Use Data Or Be Used By Data!

The January 5 issue of Seotistics is here for you!

Most portfolio projects are redundant.

They show you can follow a tutorial, not that you can solve business problems.

I give you some realistic tips on how to create effective projects to showcase in 2026.

‼ Before you start: remember what we said in the prequel issue. This is just a continuation of it!

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📣📣 Important Announcements 📣📣

I want to release a new course for Web Analytics done the Seotistics way.

This will be for Analysts or everyone wanting to explore Web Data in general, not only SEO.

The topics I am planning to cover are:

Coding in general (Python, SQL, R - all aided by LLMs)
LLMs and how to use them properly
Auditing websites and deeper business considerations (so not only SEO)
The topics covered in this newsletter

Vote below and let me know if you would buy a course like this.

P.S. If you voted before Sunday 28, your option wasn't recorded. Vote again to get a future discount 👀

Would you buy a technical course on Web Analytics?

Yes

What To Avoid (Seriously)

[OK, if you've read the previous Seotistics issue as said in the introduction of this email, you can start]

The first lesson is to avoid wasting resources on what doesn't matter or doesn't provide you with a clear competitive advantage.

Some projects can get skipped and are no longer relevant:

a dashboard tracking clicks and sessions
another app for Technical SEO
GA4 toy projects (Explorations, etc.)
simple web scrapers (complex ones are still top tier)

It's true that years ago this could get you hired, now the bar has increased and you need MORE.

What worked yesterday is definitely outdated as industries improve and hype grows.

You can also ignore my advice, have some basic stuff in GA4 and try your best luck.

What's important to know is:

that you know the main technologies
you have solid business use cases
you can communicate it
you can make it cool

The last 2 points outweigh all the rest, your projects must be presented in an interesting way.

For Professionals: Upskilling

The best part is once you are already in the market and need to improve yourself.

You have many more options and less struggles (good).

Many paths to consider:

Server-side applications (aka tagging)
Architecture / Data activation
Business / Marketing
Software Engineering

What to say, Analytics opens many doors and allows you to be diversified.

My eternal advice is to be specialists rather than generalists, as we discussed in the past:

If you have a LOT of experience, then it may make more sense to become a generalist.

Specifically for data, you must have some strongest suit or some area where you excel.

My technical areas are of course BigQuery, coding and custom solutions.

For the rest, I have extensive experience in Content, SEO, Business and B2B.

You don't have to tell ALL you know, just pick what can represent you and your services.

My Take On Degrees & Credentials

Well... this is a challenging topic because it largely depends on your country and background.

In general, you don't need any degree to work in data, you need relevant work experience.

I am not a fan of Data Science degrees or stuff like that because they skip the fundamentals of Computer Science...

and you'll never do most of the ML you study or that stuff in real-life scenarios.

This will be a future topic as well, it's hard!

Projects That Got Me Visibility

If I had to mention examples of projects that put me in a good spot, these would be the best:

Cannibalization
Visibility
Content Auditing (check my article, to be updated soon)
Data Modeling (upcoming article too)
BigQuery (go check my blog directly lol)
My websites

(I will try to show what I can without breaching any agreement 🥀)

Back then, I was still doing SEO and making some money with my own stuff before the HCU:

Big RIP, I was making money with Mediavine and my dear Display Ads

Plus all the client work, in-house experience and well, daily practice. My SEO background is my ace.

With solid domain knowledge, you can build solutions that work and solve real problems.

One of these examples is detecting cannibalization across international subdomains, which I cover in my products.

And most importantly, data modeling in general is a rare skill in Web Analytics and what get

An example of data model for GA4 but not the only one. I will share more in my upcoming article 👀

Even simple dashboards can be super effective for normal organizations.

But simple isn't easy to create and it requires a LOT of work.

The issue is thinking that you need to reinvent the wheel and build the impossible.

P.S. I got my 1st role with a sentiment analysis on UK politicians with Twitter data.

Even More Examples

There are many examples outside of projects or concepts that can differentiate you.

Automation-wise, the contributions of people like Lee Foot are clearly on point and practical.

His website alone has style and showcases projects that are perfect for the audience he targets.

Another great example is given by my friend Arben Kqiku in one of his classical pieces of content about Google Ads and GA4.

In the end, piecing the data together is tough and doing it properly gets you $$$ and visibility.

Presentation

As hinted before, you need to make your work stand out or you will have wasted your time.

My boring advice is to have a simple WordPress website and dedicated pages for projects.

Based on that, you can then create posts for social media like LinkedIn.

Same approach as always, say the same thing in different formats.

You can also have case studies as long as they are long-form content and go into the details.

My style is adjusted to my brand, I don't have an actual portfolio because I have content.

Depending on the level of effort you can afford, your presentation strategy can chance.

OK... So What Can I Show???

First, define a business problem or use case:

e.g. we are losing organic traffic and I don't know why. This affects the bottom line.

Very cool, now you can think of a way to tackle this problem with data.

e.g. first we detect which pages are affected, then we develop a system to audit them, finally we act based on a framework.

And there is even more, it's better to show the challenges you faced and the lessons you learned.

e.g. we spent a lot of time on data modeling and reducing costs as the data was too big.

Finally, you need to outline business outcomes.

e.g. we achieved to understand where is the organic loss originating and what is the cause. After the improvements, we noticed a recovery of X% for traffic and Y% for organic revenue.

Think about your projects this way: