Use Data Or Be Used By Data!

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The November 25 issue of Seotistics is here for you!

Metrics and KPIs are in everyone's mouth but many barely know how to make the most out of them.

I spent the last months on this topic to find what can be improved.

Mental models and frameworks can give you direction and make order out of chaos.

P.S. Incoming 40% discount on all my products from November 29 to December 3!

P.P.S. A new article about BigQuery (GSC & GA4) and why you should use it is available on my website!

​Read this in your browser​

(Incoming) Black Friday Offer On My Course/Ebook

I will launch a 40% discount on all my products from November 29 to December 3 (CET), the last of this year!

Stay tuned because I will notify you all when it's live!

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Reminder to check out my video with Candour:

You can watch the video here and let me know what you think about it!

What People Do Incorrectly

Metrics are relegated to reporting and often without interaction between them.

"Users increased by 10% and that's good"

Statements like this are superficial and out of context because:

• It can be bot traffic
• It didn't affect the bottom line

The solution to this problem is thinking in terms of business and outcome.

Why are you measuring something?

Well, in Web Analytics it's often because you want to make more money.

This brings us to the next point: giving context to metrics.

And what's the best way if not visualizing them?

Metric Trees & Indicators

Metric trees allow you to visualize and conceptualize how different metrics affect your outcome.

The outcome is your North Star metric, how you measure success in the long term for a business.

We've covered a similar concept some issues ago:

Leading indicators are your inputs, your decisions and the levers you pull to affect your outcome.

The outcome is also called the lagging indicator (revenue in the example above).

While this is a gross simplification, it works for many use cases!

Now, let's see a proper example of a metric tree:

The North Star (Revenue) is the outcome/lagging indicator.

The components are how you calculate the parent metric.

Influences are factors that affect a given metric, like seasonality.

These can be correlated to your metrics and often account for uncertainty, external factors and "intangible" stuff.

(This will be explored again next week).

Despite what many claim, websites have the usual business models and metrics.

True, every business is different...

but not really.

Churn is an issue for all B2B SaaS and having a good margin is essential in Ecommerce.

Publishers rely on traffic and like ads money.

There are exceptions but in 99% of the cases, we already know the business models.

Some Nice Tools For Creating Trees & Diagrams

There are many choices, like:

• Diagrams.net
• Count
• Mermaid (ask Claude/ChatGPT to create some mermaid code for you)
• Excalidraw
• Miro

My example was produced in Excalidraw (as always) but it's far from being optimal for this.

​Count is the most polished choice but you need to pay big bucks.

Adapt based on your financial situation, anything works, really.

Making Use Of Them

If you are a data professional (or an aspiring one), you should start drawing and writing.

Yes, this is my personal advice.

Even if you are not, do it anyway, it brings so much more clarity and helps you have something tangible.

Let's take the previous example... what can be said?

Most of the influences you should already know, e.g. seasonality plays a big role in sales.

The rest is useful to go beyond one marketing channel and think about different perspectives.

Stuff like the Core Web Vitals and page performance feels like fluff...

but in a business context, they do affect conversions!

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Understanding What Can Be Done

Once you figure out how to affect your North Star metric (or lagging indicator if you may), it's time to take action.

For example, if you want to increase the revenue from your website, you can focus more on conversions and retaining existing customers.

Launching offers, new product lines and segmenting users by behavior can be one such example.

This is the quick and obvious answer, in practice it's deeper than that.

For example, you created a tree and discovered some levers that you barely considered!

Root Cause Analysis

You could do the same with Marketing channels, like SEO:

"Why did I lose traffic?"

You can use the previously drawn metric tree to inspect the single levers.

And... you can also use other techniques like the 5 Whys.

Even if you made progress with this method, you can't analyze every single page of your website.

My most loyal readers will remember how I always recommend classifying pages into different groups.

A website with 100K pages can't be analyzed page by page, so you use clusters instead.

From there, you can investigate and see why you may have lost traffic:

• Ranking drops
• Seasonality
• Market contraction/Demand shift
• Competition increase
• Google update

And even better professionals look at the impact on revenue.

This forces you to think about consequences:

The plot above shows the potential outcomes of your choices.

And this brings us to the next point...

can we represent decisions with trees!?

Decision Trees

As covered in some past issues, I recommend building decision trees when you don't know what to do.

The one below is quite simple and straightforward:

You can also make them more complex and useful by assigning a probability to each outcome.

Here the true skill is estimating realistic values.

Old VS New Mindset

The outdated mindset (which is still popular in Marketing) focuses on the tool.

You will hardly see these topics discussed.

That's because many agencies and practitioners rush to implementation and forget about what matters.

If you want to work less and have more impact...

you need to target what matters the most.

As I always say, domain experts with technical knowledge win.

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If you are a marketer and have learned enough analytics, you will be more useful than a random analyst.

That's because you know where to look at the data and what drives value, there is no middleman!

Now the Web Analytics field is changing and is getting more oriented toward Python and Google Cloud...

but those won't make you special.

Civil Wars

Discussing metrics and performance is fertile ground for heated discussions with stakeholders.

My take here is that it's 100% worth it.

It's better to have conflicts early on rather than waiting until the end.

Agreeing on what's important is already a huge step forward.

A Word Of Caution

Frameworks and models are but a human representation of reality, they are not set in stone.

You can build a metric tree that is different from what other people have built.

Both of them can make sense.

In Web Analytics, there are too many things we have to consider so don't shy away from simplifying your work.

As you test it out, you will necessarily change and adapt your metric tree(s).

That's normal, go for a viable sample and keep iterating.

👥 Join Our Community

Our Discord community offers a small place where we can talk business and SEO.

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!

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This comes with periodical updates to keep the content fresh.

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P.S. Wait for the 40% discount from November 29 to December 3!

📚 Recommended Reads - Peak Content 🗻

Some insanely good resources for you:

• ​BigQuery GSC & GA4: Differences with the traditional methods​
• ​SOMA: a standard for business metrics (great read and must)
• ​Introducing Metric Trees​
• ​Designing Metrics Trees​
• ​Designing and Building Metric Trees​
• ​An Introduction to Metric Trees​
• ​How to Decide: Simple Tools for Making Better Choices​

P.S. Many overlap but you need to read the same stuff over and over to learn!

My LinkedIn content:

• ​DBT VS Dataform​
• ​Classifying SEO Solutions​
• ​Asking Business Questions​
• ​Python/AI are overrated​
• ​Partitioning/Clustering​
• ​Complementary VS Reinforcing Channels​
• ​6 Books that defined my path as a Data Analyst​

❗️ 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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