Use Data Or Be Used By Data!

The January 13 issue of Seotistics is here for you!

AI and Machine Learning are everywhere and people often confuse Analytics with these two...

or even with Engineering.

Descriptive Analytics is what you will often do and what companies usually need.

You'll read this issue of Seotistics and understand more about web data, for sure!

P.S. Some more detailed resources are at the bottom as usual, including 2 of my articles 👀

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Once again, I ask who didn't vote to choose an option:

Which course shall I work on next?

Analytics for Websites in general (excl. SEO)

Content Management/Marketing + Analytics

Thinking like an Analyst (Decision Science)

The idea is to expand Seotistics Academy with more content related to Web Analytics + Business as a whole.

Descriptive Analytics... What?

There are different types of Analytics, as summed up by the usual picture I like to share:

Describing something does NOT identify the root causes of why it happens.

Analyzing trends and past events is what Descriptive Analytics is.

We have no information about what could happen in the future but...

your domain knowledge should help you!

This is quite different from Statistics which often helps you answer those questions.

I have many people asking me stuff like A/B testing or even from unrelated fields.

Everything has its place but today we'll just talk about a specific piece of Analytics.

And if you are interested in testing, my friend Giulia has a great free resource.

The Reality Of Your Average Website

Most websites require simple operations, even enterprise projects.

If you have properly understood the definition of Analytics, you know that it's about simplicity.

Many get this wrong and confuse it with:

learning Python/coding
SQL
programming
SaaS

It's none of them!

It's a way of simplifying reality and asking good questions.

In many cases, you can survive with Looker Studio, BigQuery and some common sense.

Basic data wrangling will get you quite far, really.

The Value You Can Add

Analytics should be seen as a way to multiply the capacity of a business.

Going back to today's topic, analyzing trends is powerful enough to get more traffic or sales with your website.

A very underrated example is provided by Microsoft Clarity.

It's a simple tool you can use to analyze how people are using your website and improve your UX.

Does it require advanced knowledge? No.

Yet, it adds more value than countless hours on bounce rates and tricky metrics.

The advice that helped me the most is to think about outcomes and how you can add value.

When not sure about something, ask right away!

I am the first guy in the room to challenge solutions I don't understand.

It's for the good of the project, even if you come across as annoying.

Is this solution going to add business value? How?

If you want to be guided and avoid all of these mistakes, you can check out my Analytics for SEO course:

[Analytics For SEO Course - v3]

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

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!

Learn Analytics for SEO Now!

Leading VS Lagging Metrics (Again)

If you've been reading me in the last few months, you know that leading metrics are used to affect outcomes (lagging indicators).

It's correct to focus on financial metrics (e.g. revenue) but you shouldn't ignore traffic and all the rest.

That's because revenue is your outcome and is affected by other factors.

For example, a shift in the conversion rate can affect how much you make...

but if you only focus on the output, you will not find this important link.

This is why we rely on metric trees:

This is an example for a DTC website, there are many variations and possibilities!

Think about a systems of pulls and levers, your levers are what affect the final outcome (lagging indicators).

From a purely operational perspective, this is all the forecasting you need.

Identifying correctly what affects your outcome is already a form of "prediction".

So you can shift from metric trees to questions:

Once again, we are not using models or anything super complex, just common sense and business knowledge.

And then, those metrics can go straight to your dashboards or be used to affect your actions:

This is an example I just made up to show you how to think about dashboards.

Say, it's a B2B SaaS, you can have conversions, new users and user stickiness (DAU/MAU) as your KPIs and then MRR (or anything else) on the far right.

Take this as a demonstrative example, of course!

Redefining Value In The Era Of AI

Many of you ask me "Do I need coding to do X or Y".

This is a legitimate question and I am happy to tell you "No, you don't really need to be a master at it".

It's crucial that you learn how to think and add business value.

The 3 above are a summary of the principles I live by daily.

If you have a good knowledge of Marketing, Web Analytics will be much easier.

Otherwise, you'll be learning a lot of notions without context.

P.S. Hard skills are still the baseline, of course!

Web Analytics is often relegated to technical skills but it doesn't have to be!

Marketers with technical skills or Analysts with marketing/business knowledge sit on the left side of the spectrum:

This is what already adds value and will make a breakthrough in the era of AI.

A folk with good marketing knowledge is already invaluable, imagine one who combines tech knowledge too!

And What About The Rest?

Yes, I've talked well about Descriptive Analytics but it's not enough, as you may expect.

Predictive solutions (i.e. ML algorithms) are cool and fine if you are sure your data is decent enough.

This is not often the case in SEO but I am quite bullish on PPC and conversion data.

The big deal with ML is that building a model isn't enough because:

it needs to be maintained
it should be deployed
it needs to be trained on the "right" data

Prescriptive solutions can be quite vague and I don't have a strict definition.

Preventing damage or understanding what should happen doesn't necessarily require complex modeling.

Being proactive is already enough to cover most use cases.

Some Real Life Situations

Theory is nice but real life hits hard.

The majority of scenarios go like this:

GA4 is set up incorrectly and/or GTM is not used to track relevant events
no one knows how to use the data to build use cases
your boss/client obsesses over new tools

My complaint when I was more of an SEO than an Analyst was that data people didn't (often) understand the data.

Now as a 100% Analyst, the situation hasn't changed.

The industry is filled with practical knowledge and tutorials but rarely goes beyond and looks at the value of data.

This is where Seotistics helps you and also why shifting to a different mindset is 90% of the effort.

Once you know what you want and the requirements are clear, I recommend considering data as a product.

You Paid To Use Web Data But You Got Nothing

This sounds pretty much like Marketing, right?

We've gone through years of crazy hype over data and now expectations have shifted.

The answer is that companies often want something without having the right structure.

If you can't support these initiatives, take a step back and focus on something else.

The reason is usually tied to how we interact with data.

Many companies never cared about having a strategy, do you think they will succeed with data?

The Great Disconnect

When I was a student, I recall that many people with a pure background in Computer Science went into Data Science and Analytics.

It wasn't necessarily a great thing seeing how the industry evolved lol

An unhealthy obsession with technology is one of the factors that made people see data as a cost center.

This is why today you see people focusing more on the business (finally!).

In Web Analytics, the news hasn't yet arrived because most of the content is about Tag Manager and technology.

Nothing wrong with it...

but business don't grow with compliance.

They've tried to convince us that pointless technical details were used for ranking in SEO.

You Can Be Different (From Now On)

Instead of going for the best tool out there, think about the actions or what you need to reach business goals.

You'll soon realize it's not about the how but the why.

Just open social media and write a post about tools, people go crazy for that.

There is so much hype around shiny objects and technologies!

👥 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.

Join our Discord Community!

🔎 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:

👉 Prepare audits that make sense and are actionable 🔥

👉 Avoid common pitfalls that cost you money 💸

👉 Create analyses that add value and money💯

👉 Move the needle faster with efficient SEO systems ⏳

This comes with periodical updates to keep the content fresh.

Unlock Powerful Insights Now!

📚 Recommended Reads - Peak Content 🗻

Some evergreen peak resources:

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
Data/Web Analyst

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Bernerstrasse Süd 169, Zurich, Switzerland
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📊 Descriptive Web Analytics & How To Do It