3 Types of Data Science SEO and How They Work

3 Types of Data Science SEO and How They Work

When it comes to successful data science for SEO, nothing is more important than having the right team in place.

Challenges in obtaining and ensuring the consistency of the data, as well as in your choice of machine learning models and in the associated analyses, all benefit from having team members with different skill sets collaborating to solve them.

This article presents the three main types of teams, who is on them, and how they work.

Let’s open the floor with that loneliest of data science SEO professionals — the team of one.

  1. The Solitary Data Science SEO Pro

The one-person team is often the reality in small and large structures alike. There are plenty of versatile people out there who can manage both the SEO and the data functions on their own. The lone data science SEO professional can generally be described as an SEO expert who has decided to take advanced courses in computer science to focus on a more technical side of SEO.

They have mastered at least one programming language (such as R or Python) and use machine learning algorithms. They are closely following Google updates like Rankbrain, BERT, and MUM, as Google has been injecting increasingly more machine learning and AI into its algorithms. These pros must be skilled in the automation of SEO processes to scale their efforts. This might include:

  •     Automatic indexing of new URLs in Bing.
  •     Creation of sitemaps with the new URLs for Google.
  •     Text generation with GPT models.
  •     Anomaly detection in all SEO reports.
  •     Prediction of long-tail traffic.

In my organisation, we share these SEO use cases in the form of a Jupyter Notebook. However, it is easy to automate them using Papermill or DeepNote (which now offers an automatic mode to launch Jupyter Notebooks regularly) in order to run them daily.

If you want to mix it up and enhance your professional value, there are excellent training courses for SEO enthusiasts to learn data science — and conversely, for data scientists to learn SEO, as well. The only limit is your motivation to learn new things.

Some prefer working alone; after all, it eliminates any of the bureaucracy or politics you might (but don’t necessarily have to) find in larger teams.

But as the proverb goes: “Alone we go faster; together we go further.”

Even if projects are completed quickly, they may end up as successful as they could have had there been a wider range of skills and experience at the table.

Now, let’s leave the solitary SEO and move on to teams of two people.

  1. The Data Science SEO MVT (Minimum Viable Team)

You may already know MVP as a Minimum Viable Product. This format is widely used in agile methods where the project starts with a prototype that evolves in one- to three-week iterations.

The MVT is the equivalent for a team. This team structure can help minimize the risks and costs of the project even while bringing more diverse perspectives to the table.

It consists of creating a team with only two members with complementary skill sets — often an SEO expert who also understands the mechanisms of machine learning, and a developer who tests ideas. The team is formed for a limited period of time; typically about 6 weeks.

If we take content categorization for an ecommerce site, for example, often one person will test a method and implement the most efficient one. However, an MVT could perform more complex tests with several models simultaneously — keeping the categorization that comes up the most often and adding image categorization, for example.

This can be done automatically with all existing templates. The current technology makes it possible to reach 95% of correct results, beyond which point the granularity of the results comes into play.

PapersWithCode.com can help you stay up to date with the current state of technology in each field (such as text generation), and will most importantly provide the source code. GPT-3 from OpenAI, for example, can be used for prescriptive SEO to recommend actions for text summarization, text generation, and image generation, all with impressive quality.

Click below to continue to part 2 of The Data Science of SEO.