid – that do a good job of reporting on SEO performance metrics such as rank, traffic, and backlinks. For example:
- Technical SEO: Screaming Frog, Google Search Console, Google Analytics.
- Link Research: Ahrefs, Majestic SEO, BuzzSumo.
- Keyword Research: Google Keyword Planner, SEMrush, Ubersuggest, KeywordTool.io.
- SEO Competitive Analysis: Searchmetrics, SEMrush, Ahrefs, BrightEdge.
What these tools fail to do, though, is combine key SEO metrics into a holistic view of search performance.
In the absence of a single “point of truth” for SEO, search professionals must collate data from multiple sources to make meaningful analyses and recommendations.
This requires skill in handling (and interpreting) large datasets which not all SEO practitioners have.
Many SEO professionals, therefore, make decisions intuitively: an approach that works sometimes but can hinder scalable and consistent success.
2. Too Many Metrics, Too Few Insights
Even if one manages to bring all of these data elements together in a single place, it is not humanly possible to sift through them and identify meaningful action items in an objective manner.
Also, not all the attributes will be of equal importance for scoring.
Without addressing these multicollinearity issues, search practitioners risk introducing bias into their analyses and reaching faulty conclusions.