Use Data Or Be Used By Data!

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The December 23 issue of Seotistics is here for you!

Data is cool but what about decisions?

In the end, we use data to make decisions and it's all about what we do later.

I will show you some practical concepts of Decision Science to improve your daily tasks and limit uncertainty.

P.S. I am still running the poll to decide which course to release next, go vote please!

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📣📣 Weekly Announcements 📣📣

I've crossed 10K followers on LinkedIn, thank you folks!

Thanks to whoever voted last week!

I need a bigger sample though, so keep voting:

Finally, good holidays to you all folks, I will still send you a newsletter next Monday though!

Some Definitions

We can define Decision Science as the discipline involved in studying how to make decisions with the information available.

As you can guess, Psychology plays an important role in decision-making.

People aren't rational and often act based on emotion, so it's a bad idea to use purely logical motivations.

Convincing stakeholders is often seen as a cold activity where you have to gather data and show objective proof.

Anyone with some job experience knows that hard skills aren't all...

actually, soft skills are what get you through.

Despite that, knowing some models that guide you toward better decisions has never hurt anyone.

Simple Models/Charts

Make your life easier and adopt these models to communicate with people and/or break down problems.

Decision Tree

This is the simplest and most intuitive model of them all.

You can use it for your life and when weighing different alternatives.

This is a good exercise to do when planning content or thinking about the consequences of pruning some pages.

There are many variants and the one above is just one specific case where you list potential outcomes.

You can also think of it this way:

A decision can unlock multiple actions which can result in more decisions being made and multiple outcomes.

Pugh Matrix

I recommend all of my friends use matrices and tables when they have to evaluate alternatives because they are simple and intuitive.

You have multiple criteria that you can use to evaluate a solution and then assign some weights.

The alternative with the best score gets implemented.

You don't need to go that far, as in the example above...

you can even avoid using negative numbers.

The key is having some weights with which to evaluate alternatives and then pick one.

RACI Matrix

Simply put a 2x2 matrix with the tasks in the rows and the roles in the columns.

In the example below, you can see that the Data Analyst is NOT responsible for the architecture stuff (of course!).

I almost forgot this is what each letter stands for:

Claude is the MVP as usual and lets you create these matrices easily ❤️.

Stakeholder Map

Classifying your stakeholders in terms of importance is a big part of your analyst job.

This model here can help you in the activity:

If you still think that Analytics is a purely technical job, you don't know what Analytics is.

It's mostly politics!

Deciding Before The Data

The most important advice is to decide before you even see the data.

Many people look at the data in search of what they want to validate.

This is a dangerous approach and that's why you formulate hypotheses and assumptions before you look at the data.

It can also mean you ask specific questions before you even start:

If you don't, you will be affected by confirmation bias and probably focus on what you want to focus on.

But that's not all, you should decide in advance and choose a default action.

What would I do in the absence of data?

Let's say you don't have access to GA4/GSC... how would you decide which pages to keep?

I know, it sounds strange but the idea here is to have a reason to use data.

If you spend 10 hours on data and then make the same decision, you have wasted time.

Web Data Is Uncertainty!

If you work in Web Analytics, you already know most of our data is questionable.

Apart from the difficulty of tracking web activity, now you have to deal with consent mode and blockers.

That's why topics like server-side tracking have increased in popularity.

Even so, that doesn't change the fact web data is inaccurate by default.

Look at it directionally rather than being obsessed over the specific number.

It's important to have benchmarks to get an idea of whether a specific number is good or not.

You should get worried when the BigQuery exports show you strange numbers! (check your GA4/GTM setup)

The challenge with it is that you must balance stakeholder trust.

Using the UI or the API data for Google tools and then showing the results from BQ is a dealbreaker.

This is why you need to select a single source of truth that will be referenced for every decision:

How Does This Fit?

Before we get into processes, we need to make decisions.

Everything in life can be considered a decision and if you work in Marketing, you will face many decisions.

Pen and paper are your best friends when sketching ideas and possible scenarios.

Try decision trees and Pugh matrices before important decisions and think about the possible consequences.

Actual decision science is more complex but I don't think it's really needed for the majority of Web Analytics use cases.

No Data = Best Decisions?

There are cases where you don't need data and it'd be stupid to look at it.

I know, you have been brainwashed that data is factual and your instincts are bad.

For quick decisions, there is no need to use data.

You can understand if a page has a bad UX by looking at it in most cases.

The matter is different for important and complex decisions!

Data will be a valuable partner as long as you have enough context and knowledge to make it work.

Remember:

Some Concrete Examples

This year was FULL of scenarios where a basic knowledge of Decision Science helped me.

For example, I've worked on a quick process to ingest new data sources, it goes like that:

• verify if there are solid and doable use cases
• investigate who will use the data sources

I kept in mind that data should lead to something... if not, stop working!

It's discouraging to quit a cool initiative... but it will save your career.

By thinking about your stakeholders, the average web project can become super simple.

Especially with Web Data, you need to know what you need and what's an extra.

We get infinite pitches about the latest web tool that promises the impossible when in practice a simple dashboard wins.

Yes, this year I worked a lot on requirements and effective dashboards or processes.

Nothing fancy for us but for your target audience it's pure nectar.

Analyzing websites from scratch is confusing and tough, this is why I've created a course just for this topic:

[Analytics For SEO Course - v3]

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

What a way to start 2025!

P.S. Update coming soon, it's taking me a while due to work.

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!

An Important Lesson

As mentioned at the beginning of this issue, quantitative methods won't give you the best results.

Humans don't think in terms of models or rationality, so be sure to consider qualitative data too.

If you work in a corporation, office politics is 99% of the game and it will lead you to better decisions.

As a solo consultant, being likable plays a huge role because you are not chosen just for your skills.

When handling big projects, there are secondary interests to get more credit or budget, so you have to understand the social dynamics.

👥 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.

🔎 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.

📚 Recommended Reads - Peak Content 🗻

Read this to increase your knowledge:

• ​How to Decide: Simple Tools for Making Better Choices​
• ​Thinking, Fast and Slow (overrated imho but still)
• ​Introduction to Decision Intelligence​

My LinkedIn content:

• ​Store your web data ASAP​
• ​Content Inventory​
• ​Questions -> Actions​
• ​Documentation makes a big difference​
• ​Heatmaps are great!​
• ​Python... yes or no?
• ​Web Analytics -> Strategy​
• ​Split web pages into groups​
• ​Product Management​
• ​6 technical data books to read​
• ​Web Data Paths: Business or Tech?

❗️ 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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