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‘ What's Missing Today In Web Analytics
Published 1 day agoΒ β’Β 7 min read
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Use Data Or Be Used By Data!
The April 22 issue of Seotistics is here for you!
AI and LLMs made data even more popular but there are a lot of lingering issues.
Many of you have an interest in Web Data and it's important to show how you can get the most out of it...
while being profitable!
So what's actually missing today?
P.S. I've updated my BigQuery Handbook, go check your welcome emails... or in case ask me and I send you the link.
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.
It gives you all the missing business and marketing skills I mention when talking about Web Analytics.
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If you purchased my previous course, go to this lesson here and the last section contains a special promo code for you:
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Updates for my other products are coming π
Domain Knowledge
Yes I say it every day and there is still massive work to do!
As I always say, you need to be an expert in your niche.
The generalist vs specialist paradigm has always caused some stir but I side with specialism.
Data projects can fail because you have no clue about what is really needed.
The reason is simple, in advanced economies it's much easier to specialize on very specific tasks.
Let's consider Marketing, you have infinite choices and options:
SEO (Content, Technical, Hybrid, Product, by industry, by type)
PPC
Social Media
In Web Analytics, I've never seen the same variety as most roles refer to:
implementation (aka GTM and server-side tagging)
BigQuery work (partially)
Analytics Engineering (not analysis by definition)
As you've noticed, the strategical side is completely missing.
The same doesn't always apply for consultants or agencies, where YOU decide what to offer.
Some claim that specific knowledge will be absorbed by AI.
β True specialists know that real knowledge is hidden and not codified... so you are safe.
The Power Of Niche
Many professionals get burned out because they are overwhelmed by the list of things to learn.
Catching up with changes and trends is annoying and I don't even think you need to always stay on top.
That's because effective work is boring and doesn't change too often.
This doesn't mean you can sleep on the job and stay out of touch with the market!
Niching down allowed me to be less stressed and focused on solving problems I care about.
OK... So Who Does The Analysis?
There is no cookie-cutter answer but it's generally the specialists who analyze the data.
For example, the PPC professional analyzes Google Ads data and analysts are relegated to providing the foundations.
In SEO it's exactly the same.
Technical SEOs usually handle the analysis part and "digital analysts" do the boring background work.
This isn't always the case (luckily) but as you noticed from my past issue, we need to ignore labels.
Despite that, it's reassuring to understand where you sit on the spectrum:
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We clearly need more people on the left side, who are able to frame problems.
If you overfocus on technology, you are overlapping with engineers and sacrificing the business part that YOU should own.
This is what I usually notice with many analysts.
They'd rather invest in tech skills than climbing the business ladder.
This means we have an excess of technical people and not many actually using this data to drive value.
Hybrid Roles
As implied before, it's much better to be hybrid and doing a little bit of both.
A good marketer is capable of running its own analyses and treats data skills as nothing more than an extension of its skillset.
A good analyst is the same but with a wider focus on some problems, e.g. measuring and metrics.
This is what makes you even better than the competition.
Think about it...
if you can be a technical (enough) person with a strong business acumen, you will own the room.
For example, this is the path I recommend to many SEOs:
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Data skills act as a complement to your existing skillset.
In the past, companies hired people with good coding skills but... they didn't really know anything about the industry.
The (Hidden) Costs For Businesses
You may think that these mismatches have little to no effect on businesses, it's the other way around.
Data projects are EXPENSIVE.
Consider that:
you need some infrastructure and have an architecture
people are expensive... even if you outsource
at least 6-8 months of work
Resource usage can vary a lot but we all agree they are big investments.
You get a project wrong and you burn the trust for data for years to come.
Ask the right questions early on!
This is why I insist of making a great first impression...
you can make mistakes in marketing but in data a mistake can cost your job!
Consider that even a $200K project that doesn't bring results can affect future data initiatives. β Now imagine companies that spend +$5M and get NOTHING out of it.
Questioning doubtful initiatives at the start is better than realizing you burnt money at the end of the project.
Start Small
The general rule is to always start small and take what you needed.
I've seen a lot of projects go like this:
store everything (just in case)
tag everything
hyper complex solutions
And then they don't get used and data gets blamed.
The solution I preach is always the same: spend time on requirements and simplify as much as possible.
You can even start with a super basic marketing stack, it will do the trick:
DBT/Dataform you can also skip... be sure to track the correct events and have a working setup, though.
By using an "inverse" approach, you can understand which questions you want to answer:
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People will also think you are smarter because you don't jump to execution but take your time to evaluate the problem.
This makes you different from 99% of the people out there using AI for simple use cases.
With Web Data, it's quite simple to start small because Google gives you all sorts of free connectors.
You can build a simple system with BigQuery and be extremely effective.
And if you want to know more about GSC, GA4 and BigQuery, you can check out my course:
[Analytics For SEO Course - v4]
If you want to be guided and start from scratch, this is the course for you!
There is an update coming soon that will add quite some content...
You will:
β Use GSC and GA4 Data to their fullest potential
β Learn Python/SQL for your needs
β Get a complete blueprint for auditing websites
β Learn how to 10x your productivity
β 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!
Yes, it's true that Web Analytics often lacks a strategical flavor (and that's why Seotistics does the opposite) but that's not the only thing.
In Marketing you still see outdated methods to manage operations, for example the overreliance on spreadsheets for tasks that shouldn't be handled there.
I am happy to see people are realizing that solutions like BigQuery are needed.
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GA4 and GSC are only needed for the settings and to provide what is needed for BigQuery to work.
For sure, micro businesses can't afford even basic complexity but then I ask you...
do you even need data for micro SMEs?
Spoiler: no.
In the vast majority of cases, you need some basic decency when working with data, it's not an option for every business.
Can AI Save Us?
No, because what's missing is related to thinking and old routines, AI is just a set of tools.
Still, AI has a lot of potential to make Analytics look different from how it's performed today.
The affected areas will be:
query writing, I already rely on Claude to prototype most of my code
(basic) interpretations, LLMs can explain results
This doesn't affect analysis but makes it even better.
The real data interpretations are so complex and need so much context that it's unthinkable to consider AI.
Of course, simple analyses don't need humans.
Coding-wise, it's great news as you can focus more on the "strategy" and less on nerding.
I will talk about some nice AI use cases in my next issue though π
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.
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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