Google search results are ever becoming more relevant and precise. The integration of machine learning (ML) has taken natural language processing (NLP) to a more advanced level, allowing for things like voice search and better algorithm “understanding” of context in search queries.
The advancement of ML has also allowed for the wide adoption of artificial intelligence (AI) for numerous purposes – one such area being SEO. As a result, learning Python has also become increasingly popular among SEO professionals because there’s so much they can do with Python-based ML models to improve their SEO strategies.
This is a saturated eCommerce market, with more and more businesses undergoing digital transformations. This means there has never been a better time to harness technology for SEO competitor research. We’ll take a look at the benefits of ML in SEO competitor analysis and some tips for implementing these strategies in your business.
How Machine Learning Revolutionized Search
Machine learning enables independent learning in computers in a way that relieves them of the need for extensive human intervention. Computers can learn by grouping data with similar properties and making predictions based on past behavior. This is how Google’s ranking algorithm is always updating – and why you need to keep up as an SEO professional.
This algorithm determines the context of a new search query, even if no one in the past has searched it. It can learn the context of unfamiliar searches by pulling semantically similar keywords and comparing them with the current search to deliver a result that seems most relevant.
Using machine learning, search engines like Google find patterns and make sense of relevant data to analyze web pages and user engagement for search engine results page (SERP) listing. The implications of this for keyword research and SERP rankings are that keyword rank is more stable, but the algorithms have become more dynamic.
Hence, machine learning has allowed for the prioritization of engaging and relevant content that predicts higher user interaction for each search term. So, to rank better on SERPs, you need your content to be relevant and targeted to intended audiences.
This makes quality content more important than keyword quantity. If you want to hire a freelance writer for your web content, you can expect to pay at least $25 an hour for a good writer who understands SEO. This will save you time later because you won’t have to edit your content when you audit for SEO – it will already be optimized.
Machine Learning for SEO
A few years back, the ways of collecting data from SERPs were different and do not really align with today’s SEO trends of mobile SERPs, social media, personalized search, etc. All these sophisticated factors have improved user experience (UX) over time with the help of machine learning.
When attempting to apply these trends in SEO analysis, Python can handle a plethora of data, gleaning insights from billions of rows of information. Being an SEO professional, you should explore how to learn Python and how to apply this knowledge to improve the SEO of your business’s website.
Then, you can use machine learning to do competitor research and find out things like:
- Ranking factors that determine the reason for the difference in rankings of competitor websites
- The winning benchmark
- The worth of unit change in the factors in terms of rank
Machine language can overcome many flaws in the previous techniques of competitor analysis. For example, it can categorize, classify and predict very well, so it is useful to improve the quality of your website’s SEO and examine your competitor’s rankings.
Uncover Competitors’ Strategies
You can also use third-party data to analyze the metrics of your competitors using machine learning SEO tools. Once you’ve gathered and cleaned your competitor data, it is ready for remodeling.
You could consider including the following competitor data in your database columns:
You can then train your machine language model, such as a Python-scripted XGBoost model, to work on your dataset. This will help you determine the most influential SERP features ranked for your competitors, in order of importance. You can also see information like a title tag’s relevance to the keyword.
The deliverable rank is based on the importance of a particular factor. For example, if you go one character over the defined length of meta description, the Google rank could sustain a drop of 0.1. So, this is an important thing to know when auditing your technical SEO, as well as in competitor analysis.
ML analysis can also illustrate the given conditions of a certain factor in different industries. For instance, the meta description length for a fashion brand vs. an educational institution might differ. Looking at what your competitors are doing, especially if you’re new to the industry, is a good way to learn these tricks and adapt accordingly.
Automated ML analysis
The functions mentioned can make competitor analysis convenient and far more accurate. Adopting ML in your competitor research is just one way to automate your business processes and take the pressure off your marketers, analysts and SEO professionals.
It would help if you had a continuous and ongoing data collection and analysis stream to get a more holistic picture of the SERPs of your industry. To tackle this challenge, you can use SEO purpose-built data warehouse and dashboard systems.
These systems help you to:
- Combine the data
- Take in data from your favorite SEO tools daily
- Gather insights using ML with a tool of your choice (e.g. Google Data Studio)
You can also deploy a cloud infrastructure to build your own automated system with a process called extract, transform and load (ETL). Extract stands for the daily collection of your SEO from APIs. Transform means the analysis part of the process using ML, and Load is the loading of the finished result of the analysis in your data warehouse. This way, you can automate your data collection, analysis and visualization all in one place for easier, more streamlined competitor research.
Optimize Your Own site for SEO
When building your website, you can also use built-in tools that use AI for SEO. In addition, website building tools like Wix and Shopify have made it simpler to build optimized websites without web development experience. According to online web developer and marketer Nathaniel Finch from Best Web Hosting Australia, ensuring your websites are SEO friendly is much easier than before thanks to easy-to-use web building tools.
“Practically all website builders worth your time and money will come with a number of templates,” says Finch. “These start you on the journey to creating your own website. More templates are almost always better, as they give you more creative options and allow you to really define your website as a unique space compared to the competition. But you should also look at a few examples of templates for a given service before giving them your money. Most builders should show a few example templates somewhere on their sites.”
But even with web builder templates, you should still audit your site for SEO and track multiple metrics to measure how fast it loads, how visitors are interacting with it and so on. One of the things that regression through ML solves is the factors that affect the metric in addition to measuring the metric itself.
Keyword rankings, for example, may change often – during winter people want to buy jackets and hoodies, so the related keywords will appear at the top. However, with machine learning, you can avoid this instability so seasonal changes won’t depreciate the rank of the website.
Regardless of the size of your business, executing SEO competitor research for your website is always a good idea. ML-based SEO research can give you scalable, accurate and precise data to assess your own performance and your competitors’. Then, with this competitor analysis in hand, you can develop a strategy that will help you outperform the competition and reach the top of the SERPs for your industry.