With the ever-increasing desire of SEO professionals to learn more about Python, there’s never been a better – or more exciting time – to take advantage of machine learning’s (ML) capabilities as well as apply these to SEO. This is particularly true in your competitor research.
The aim of a competitor analysis is to identify your competitors’ strengths as well as weaknesses in comparison to your own in addition to finding a gap in the market.
A competitor analysis is important as:
- It will assist you recognise how you are able to enhance your own business strategy.
- It will tell you how you are able to out-do your competitors in these areas in order to keep your customer attention.
- It will turn out in a competitive edge over others in your sector.
Why Do We Need Machine Learning in SEO Competitor Research?
Most, if not all, SEO professionals working in competitive markets will analyse the search engine results pages (SERPs) and their business competitors in order to find out what it is their website is doing in order to achieve a higher rank.
Back in 2003, we made use of spreadsheets in order to collect data from SERPs, with columns representing the various aspects of the competition such as the number of links to the home page as well as number of pages. In hindsight, the idea was correct however the execution was hopeless owing to the limitations of Excel in doing a statistically robust analysis in the short space of time required.
And if the limits of spreadsheets weren’t sufficient, the landscape has moved on quite a lot since then as we now have:
- Mobile SERPs,
- Social media,
- A much more advanced Google Search experience,
- Page Speed,
- Personalised search,
- Schema, as well as
The above is by no means an extensive list of trends but serves to demonstrate the ever-increasing range of factors which can explain the benefit of your higher-ranked competitors in Google.
What Role Does Machine Learning Play In The SEO Context?
Luckily, with tools such as Python/R, we’re no longer subject to the constraints of spreadsheets. Python/R is able to cope with millions to billions of rows of data. If anything, the constraint is the quality of data which you can feed into your ML model as well as the smart questions you ask of your data.
As an SEO professional, you are able to make the conclusive difference to your SEO campaign by cutting through the noise and utilising machine learning on competitor data in order to discover:
- Which ranking factors can best explain the variances in rankings between websites.
- What the winning benchmark is.
- How much a unit change in the factor is worth in terms of rank.
Like any (data) science endeavour, there are a number of questions to be responded to before we can start coding.
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