Use Data Or Be Used By Data!

The October 6 issue of Seotistics is here for you!

There is too much misinformation online, especially on the topics of Analytics and Marketing.

The thing is, some bad advice can be extremely expensive for businesses.

I show you what to avoid and why it's suboptimal!

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📣📣 Important Announcements 📣📣

Join me for FREE on October 9 at 10am EST at MeasureSummit 2025 😎

I will talk about auditing websites with GA4, GSC and more data!

Once again, this is FREE, you don't pay anything unless you want the recordings .

And on October 14 (Vienna), there is also SerpConf:

Similar topic but specific to Ecommerce, with some exclusive treats.

If you can come to Vienna, DM me because I have a special offer for the event I can't share in public.

Be quick though, it's only for one person!

The Stack

There is a lot of anecdotal buzz around data warehouses like BigQuery.

Them: BigQuery is too expensive!

Me: did you test it

Them: no

It's not uncommon from time to time to see people claiming BigQuery has high costs.

They either didn't use it or tried to do engineering without having the know how.

I talked to a lot of non-savvy BQ users and the mistakes are the same:

not knowing how BQ is priced
not following the best practices for modeling

Once you learn them, it's obvious. If you don't know them, it's a mess.

And this leads to our next point, having in mind an minimum viable stack:

You don't need to wait for permissions to get value out of data.

Sure, just be sure not to create technical debt for later... but it's quite hard to do so with Google tools.

I am on a mission to purify the business world from using Excel and terrible interfaces to run organizations.

This is what kills productivity and efficiency in companies!

Coding

In the past, companies hired "Data Scientists" based on buzzwords and Python knowledge.

You may have guessed right, it didn't end up well and layoffs happened.

Today this is much more reasonable and you need to have a good grasp of SQL.

SQL is the English of data, there is no replacement for it.

Sure, you can avoid learning SQL but you will not be a data professional.

But almost always you will talk and write SQL. Actual data work happens in databases, not on clean Excel files.

In the SEO and PPC community there is an unhealthy attachment to Python that was cool back then...

but not needed in 2025.

I also teach the necessary Python to wrangle data but it's just a tool.

The role of a marketer and/or analyst is not to be good at Python... not if you want to be good, at least.

And most of your time on code should be spent on SQL anyway, not Python.

P.S. The boomer advice of learning some old technology is also bad. Learn what works today!

n8n/Make

n8n/Make are nice tools that are overused and often sold as mandatory...

but to get which jobs exactly?

In most actual use cases you need proper development, not a 3rd-party solution.

There are some valid use cases though, like my friend Simone de Palma shows in his article.

Yep, this is one such case where Make can help you!

The unfiltered truth of Analytics is that boring wins.

Actually, this is also true for business and marketing.

What makes you excited is most likely useless to run a business.

You can easily tell how someone is proficient in a role by the tools they use or what they teach 👀

The "newer" stuff has less time to be tested, which means you are getting taught topics that were barely tested.

"Agile" & Scrum

Maybe you are used to it because your background is either Software Engineering or Product...

but this is the anti-thesis of Analytics.

It makes everything so complex and hard for no reason.

There must be a reason for what you do.

I think Agile and Scrum can work well with Data Engineering too but it's a real struggle for other disciplines.

LLMs

Yes, this topic can't absolutely miss from my list.

Vendors and some bad agencies are trying in all ways to push AI just to make more money.

Rather than starting from the problem and how it can be solved, they do it the other way.

This is perfect if you want to fail and waste your budget.

If you don't have the following in your company:

where to store your data
proper tagging/implementation
clear processes

then don't you even bother with other topics, really.

Topics like the ones I teach in my course "Think Like a Web Analyst" 😎:

Become a Top Data Analyst Now!

My best use case for LLMs today is about structuring chaos like text and write code.

Brainstormng and coding are a good chunk of what I do and Claude massively helps.

Guess what, now even Google Colab and BigQuery have Gemini included so this is great for us!

Big Data

Back in 2017, this was the equivalent of crypto and NFTs but for college.

I saw this topic mentioned everywhere and even had to read and study material on it.

Turns up the prediction of data growing exponentially was completely wrong.

In web, we deal with large datasets, although we don't exactly need drastic measures.

Now the topic isn't as cool and mainstream as before but it still lingers.

If you have followed my past content, you know that in most cases the data we work with doesn't have to be larger.

That's sampling at work:

Yes, I had multiple discussions with different people about the accuracy of web data and how much is needed.

The best way to think about is simple: which decisions do you need to make?

The Tactical Trap

It's no secret that businesses want the best tracking, server-side tagging, advanced consent mode and then do nothing with the data.

Once again, the problem is in the lack of framing.

You need to first figure out what you are trying to solve and THEN use data (as said in the LLMs section).

Otherwise, you will build a lot of cool stuff that is not needed.

Mind you, this doesn't mean technical skills are optional or secondary, no!!!

You need both technical and business skills, otherwise you are all talk and no action.

But before action should come thought 🗿

The Exports & Modeling

You know I love Google exports to BigQuery, they did something revolutionary for the digital industry.

However... many companies stop there.

Data isn't supposed to be used how it's given to you.

Since data is just another way of encoding how your business works, it must be adapted.

This brings us to the topic of data modeling, which I often mention as crucial because it is:

Luckily, not many actually think you don't need to modify the exports but still!

If you want some great tools as always, go check GA4Dataform and PipedOut.

This topic is where I am spending a lot of time because is where companies struggle.

Measure of Visibility

No folks, there aren't new ways to measure visibility unbeknownst to marketers.

Most just discovered branding and visibility.

This topic has always been huge, especially if you are a content lover like me.

Clicks and CTR are quite limited as SEO measures but they are the only ones in GSC lol

Impressions and Avg. Position are volatile and questionable interpretation-wise.

Branded searches have been treated like second-class queries for years... now people are starting to re-evaluate them.

Yeah measure your branded vs non-branded performance, always.

OK but then what to measure?

Adopt multiple measures and expand your horizons so that you don't exclude anything.

Don't use SEO data only but expand your data sources.

Oh, and naturally don't you think that visibility or AI traffic is different from reach and normal traffic.

Those are just subtypes.

I fully reject the hogwash of AI metrics and other fictional stuff based on patents because no one cares in a business.

What we have today is already enough but many scratch the surface.

My next issue will dig deeper into this topic because I have no space here!

👥 Join Our Community

Our Discord community offers a small place where we can talk business and web data.

If you hate all the noise of social media, then this place is for you.

I will start posting more there as we have a forum channel now.

This is the best way to stay updated in real time on Seotistics:

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🔎 Analytics For SEO Ebook - Course / Ebook

You will:

✅ Use GSC and GA4 Data to their fullest potential

✅ Learn Python/SQL for your needs

✅ Get a complete blueprint for auditing websites

✅ Learn how to 10x your productivity

✅ Learn BigQuery to work on large websites

I teach you what's needed to go from 0 to a professional Data Analyst.

Even if you leave SEO, the foundations are the same for other jobs!

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Also in ebook:

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📚 Recommended Reads - Peak Content 🗻

Read this peak content:

I am working on more content and product updates, will publish once I have some time to breathe.

As usual, my most recent LinkedIn content is here.

❗️ Feedback and Recommendations

If you have ideas/recommendations for the next issues of Seotistics, you can simply reply to this email.

Marco Giordano
Data/Web Analyst

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Bernerstrasse Süd 169, Zurich, Switzerland
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Analytics Advice That Loses Money