πŸ†™ Upskilling In Web Analytics [2025 Guide]


Use Data Or Be Used By Data!

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The December 16 issue of Seotistics is here for you!

The new year is close and many of you want to upskill.

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.

P.S. I prepared a small survey to gather some information on what I should work on next :3

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πŸ“£πŸ“£ Quick Poll πŸ“£πŸ“£

In addition to my existing content on SEO, I would like to work on more courses next year...

which would you prefer?

The 1st one is related to Web Data in general, like PPC, Social Media, you name it.

SEO is excluded as I already have enough content on it.

The last 2 are about methodologies, processes and decision-making.

You can also reply to this email if you have specific learning requests πŸ‘€

What To Focus On In 2025

The current trend in Web Analytics is to get closer to Data Engineering.

This is great advice but not really, at the same time.

The reasons are many:

  • 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.

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.

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.

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.

As detailed in my 2 articles on the topic:

Really, having all the web data in one place makes a BIG difference.

Absolutely prioritize this!

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.

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.

Now, SQLMesh is getting more spotlight but it doesn't really matter!

Learn what you need to carry out the job and you are done.

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.
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Analytics is NOT coding.
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Analytics is NOT coding.

SQL is the 1st natural choice due to BigQuery (and because data is often stored in databases).

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 Sonnet is the best πŸ’―

Our job is about analyzing data and communication, simple syntax can get you quite far.

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!

As my friend Ergest Xheblati (20+ years of exp. in the data industry) says, you need basic stuff everywhere:

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.

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:

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:

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...
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because many people won't even grasp them.
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Yet, this is how you can make a concrete difference.

For SEOs, this is what's been working great:

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).

I prepared the right path for you, don't worry:


[Analytics For SEO Course - v3]

If you want to be guided and start from scratch, this is the course for you!

This is ideal if you want to start the year with a competitive advantage.

You will:

βœ… Use GSC and GA4 Data to their fullest potential

βœ… Learn Python/SQL for your needs

βœ… Get a complete blueprint for auditing websites

βœ… Learn how to 10x your productivity

βœ… 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!


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.

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.

πŸ‘₯ 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.

πŸ”Ž Analytics For SEO Ebook (v8)

If you want to learn about Analytics for SEO and prefer self-study, this is for you!

It will teach you or your employees to:

πŸ‘‰ Prepare audits that make sense and are actionable πŸ”₯

πŸ‘‰ Avoid common pitfalls that cost you money πŸ’Έ

πŸ‘‰ Create analyses that add value and moneyπŸ’―

πŸ‘‰ Move the needle faster with efficient SEO systems ⏳

This comes with periodical updates to keep the content fresh.

πŸ“š Recommended Reads - Peak Content πŸ—»

Read my evergreen recommendations too:

My LinkedIn content:

❗️ Feedback and Recommendations

If you have ideas/recommendations for the next issues of Seotistics, you can simply reply to this email.

Marco Giordano
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Data/Web Analyst

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Bernerstrasse SΓΌd 169, Zurich, Switzerland
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Seotistics - Web Analytics + Business + Strategy

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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