Use Data Or Be Used By Data!

The September 23 issue of Seotistics is here for you!

Every data professional or marketer has always struggled with execution and business.

You may have the best analysis/plan that gets turned to dust because nobody acts on it.

This is a story of how you can be smarter about execution and get things done.

(or avoid bad projects at all, even better if you ask me).

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📣📣 Announcements 📣📣

I will be in brightonSEO next week!

If you want to catch up or discuss something, feel free to reply to this email so we can arrange something.

Plus, I will release updates for my course and ebook by the end of September.

Cool things to come!

Understanding Analysis

When I started, I went for the best possible analysis and to show off my skills.

As it's normal, once you experience the real world you get used to what works.

Simplicity wins but some overdo it and end up ruining everything.

Analysis is used to break down the complex into digestible bits and benefits.

It's not supposed to make it even harder for your audience to understand.

No folks, Tag Manager is NOT analysis, that's tracking.

So many people confuse Analytics with Tracking/Tagging and we can see the consequences.

Execution

Doing things is what makes the difference, of course.

It's not always your responsibility but you should account for it in your plans.

For example, let's say you found out that some of your client's products don't sell.

Some considerations:

Do they already know it?
If they know, do I have a solution?
Is it feasible?
How long would it take?

The best way to prepare yourself in advance is to create a problem-solution table to figure out how to solve the most common issues.

You can do this for every channel.

The combination of GA4 + GSC + Google Ads is enough to cover most of the needs that companies have.

The Role Of Marketing

Marketing is the domain knowledge.

Web analytics without enough marketing knowledge is pointless.

The best advice comes from knowing your industry.

Some examples of doing it wrong:

measuring the correlation for SEO factors or similar (always pointless)
creating complex ML models that solve trivial tasks
investigating what you already know

You'd be amazed to discover how many companies/agencies do all of these.

Domain knowledge is the compass to prevent disasters with data projects.

The Biggest Gap (And Why It's Good For You)

A lot of people in the industry don't stay up to date or follow best practices either.

The misalignment between Business and IT is gigantic and you should leverage it as much as possible.

This is also one of the most common causes of project failure.

How I Solved The Issue

My recommendations are simple and straight:

don't work on projects without budget/resources
have your personal routine in place
ask the right questions
document what you do

The 1st is obvious, you don't want to spend energies on fruitless projects.

The others are related to yourself and your workflow.

How do you manage to bring order from chaos?

If you want a blueprint, this is what you can do:

You ask the right questions as explained in my article, then define processes and document what you do.

But remember, you have to go back to questions!

You will always have more questions to ask and that's normal.

The final goal is to transform the insights into knowledge, something durable that adds competitive advantage.

It takes a lot of time, though.

You don't simply become data-informed by running some reports.

Some Examples Of Good Execution

I've seen some great examples over time.

Once, I managed to solve a pesky issue by taking a solution created by an agency and making it dramatically cheaper.

It was a very simple solution based on GSC data alone...

I also included it in my course!

This is the opposite of what you see on social media.

No shiny Machine Learning, no AI and not even an expensive paid tool.

Companies strive for cost savings and profit maximization.

That's obvious, Marco.

Yet, I see most people still falling for shiny objects.

Another great example is making an effective audit to surface which content performs well...

and how to embrace an omnichannel strategy.

You see, many websites would benefit a lot from this approach.

Web data is the fuel needed to gauge the success of this operation and the eventual opportunities.

And... if you want to learn more about good execution, you can always check out my course with SEO examples!

[Analytics For SEO Course - v2]

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

Not only technical skills but also business knowledge you won't find anywhere else.

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!

P.S. (Ab)use the referral system and get additional discounts 👀

Learn Analytics for SEO Now!

Upskilling Is Key

Whether you are a marketer or a data professional, upskilling is necessary to survive.

I am not very bullish on the future of people who ignore business/domain knowledge just for technical skills.

The real problem of the data industry is getting value out of data consistently.

One of the problems of marketing is not being a cost center (and attribution).

The root causes are many but the lack of common sense is mutual.

The Referral System

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