Use Data Or Be Used By Data!

The Augut 18 issue of Seotistics is here for you!

Most aspirant data practitioners get it wrong... because of most of what we read online is misleading.

I will show you what you need to know to be different from the mass.

So we are not talking about mainstream topics or what is already done...

P.S. My friend Juliana Jackson wrote a peak piece on analyzing websites + UX/CRO and I contributed, go check it out!

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📣📣 Important Announcements 📣📣

I will speak at MeasureSummit 2025 about auditing websites!

🔗 You can sign up here and find all the instructions.

Also working on a lot of content for you folks, so my Seotistics issues will be "lighter" until September 15.

Have an upcoming trip to Japan on August 29 so I will be less active until September 12.

Data Modeling

The most underrated of them all, yes, data modeling!

tl;dr visual representation of how data is organized and standardized.

I've talked about it in the recent issues... and many don't even know this is a thing.

If you find yourself writing impossible queries, it's because you have a bad data model.

This is the case for GA4 BigQuery where the original events table is a hot mess.

I've seen too many agencies being unable to deal with slightly different tables.

Folks, in real-life scenarios, companies don't use the basic schema you see in Google documentation.

You are more likely to encounter different scenarios.

Flexibility is required to understand how to use the data.

You could lose clients and some contracts if you are unable to adapt to the organizational specifics.

After all, why should a company bother to work with who doesn't understand their data?

An Example

I will show you a recent example of what I worked on for some companies:

Yes I am proud of it and I keep posting it.

A bad data model will haunt you and make your work impossible.

This is why you need to sit with your engineers and figure out what can work for you and the other analysts.

If you see people telling you to use default tables, run away!

It's not that simple and it will make your life impossible. I mean, engineers will stop you before that but still.

Business / Marketing (As Usual)

I talk everyday about it so I will give you a more specific angle this time.

The issue about data work is that many aren't taught to think about the implications on the business.

And the same goes for marketing efforts.

You can't audit content if you have never worked with content before...

it sounds obvious but you see plenty of wrong examples outside.

This is the actual cool part of Analytics, yet people are obsessed with tagging and other topics that are boring.

Mind you, boring is profitable but that only works for businesses!

I have a course covering this side of data that is often disregarded:

Become a Top Data Analyst Now!

Another thing many underestimate is how websites work.

No, I am not talking about HTML, CSS and rendering...

I mean on a business model level.

You can't expect a B2B SaaS to behave like a DTC Ecommerce.

The 2 are completely different and need separate metrics.

The (Right) Coding

As I always say, SQL is the English of data.

Too many people on my feed start with Python and do useless courses covering Machine Learning...

when 99% of the job requires you to pull data with SQL.

Then, you can learn Python as it's the best language to build prototypes.

It's not efficient and far from perfect but hey, analysts aren't engineers.

Use it as a reference to build small apps or examples you can show to engineers.

P.S. Personal tools are also fine.

Javascript is another addition I recommend because of web apps and since it's required for GTM and Tech SEO.

In one of my past issues I talked about the lost foundations of Analytics...

and coding skills are defo lacked by many Web Analytics professionals.

Claude now makes it easier and it taught me a lot after tampering with its nice React apps.

What I Still See That Is Wrong

Big rip to all the newcomers who read what's on social media, most of the content is aimed at building hype.

Yes, this includes Reddit where there is some decent advice but most of it is embarassing.

You absolutely don't need to:

create a different web scraper everyday
apply complex Machine Learning models
use AI in any form
create a small app per day
learn GA4 and GTM and stick to them for the rest of your life

Most available solutions already do what you need, coding is required in other cases.

The most immediate use case is analysis because:

existing tools are mid
more freedom over what to analyze and do
you actually decompose problems and understand data

This easily justifies the use of coding for an Analyst/Marketer as long as you don't obsess over the details.

Leave them to engineers and developers, we are not paid to write code!

(Alternative) Data Warehouses & Tools

What about non-Google tools?

If you work in enterprises, you will realize that using our good ol' Google stack isn't always possible.

DataViz:

PowerBI
Tableau
Qlik

Looker Studio/Looker may be convenient but not the best choice for other data sources.

A lot of people using these other tools are underrepresented in the industry!

I am no fan of PBI but I am forced to use it from time to time. ☠

Some organizations, especially if big, can't switch to other tools so you must be decently familiar with these alternatives.

Or, if it's too specific, you simply work with only those who use Google tools.

Analytics:

Matomo
Piwik PRO
Mixpanel
Amplitude

I recommend having more than one Analytics tool as the impact on page performance is close to 0.

Mixpanel and Amplitude are the GOATs for Product Analytics; Matomo/Piwik PRO win for compliance.

There are more competitors and choices on the market... and that's not all!

But... Wait

If it's true that GA4 now has some competition, it's also true that its market share is the biggest and there is no match.

As I said in some past issue, the reasons why GA4 is my favorite choice are simple:

majority pick
free (no guys, GA4 360 is not worth it)
the BigQuery export
integration with other Google tools
easier to find professionals who know it

In our small bubble, we think the BigQuery export is mainstream.

In real life, many companies are doing it all wrong and being overcharged to move their API data to their warehouse.

UX/CRO

Not my area but UX/CRO are the official underdogs of marketing.

If you want to convert and retain users, you must have a nice website.

This isn't exactly my area of expertise, so go read the article I told you!

Integration With Other Systems

If there is one thing that companies need the most, that would be the integration of different data sources.

Like I said in my past issue, you can have many systems and need to understand your customer journey:

Well, you won't be able to perfectly identify users but we already know Marketing is a tough beast.

For B2B, this means understanding how to connect your CRM data.

For Ecommerce, you don't wanna use GA4 for reporting on revenue and sales. It's more of a support!

The real catch is figuring out how to use this data AFTER you integrate it.

In some cases, you don't even need an integration!

Statistics/Math

Oh yes, if this topic is not underrated!

I see it often, people get into data without having basic notions because "you can get a job with tech skills".

Sure, I get it.

The problem is when you actually work with them and everything is misinterpreted.

For example:

Calculations that make no sense (e.g. average of averages)
Wrong probability interpretations (everything is equally likely to happen)
Wrong conclusions (e.g. mobile is getting more traffic -> we need to invest more into it)

Using averages like there is no tomorrow is the best way to do terrible analyses.

We could also mention AB testing which is the perfect example of Statistics.

You need a decent level of both Math and Stats to understand the most advanced topics.

Even if you are like me and don't wanna spend your days testing, this is still a nice perk.

After all, I came up with my mentor Andrea d'Agostino with the method to measure Content Decay with some Math knowledge:

Yeah that's a basic slope and the method is simple, yet it worked fine for many many websites!

All of my nice Analytics solutions are born out of my knowledge of Stats/Math.

Start VS End

Yes, it's true that you can get an average job as a Digital Analyst with the basic skills.

If you read Seotistics, you aim to be exceptional though.

The basic stuff doesn't cut it anymore if you want to stand out.

In 2017, jobs required fewer skills and it wasn't even clear who does what.

Just compare a random data offer to today.

The good news is that you can add more value now than ever before.

👥 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 🗻

Some peak summer reads for you:

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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The Underdogs Of Web Analytics