Use Data Or Be Used By Data!

The June 9 issue of Seotistics is here for you!

Social media are landmines of information.

You can find pretty much everything and most takes aren't even correct.

Today I will show you some concepts to get started with web data in any company.

This won't be a tutorial, this is about processes, mindsets and thinking correctly.

P.S. I revamped my article about Content Decay, go check it out!

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To come next:

"Analytics for SEO" ebook v9 (I swear, this one soon 👀)
More additions to "Analytics for SEO" course
More content for "Think Like a Web Analyst"
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Articles

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More article updates and content to come 👀

Stages Of Analytics

You can't skip the foundations, it's nonsense.

This is a rookie mistake I often see.

Companies rush to use cool methods but don't ask the basic questions, like:

how do we make money
how can this solution help us
what does the data mean
how was the data collected

People overcomplicate it, you need to use common sense and ask the "stupid" questions right away.

In most cases, you can simply stop at the beginner stage.

If a business isn't mature enough, you can start by reporting and monitoring what's going on.

Based on that, you can think about plans, corrective actions and stuff.

Yes, that's it! This is already valuable.

Leading VS Lagging (Misconceptions)

If you approach Analytics or every marketing channel just by measuring the outcome, you are missing out.

The outcome should always be to make money, though.

Paying for ads or content is not for awareness (doesn't mean anything) but for making money.

That's why we distinguish between leading indicators and lagging indicators.

All the traffic metrics are leading indicators, they inform you that you are on the right way.

The lagging metric is what happened or the outcome. For example, getting money.

It's easy to say we only care about money but that is wrong!

Monitoring leading indicators prevents catastrophes in your outcomes.

Ranking for fewer keywords may (or may not) lead to less money.

Less engagement on social media may translate to monetary losses.

The Basic Stack

Most of us will often deal with one or more of the following tools:

The average Web Analyst has the following split:

Excellent GA4 knowledge
Excellent GTM knowledge
Good Looker Studio skills
Some OK-ish BigQuery skills

I consider them to be basic today and not enough if you want to stay competitive in the current market.

What is needed is the so-called "Domain Knowledge" aka your understanding of a given industry.

Would you accept the advice of someone without experience?

I would never!

Apart from it, there are other desirable and important skills:

Marketing understanding
Coding (now with LLMs it's super easier)

This is what allowed me to stand out and make my life much better.

The Case For Web Analytics

The focus is often on vanity metrics or too much on money.

Having balance is what is needed, both extremes are subpar.

Impressions and Unique Query Count are the rawest types of leading metrics.

They tell you that you may eventually get traffic.

Users, Clicks, Sessions, etc. are also leading metrics but they occur after people see you.

I stick to one traffic metric in my reporting because no one really cares about having multiple.

Too much information means no information.

What you should usually be more interested in is:

Revenue (Profit if you are that lucky to have it)
Conversions
Leads

As said before, these outcomes (lagging) are affected by other drivers (leading).

Depending on your perspective, conversions and leads can also be lagging/outcomes!

Data-driven VS Data-informed

Data-driven is the worst buzzword to be ever created.

A lot of non-technical people use it to mean absolutely nothing.

I can tell if someone is not experienced (in Analytics) if they use this word.

Are many companies actually using data to make decisions?

Not really, they cherry-pick what makes sense for them (the big shots).

I prefer the word data-informed because the focus is on the human side again.

The difference can be summarized like this:

The issue with all the information online is that it's too techie...

but this isn't where companies struggle.

Let's be real, anyone has data and they have no clue how to use it.

What's the point of building tools that people won't use?

And if you want to get all of those extra skills I mentioned, check out my course:

Become a Top Data Analyst Now!

Product VS Research

A product mindset can benefit you but it's not the only thing to look out for.

Once you have defined the issues and you are past the research phase, that's it.

If you are exploring a website for the first time, you can't adopt the product mindset.

Take your time with research and don't overload yourself with pointless tasks.

The requirements will change anyway!

Just to be clear, Analytics doesn't really get along with Scrum and Product Management.

I've attached a great article in the resources... and I think the same!

(My old readers will know that I even mentioned Lean and Six Sigma).

If we have to summarize what worked for me:

ask users what they need. If they don't know, you either know it yourself or don't do anything
documenting what matters to avoid inconsistencies and definition problems
know your industry dude!

You can use Scrum for specific parts though and that's OK.

But if you are coming from that background... data is just built different!

The level of complexity and exploration required is much higher... leading to severe unpredictability.

Data Dictionaries & Governance

If there is one thing no one talks about is data governance.

Have you ever heard someone talking about governance in web analytics? Not sure!

A data dictionary is a necessary piece of governance and helps you univocally define the meaning of your data.

Literally a dictionary where you define your metrics/dimensions and list down everything.

As an example:

GA4 data is a hot mess, so you need one or even more if you are using BigQuery data.

Some metrics have no official calculation and there needs to be clarity.

I love investigating and asking users what they need or how they define things.

It's a great business exercise since not many would actually start from the basic definitions.

The Boring Work No One Does

Most of the Analytics work is boring, pretty much like anything effective.

Boring is a good indicator that something works.

Too much variation is a bad thing, especially if you are trying to streamline processes.

For this reason, spend time on documentation and processes because this is where you can add the most value.

I've spent countless hours fixing mistakes made by other people or simply understanding GA4 events.

In my experience, a lot of companies need this kind of service in addition to what we already offer!

Luckily, most of this you can now handle with Claude and standardize the process:

LLMs offer a new playing field and so much boring stuff is now much easier!

And let's not forget that now prototyping ideas has never been faster... use this to your advantage.

👥 Join Our Community

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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!

Learn Analytics for SEO Now!

Also in ebook:

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📚 Recommended Reads - Peak Content 🗻

Some peaks for you:

Seotistics blog (if you just subscribed) - updates and new content coming 🔜
How To Fight Content Decay With Data & Prevent It
Dataform RFM Segmentation and Python Notebooks
Why Agile and Product Management fail with Data & AI? by Anne Claire Baschet and Yoann Benoit
The Data Leadership Triangle by Ergest Xheblati
The first steps as a data leader/consultant by Ergest Xheblati
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

My LinkedIn Content:

❗️ Feedback and Recommendations

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Marco Giordano
Data/Web Analyst

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Bernerstrasse Süd 169, Zurich, Switzerland
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