Thread by @GiordMarco96

A handful of lessons I learned (and I am still learning) while trying to apply #DataScience to #SEO. Some of them are not so obvious either.

This is an updated thread with new personal considerations 🧵

Communication is hard and you will get mad a lot of times. Non-technical people have no clue what you are talking about and you have to educate them.

Easier said than done, but I think that you should stay strong and keep trying.

Data quality is all. In SEO it's way harder as you are working with estimates and you don't even know the original data distribution.

That is why I am very careful when using Machine Learning models for SEO.

Now I'm getting more used to NLP tho.

SEO is challenging, now imagine explaining bits of #DataScience and SEO together to non-technical people.

Mastering the art of communication is a must, while keeping ethics into account.

That's why I recommend learning how to use PPT (no joke) or Canva to create infographics.

Visualization is your way to go for most things. People don't understand the impact of your work?

Plot it and show them your progress visually. That's why something boring like query count can be shown as something magnificent.

You don't need Deep Learning to solve simple tasks. You don't even need it for most SEO tasks either, barring a few exceptions.

Basic data analysis and plotting are way more useful than you think.

This is especially true for tabular data.

There will be moments where you will use DL models, that's true. In general, you will mostly rely on basic data manipulation and plotting, plus some NLP models.

If you have to learn something focus on what creates the most impact.

Mentally prepare yourself for hearing something like "OK, now can you do it in Excel?" when you are working in Python/R or whatever programming language.

After some months of work, I am finally getting less and less of this.

You have to be very careful when you define KPIs and metrics. A blog will have a different business model compared to e-commerce, implying different success metrics as well.

Even different sections of a website can have different goals/intents.

Google APIs are your best friend. Imho GSC API alone is a goldmine of information and if you have a medium-sized website you already have enough material to create crazy stuff.

Create some basic workflows and you are fine for most of your tasks.

It takes much more time to combine 2 disciplines. Consistency is key and the results will be much higher if you think about the long term.

I don't suggest this path to anyone, it's just for those who have an interest in data.

The king of data is SQL. This one is mandatory and should be improved over time.

I am trying to learn BigQuery as of now and guess what? It's a Google product

You don't often hear of SQL in the SEO community but you should learn it.

Most times you'll be frustrated because you can't use the cool and shiny scripts that you created.

It's normal, you have to think about value first, to avoid being disappointed.

Set lower expectations and try to create milestones for your projects.

Most data sources for SEO coming from tools can be considered "clean" compared to what you see in other industries.

We are so lucky to have tools with CSV exports or an API for everything. The downside is having inaccurate data.

Either way, data cleaning > all the rest

#NLP is a great friend for SEO. You have so many options to find cool insights with small scripts.

For more details:

https://twitter.com/GiordMarco96/status/1499842120068177920

There is a lot of hype around data, it will eventually fade away.

That is why you have to learn the basics! Most people will probably focus on buzzwords, you should do the opposite instead.

Look for momentum and strategy. >>>

>>> Let me explain, strategy as a whole is underrated. Everything is strategy. If the mass is going in one direction you should question why they are doing like that.

Don't go where there is fierce competition, differentiate yourself.

If you want to go all-in consider studying Statistics or Data Science. Beware, your focus should be on creating value first, your client/employer/whoever pays you doesn't care at all!

This is a shocking truth for people with a strong academic background.

You don't need to learn super-advanced concepts, understanding the key ideas is fine.

And I will be blunt, most concepts are so theoretical you will never use them. For SEO you can get away with knowing the essential, as long as you make good use of it.

If you're an in-house SEO try to convince your company to have a pipeline or at least a data storage. If you don't, expect to have to deal with tons of manual work and checks.

Scaling SEO processes requires a company to have some idea of how to deal with data.

I am continuing to study and summarize patents, some technical knowledge is required to be faster when skimming.

Data Science and Machine Learning are different subjects but they are strongly related. Improving your knowledge in both is highly beneficial.

Graphs are not new, even though their applications have surged in the last decade. For SEO we talk about Knowledge Graphs, one typology involved in representing knowledge.

There are other types like social networks/graphs

I am noticing that graphs are super valuable and will probably become more and more important due to their versatility.

Web 3.0 is another factor to take into account and you should already get used to this concept.

The more time I spend with data, the more I understand communication and persuasion are important.

This is something that isn't always taught in unis. Try to have in mind what your audience would like to have as an output.

You can start by replicating what you do in Excel/GSheets in Python. It's easier this way and you can see the benefits or the pitfalls of a programming language.

This is the best exercise to start your conversion to coding.

Most people think that coding is impossible and non-technical people should stick with Excel. I disagree because things will certainly change.

New generations are studying newer subjects that weren't even taught when I was in high school.

Data have impacted SEO in different ways. One example is provided by traditional tools including search intent and other suggestions based on ML.

Moreover, we have seen the rise of newer SaaS solutions based on clustering or knowledge graphs and entities.

Data analysis is one thing, automation is another. I prefer the former over the latter because we SEOs have the right domain knowledge to understand the data.

This is not true for Data Scientists without SEO experience.

Speaking of which, automation doesn't require the same amount of knowledge as analysis.

Automation is a powerful ally to save time and invest more into other activities. It's dangerous to claim that everything can be automated.

One example is given by toxic links reports.

This is a recurring topic in the SEO community and I don't think you can use tools for such a delicate process.

Sure, you can compare values across 3 or more software but what for? Disavowing is not so good as many claims to be

You can automate some steps of Keyword Research or even reporting.

You cannot automate something that requires strategy or tactics. I mean, you can try to a certain extent but you will notice that it's not always the best outcome.

Even though we are learning more and more IT skills, companies have still problems finding talented individuals that can help them.

Learning Python/R/SQL or even Power BI or Tableau will pay off!

Not all SEOs need to choose this path, I am saying this again. There are so many opportunities and markets that it would be stupid to tell you all to do the same things.

Pursue what you like and find job market gaps.

Recently, Data Engineering and cloud-related skills have increased in demand. If you want to try something different, go for it!

SEO and Data Engineering may seem distant but, as mentioned before, companies need to create pipelines or understand how to manage data.

You can take it to the next level and focus on market research for products or industries and not just for SEO.

This is very niche and just for a few selected people who have a super solid background. I have seen some professionals doing that, it's very rare.

For instance, let's say you want to launch a new app. You can do extensive Keyword Research but what if you were able to combine it with market research?

This is my personal opinion, take it with a grain of salt. Costs could outweigh the benefits.

Working with data may get super expensive and some projects are not worth it. Be sure to assess whether you have such a budget and if the problem lies elsewhere.

Imho the problem with most websites is a strategy and not even SEO.

The strategy problem will be covered in another thread. Data cannot save you if you are not able to find a good use case.

So the end goal here is understanding how to leverage what you have.

Imho, the situation can be summarized as follows:

- You get insights, you fail to act on them
- You misunderstood the problem
- You don't even have the data

... and much more! That's why I discourage working with data if you don't understand why you need them.

The strategy will always be the most important factor, followed by tactics.

What is your value proposition? Can you enhance it with the help of data?

It isn't always possible!

Next threads will focus on non-tangible skills. Please, don't ignore the fact that strategy plays a vital role in SEO.

This is the difference between having a plan and shaping a project and getting carried away. You should be proactive and not reactive.

>>>

>>> Methodology is necessary for documenting your steps and defining an effective SEO strategy (and plan).

This is a good method to fight imposter syndrome and understand where you are lacking.

After this thread you should be able to understand why data are not your top priority without proper thinking.

Methodology and strategy are your friends.