Use Data Or Be Used By Data!

The September 29 issue of Seotistics is here for you!

You often read about crazy stuff on social media like LLMs and automation.

Even some "consultants" or agencies hype cool stuff up for selling.

However, all of this doesn't matter without the good data basics.

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📣📣 Important Announcements 📣📣

Architecture & Storage

Storing data is CHEAP.

Don't listen to the fake experts on social media telling you that BigQuery is expensive.

Test it yourself or use the Google calculator.

The only thing that is actually expensive is compute.

But this is why engineers exist... to keep costs down.

My costs since I only have small websites on my personal account.

I often talk about the benefits of BigQuery but a reminder is always nice.

GSC is mandatory for any website so you must go through BigQuery.

GA4 is not, maybe you use another solution but the gist doesn't change.

Store your data somewhere and thank me later.

I see many companies use the wrong data or tools and end up overpaying for basic stuff.

This is even more common in the SEO industry for enterprise solutions, where you get upsold overpriced features.

An Example

A company I worked with couldn't compare YoY organic performance due to GSC 16-month limit.

This resulted in a complete mess in terms of reporting.

Now add GA4 and its stupid quota limits for the API or even the Explorations limit of 14 months...

it's unconceivable to work on medium or large websites with the traditional setup.

On the other hand, I've seen more virtuous companies where they have all the stack and are able to make valid decisions.

A simple dashoard like this is what I use to make decisions lol N.B. This is only a small piece of it.

Even I use BigQuery for storage and built a dashboard based on web data...

I was amazed to see that my pages get organic traffic without even doing SEO.

A Roadmap

The good thing about being specialists is that you master your area and don't have to worry about everything.

Data projects are a mess to manage and even more if you don't know the topic.

You can't be the average Joe and work on Web data without proper understanding.

This also applies to other specialised tools like Salesforce or SAP.

It'd be delusional to think anyone can actually understand the data in some weeks.

The truth is that not every business can grow with data or is even ready to do real marketing.

This is why you need to carefully select who to work with and let go, if needed.

Mapping Questions

A simple approach you can try with data is to map each data source to some potential common questions it answers.

Then, you can even map what a user would ask out of your data.

This is much more productive than creating another dashboard.

This is my basic tip for anyone involved with Web Data... figure out what people usually do.

In a project I am working on, I noticed that people often want specific events or page performance...

so we can apply specific technical optimizations and focus our efforts on specific data.

Data Modeling

Yes, I've mentioned this one quite some times because there is massive misinformation or ignorance on the topic.

Modeling your data correctly is an underrated skills in the web world.

Many say "my Looker Studio dashboard is too slow!" because they ignore this topic.

You aren't supposed to push your tables straight from the BigQuery exports into a visualization tool.

Those tools aren't suited for transforming data... they simply visualize it.

You model your data first to make it more digestible and only then you use it.

This is an example blueprint I created for GA4 data:

Article coming out soon folks, just some time to process all.

There are tools like GA4Dataform and PipedOut that do the work for you and have great data models to start with.

P.S. GA4Dataform released a Premium version with even more features. 15% off until October 20.

For more complex scenarios you have to consider multiple data sources!

Understanding of Analytics

I get asked the craziest questions from time to time, so here is what Analytics is NOT:

tagging/implementation
engineering
data science

It's simply about altering your decision process and simplifying it.

Understand your role. No, it's not silos, it's figuring out where you can excel.

That's it, it doesn't have to be about data.

The issue happens when an Analyst has to do Engineering tasks or vice versa.

The lack of division of roles means bad quality.

Most solutions you see don't make any sense, problems can be easily solved with the mere basics.

Let's consider the case of an Ecommerce, what do you think they lack in most cases?

No, it's not LLMs or AI.

They lack basic stuff on their website like:

Good CTAs
Proper information architecture (aka the structure of the website)
Proper page structure (headings, content)

While all of these things are NOT strategical, they tell you that many businesses didn't even do the basics for their websites.

Do you need any expensive tool to figure this out? Absolutely not.

On the business side, it's all about framing problems and figuring out where there is leverage for growth.

The Crazy Requests You Get

It's normal for us to get impossible or strange requests from stakeholders, like:

having some new shiny AI tool
to create a single dashboard for every use case

It's our responsibility to educate them IF possible.

There are cases where it's not worth your time and you should just walk away.

Not every company is suitable for quality work... deal with it.

And this is exactly where my course about Analytics thinking can help you 😎:

Become a Top Data Analyst Now!

Marketing Understanding

Web data per se is never the problem but the expectations that people have on it and their marketing knowledge.

If you complain about data accuracy or say you don't have enough, you've never done marketing.

There is no point in complaining about Consent Mode and suchlike, they exist and will stay where they are.

It's not like you can't do marketing without a computer or data!

A good chunk of what I personally do is often based on my expertise first and not necessarily data (for small websites).

What's the point of using data if there is none?

This is different with big websites where you have a lot of data and evidence.

The big misconception is measuring the wrong things as indicators of success of marketing.

I am talking about:

traffic as your sole reference
last-click attribution like there is no tomorrow
over-spending in Paid because I see quicker returns today

We know these are rookies mistakes but companies keep doing them.

No data stack can save a business that uses traffic to gauge success.

Incremental Approach

Start small (as I teach to my students in my Think Like a Web Analyst course), then keep growing.

A stack like the one below is perfect:

No fancy tool or strange tech, just what simply works.

Data projects are often bloated with costs and too many tools.

You don't know how many projects end up in failure because of crazy expectations.

Don't be the hype man and your long-term work will thank you.

Output & Execution

The most common struggle for corporations and large organizations lies in actually producing something.

Yep, they have the money but lack flexibility and tempo.

All the data in the world can't save you if you are not able to execute.

This is also why many data projects are failures... there is nobody who can actually work on the outcomes.

On the contrary, medium-size companies offer the best ground and that's where you can easily get the best results.

👥 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:

Join our Discord Community!

🔎 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!

Learn Analytics for SEO Now!

Also in ebook:

Unlock Powerful Insights Now!

📚 Recommended Reads - Peak Content 🗻

Read these peaks:

Interpreting GSC Data (& Metrics Relationships)
The (Lost) Foundations Of Analytics
Launched! GA4Dataform Premium is here
Is Analytics Just a Growth Function? by Ergest Xheblati
My GEO process (if I ever did one) - great article by our community member Ian Sorin
Analytical Skills for AI and Data Science : Building Skills for an AI-Driven enterprise
Data Literacy In Practice
Becoming a Data Head
Storytelling with Data

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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Seotistics - Web Analytics + Business + Strategy

Data Basics That Companies Lack