Use Data Or Be Used By Data!

The November 4 issue of Seotistics is here for you!

If you want to learn more about methodology and reasoning, this issue is for you.

We'll go through what you can do to boost your organization's literacy about data...

and why case studies are often limited and tricky.

Anyone can follow, this is a light issue!

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Confusing Outcomes

The issue with many case studies and web projects is that technicalities take over the important things, like:

profitability
efficiency
solving problems

I studied quite complex stuff back then and barely used that much knowledge.

In real-life scenarios, solutions are often already known and mainstream.

How often do you see completely new problems?

The table above is one of the best ways to explain standardized solutions.

Common problems have known solutions you can buy and adopt.

If they are too expensive, you can take it to the next level and optimize costs with bespoke alternatives.

Once you get used to working with data, you should reach the top ladder of the data pyramid:

Only a minority of companies reach the Wisdom layer, where you fully act on data.

It takes years to reach that sophistication and in most cases, it's enough to have solid knowledge of your data.

Correlation VS Causation

When reading the 99.9% of case studies, you have to consider them as correlative studies, so they hint at associations without proving causality.

This means that you can't generalize their conclusions and you have to take them with a grain of salt.

The difference is explained below:

Causation isn't always the most important thing since most of what we do is testing and breaking things anyway.

Your strategy shouldn't be affected by ranking factors or shiny objects, else it's not a good strategy!

"Correlation does not imply causation."

Also, causation doesn't imply linear correlation.

Remember, correlation is a type of association, not the only one. It's too long to dig deeper into details, we will leave at it.

P.S. The majority of case studies you read online (including Semrush and Ahrefs) are observational!

OK... So What Should I Do?

Well, my advice is to test stuff yourself instead of reading from other people.

Your websites are vastly different from the goliaths some people work on.

It's unfair to compare an SME to Amazon.

Specialize in some website models (e.g. Ecommerce, SaaS, publishers, etc.) and test on them.

Data-wise, there won't be many differences.

In terms of business decisions, they are completely different realms.

Observational studies are often sufficient to make good decisions.

For super important decisions, you are forced to conduct experimental studies.

It's definitely not my area of expertise but this resource by Giulia Panozzo will help you.

What Matters In Practice

Companies care about money but they sometimes get lost in details.

Dashboards, useless tests, strange experiments with AI, you name it.

Luckily, business reality is often much easier and you should be the guiding compass.

Yet, it's dangerous to only focus on the outcomes and ignore what affects the business.

This is the other extreme of the spectrum, thinking that measuring revenue solves all the issues.

As you can see from the picture above, Revenue is the outcome but it's affected by a tons of metrics.

A sudden drop in products sold will affect your bottom-line revenue...

and that's why we track multiple indicators.

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Data Literacy

I've talked about this topic quite often and it's everywhere in Seotistics.

Working, analyzing, and arguing with data while having a solid understanding of how the world works.

This is data literacy.

This isn't a simple buzzword because you just saw the effects of a lack of data literacy in practice:

finding associations where the correlation is low
confusing cause-effect factors
confusing GSC with GA4 data or mixing them with 3rd-party tools

These problems are to be found in every corner of the industry, from in-house to freelancing.

Data literacy is strictly related to the analytical maturity of an organization:

Many get the above example wrong and think that you need to go through every single step.

A good part of maturity in our industry would be to alert, perform analysis and optimize as much as possible.

What You Can Do Differently

A lot of ideas and cool stuff but you need to apply something from this Seotistics issue.

You can:

Identify existing problems
Finding if you can solve them with data
Apply the frameworks I showed you

That's it.

Before embarking on an expensive data project, think about the problems you are solving or the bottom line.

If you can't think of a good motivation, it's better to take a step back.

If that's you:

chasing AI tools/new stuff because it's cool
studying the most complex topics without having the right foundations
actually thinking executives care about your technical knowledge

Then you are set on the wrong path.

The quickest fix is to be obsessed with context, i.e. the Knowledge step in the DIKW Pyramid we've seen before:

In Web Analytics, most stuff doesn't make any sense on its own.

You need more context to actually figure out what's going on.

The majority of case studies and tests gloss over context because it would invalidate them.

Get the context and you can easily figure out if you are reading BS.

Mapping Analytics To Business

Once again, this is where you can add real value and get stuff done.

Many companies I've talked to have this exact problem.

They have the data and all but can't seem to get business value out of it.

As a consequence, this makes people lose trust in what we do.

Apart from the usual advice I give you, it's important to evangelize people and teach them about data.

The ultimate aspiration is to have a company that is data-literate.

Throughout my career, I've noticed that it's often because data is detached from business and marketing.

The first sign of alarm is when managers get too obsessed with tools.

Teach them that they should love processes and simplicity more!

This is why communication is your best friend and it will be one of my future topics for Seotistics (again)!

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