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.
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The Case Of Traffic
One of the first things we see are case studies boasting crazy traffic growth.
A sharp increase in traffic doesn't always mean you made more money.
Consider this case for an Ecommerce:
- 200% traffic increase - ok what was the baseline?
- which pages brought more traffic then?
Working on informational content is cool... but you ideally want people to land on your product pages and buy.
Great content helps you convert users in the long term but we know that in most cases extra traffic to average articles brings $0.
There are exceptions, like publishers who may monetize via Display Ads, so extra traffic = more money.
Measuring (& Defining) What Matters
You need to figure out what you want to achieve and have some common sense about it.
In my very early days and when I was still an SEO, I was asked to "10x traffic" in 4 months π€£
This is also a red flag and an important lesson in qualifying clients and projects properly.
10xing your traffic is NOT a good goal for an app because it doesn't necessarily bring you qualified customers.
Even if you get more customers, you should be worried about their retention.
The same applies to social media... many fellow creators are obsessed with vanity numbers like followers or subscribers.
It doesn't matter if there is no engagement or no one converts.
My most loyal readers know what I am hinting at: you need a good North Star!
For those how missed my previous issues:
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.
Business decisions are completely different realms.
Observational studies are often sufficient to make good decisions.
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."β
β
βAlso, 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 are observational!
But don't worry, analyzing websites is often enough to add business value and what I teach you in my discounted course:
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Even if you leave SEO, the foundations are the same for other jobs!
Testing Is Important
There's a key distinction between Analytics and Statistics.
If Analytics helps you create better questions, Statistics often answers them.
The field of testing deeply involved the latter.
In 2025, there is enough content to study the topic but I can't but recommend this resource by Giulia Panozzo.β
You don't necessarily need to test for some roles (like mine) but it's a super important skill to learn.
Even if you don't run tests, I recommend you at least figure out when they are selling you slop.
Incrementality Testing
I know many people are guilty of this and often they are unaware of the issue.
Ever heard someone taking credit for additional MILLIONS in sales due to a specific channel?
In most cases it's bollocks.
That's because a single channel doesn't affect the whole of your sales.
Imagine working on SEO for 6 months and seeing a surge in sales.
Some not-so-honest agencies would tell you SEO is the cause...
in practice, it's more complex than that:
- seasonality
- randomness
- SEO helped you sell due to your strong brand
- some other marketing channel contributed (or their combined effect)
A strong brand and positioning make your life easier.
The correct way is to measure incremental revenue, i.e. the additional revenue that is generated by a channel beyond the baseline.
The baseline is no marketing activity, a normal condition.
Many of you will be familiar with paying for ads... and as we know many companies overspend and barely track their results properly.
Now guess what, there are cases where ads don't contribute at all, as shown in this nice LinkedIn article.
While this is absolutely out of the scope of Seotistics, this is an important lesson in context.
My friend Charlie is an expert in the topic of MMM and Incrementality.
(The topic is quite complex and incrementality tests are just one topic, though).
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 (Now)
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.
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.
π₯ Join Our Community
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If you hate all the noise of social media, then this place is for you.
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It will teach you or your employees to:
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π Recommended Reads - Peak Content π»
The usual peak to read:
My LinkedIn content:
βοΈ Feedback and Recommendations
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Marco Giordano
βData/Web Analyst
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