The Seotistics newsletter is written by Marco Giordano, a Data/Web Analyst with the goal of combining business and web data. Tired of the usual boring Analytics content without any business impact? Seotistics teaches you how to use Analytics, web data and even content in your workflow while helping you with Strategy.
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π Upskilling In Web Analytics [2026 & Beyond]
Published about 19 hours agoΒ β’Β 7 min read
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Use Data Or Be Used By Data!
The April 20 issue of Seotistics is here for you!
Whether you are a marketer or a data professional, it's always good to have more skills, especially during a recession.
I'll show you what you can learn and how to avoid spending time on topics you will never touch.
Also... demand changes so you must be there to supply it π
Please move this email to your Primary inbox or reply to it. This is to prevent Seotistics goes into spam by accident. Gmail users can read this tutorial to do it.
This will be for Analysts or everyone wanting to explore Web Data in general, not only SEO.
The course is split in 4 amazing modules: β β GA4, GSC and other data sources: how to use them β LLM Workflows, BigQuery, SQL & Python β Dataform & Pipelines β Real Use Cases at work
What To Focus On In 2026 (And Beyond)
The current trend in Web Analytics is to get closer to Data Engineering.
This is great advice but it really depends on what you are trying to accomplish.
You see, multiple topics are mainstream and becoming needed, like:
server-side tagging
anything about consent
data pipelines
These are already requested, it's nothing new.
However, leaning too much into engineering is NOT the answer for some of you because of:
overcrowding the already crowded Engineering space
forgetting about our job: making money
doing tasks that companies won't give you lol
Marketers who become too technical suffer from the same issue...
they try to do what they shouldn't.
I advocate for a hybrid approach where you can do the analysis yourself and still retain your domain knowledge.
(Some engineering is more than fine too, uh).
Still, you may want to have a look at these topics because they are useful:
BigQuery
I always talk about it because it has changed the way Analytics is done!
Before BigQuery, it was a tragedy to have data in one place...
let's take Search Console:
different results based on your queries
heavy sampling
Now, you don't need to worry about this anymore.
β
GA4 is the cherry on top with its complex (yet useful) structure.
I know, GA4 is hard and y'all hate it...
but the BQ export makes a big difference.
Check my articles below, it's all there...
I can recall my past days when I worried about having the data for tomorrow in the right format.
Now, it's all in BQ and I just need to query the data, simple.
Really, having all the web data in one place makes a BIG difference.
Absolutely prioritize this!
You can't do serious Analytics without a data warehouse/lake, forget about it.
DBT/Dataform
While Engineering is a separate discipline, this is a nice one to have.
I use both occasionally if there are specific tasks that require processing.
To keep it simple, these tools process your data in BigQuery and make them more usable.
β
Pick either and find some practical use cases, like:
transforming GSC data
unnesting GA4 data/breaking it down into multiple tables
Remember, you can simply use packages or solutions built by other people.
Learn what you need to carry out the job and you are done.
It's not strictly required BUT there are too many situations where some knowledge makes your life easier.
Data Modeling
Yep, if we talk about DBT/Dataform, then we must mention the art and science of data modeling.
The tables we get are often in inusable or hostile formats for consumption.
So you need to understand how these tables should look like:
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But in many cases it's much easier, don't worry!
often mention great tools such as GA4Dataform and PipedOut... but for custom use cases, you need practice.
Web Analysts have always been hybrid so some Engineering skills won't hurt you.
Expect a detailed article soon enough π
Coding (To A Certain Extent)
A lot of people ask me about coding in general...
because it's considered the hardest part.
Short answer: yes but not too much.
Analytics is NOT coding. β Analytics is NOT coding. β Analytics is NOT coding.
SQL is the 1st natural choice due to BigQuery (and because data is often stored in databases).
Claude Code helps MASSIVELY with coding and prototyping
Then, you can choose one between Python and Javascript.
I went for the 1st due to my background, but if you are obsessed with GTM and Dataform, I recommend the latter.
AI made it easier, you aren't even required to be as proficient as before.
Back then, you had to be more involved with coding and I was every day on Stack Overflow.
Now? I hardly check it, Claude is the best π―
Our job is about analyzing data and communication, simple syntax can get you quite far.
β
SQL and Python are well-documented... AI can write entire scripts without many issues.
Some "veterans" consider this taboo because they are afraid of change.
It literally made me save hours per month and immensely boosted my productivity!
What You Don't Need
It's easier to tell you what NOT to study because I was in this position years ago.
The shiny topics are the worst ones:
AI (complex models)
the Machine Learning you study in universities
I was lucky because I spent my college years reading good advice on LinkedIn.
Deep Learning and the "complex" models are topics I completely skipped because many companies don't need them.
At least, in the Web industry!
β
The Web industry is a little bit quirkier but it's still boring and repetitive (great for making money).
Most of my work consists of:
framing business problems
deciding which data to use
running SQL to get insights (occasionally Python/R)
communicate them
prepare actions
See? No mention of strange models whatsoever.
You don't even need fancy degrees because it's all practice (and industry expertise).
Some of my connections went back to college to study topics that are outdated by now...
and no one really cares about the Web world in the academic world.
P.S. A good degree makes the difference (Engineering) but not the "cool ones" about Data Science lol
Lucky you, the basics will get you far, I saved you 1000s of $.
My Personal Experience
I started with a background in Business Administration and Marketing and then moved to Data/Computer Science.
So I'd say this is a great example of upskilling.
Back then, there was no reliable resource to learn from, it was pretty much all generic stuff and hyped content.
The most impactful skill is without a doubt understanding problems and marketing.
β
You can save so much money with the right mindset.
Don't start that project if the motivations aren't clear.
Avoid pruning half of your website because the "data suggests so".
This is what makes the difference with a mediocre data professional.
If you feel in doubt, remember this picture here:
β
You convert data (raw material) by giving it a meaning put into the right context that eventually leads to action.
It all depends on your understanding!
A Ready Roadmap
Like in RPGs, you can choose different paths.
While reality is more complex than binary choices, this is often true:
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Seotistics is clearly on the left end of the spectrum as most of my content is about business.
You can follow the mainstream and become another half-engineer...
so the market will be saturated again and you will make even less.
Business acumen and having a clear mindset are underrated... β because many people won't even grasp them. β Yet, this is how you can make a concrete difference.
For SEOs, this is what's been working great:
β
Don't venture too much into Engineering topics unless you want to be overworked.
Most of those tasks will be handled by proper engineers (if not, there is a severe organizational issue).
For all the others, the situation can be different.
LLMs (Claude Code π)
You can't really sleep on these, it's not 2020 my folks.
If you don't use AI, it means you are wasting your precious time working with outdated procedures.
As explained in my previous issues, this is a must, check the resources at the bottom.
I've talked too much abou them, no reason to cover them here!
The Domain Knowledge You Miss
If you are a marketer and have a strong knowledge of your industry, then you can skip this section!
If not, keep reading.
Analyzing data also requires you to understand what it means.
The best things you can learn are:
Content Marketing + Management + Distribution & Repurposing
Inbound Marketing
Outbound Marketing
You can't be taken seriously if you don't know how to interpret your data.
β
One of the best examples I can think of is the typical case study correlating technical factors.
Is the Title Tag length important for ranking?
If you spend time on this BS, it means your marketing game is weak.
This brings us to the next point: metric trees.
β
Understand the levers to pull require business understanding.
Too many people work on needless tasks because they lack this sensitivity to gains.
π₯ 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:
The Seotistics newsletter is written by Marco Giordano, a Data/Web Analyst with the goal of combining business and web data. Tired of the usual boring Analytics content without any business impact? Seotistics teaches you how to use Analytics, web data and even content in your workflow while helping you with Strategy.
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