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Seotistics - Web Analytics + Business + Strategy

Wrong Web Analytics Practices

Published 2 days agoΒ β€’Β 6 min read

Use Data Or Be Used By Data!

The November 17 issue of Seotistics is here for you!

Best practices are cool until they no longer serve their purpose....

today I will show you why most of the things that were done in the past don't actually make sense.

It's almost 2026 and we closer to going to Mars than having decent web data practices across the world.

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πŸ“£πŸ“£ Important Announcements πŸ“£πŸ“£

Publishing the first product updates either this end of week or next...

and Black Friday is coming soon (Friday, November 28)!

I will be at MeasureCamp Zurich this Saturday, see you there πŸ‘€

The Average Web Analytics Workflow

Most dashboards are terrible and show basic stuff that don't elicit action (or are just irrelevant).

Yes folks, stuff like:

  • Bounce Rate
  • Traffic
  • Engagement Rate

Not saying that you don't need to report on traffic... you just need better context and something tailored.

Most of the stuff you see is GTM and then straight to show the basic metrics we all know.

I've been operating completely different in the last years because I know that our audience doesn't care.

On the other hand, a simple dashboard like the one below (example for B2B SaaS) is much better:

This is NOT the entire dashboard, of course.

It's an example of what you can show immediately, including $$$.

My quick tips:

  • show the bare minimum
  • include the bottom line (if possible)
  • always define actions or consequences for the metrics you show

What You Can Do Differently

First of all, frame the problem and ask the right questions.

This will set you apart from the majority of professionals and it's FREE advice.

I noticed recently that there is a LOT of negative behavior in terms of ad-hoc requests.

An ad-hoc request is when you get asked by one person to do something specific.

Some data "influencers" in the last year have been evangelizing the importance of strategy.

Which is all good and cool but as I said before, specialists are NOT responsible for the plans of an entire company.

An example from recently: people asking about LLM visibility, a concept that is still hazy and "questionable".

Your non-technical users and laymen couldn't care less, they want to understand if this affects THEIR decisions.

Not YOUR decisions, but THEIRS.

Even if some data request is "strange", the best way is to test it and prove why it's wrong.

This approach will not only prove you right, it also educates your client and create a transparent environment.

When some people ask me why X is better than Y, I don't simply tell them it's an industry best practice.

I show examples and tell why it's wrong and even prove it if needed.

The Tools

I posted today about not using GSC from the interface, even though they added annotations today.​

How many people do you think actually read all of the post and will act on it?

Not many and I am here to consistently remind people why some practices are bad.

I am lucky to work on projects with the right people and tools:

  • data warehouses so we can work properly and not lose data
  • setting the right expectations (i.e. educating stakeholders)

Many professionals get unreasonable requests because they never set boundaries or prove things.

How can I, a non-technical person, know that what you propose is right?

Once you build a business case, it takes 0 effort to sell something like BigQuery:

What you see below works for ANY size of business:

For sure, enterprises have much more complexity, but the above setup can also scale for them.

Small businesses won't even pay for BigQuery and now you have great options like GA4Dataform to tackle the heavy GA4 work.

And you all still using spreadsheets and legacy tools that even cost more!?

Wrong Metrics & Domain Knowledge

One of the first things I check when approaching a new business is how they make money and what they sell.

In the end, we use Web Data to drive decisions that eventually make more money.

It's that simple.

The issue is knowing which levers to pull and how they are related to that business.

If you have B2C experience, adapting to B2B won't be that easy, especially for understanding which metrics to pick.

Traffic is invaluable for publishers making money with Display Ads... but would you say the same for a B2B business selling niche products?

Absolutely not, they will also have a much lower traffic but you don't really care.

Accuracy, GDPR & Attribution

I've been asked a lot of times about all these topics...

Stakeholders don't trust web data and the same goes for skeptical "technical people".

"But Marco, what about bot traffic? And attribution? Anonymization? Sampling?"

The time these people spent asking questions and doubting everything could have been used to actually make a decision.

This is how I educate stakeholders:

What's the point of using the data if no action will be taken and no decision changed?

It's that simple, really.

Once you explain people that Web Data does NOT need to be 100% accurate, you can steer into a different direction.

Many technical people will never get it, what matters is the outcome and naturally the usefulness.

What I mentioned before, aka LLMs, are a great example...

just because we can't 100% measure something it doesn't we can't estimate it!

P.S. GDPR and Consent are always important though lol just be sure not to stop there.

Theory Meets Execution

I am not a fan of some other branches like attribution because you spend a lot of time talking and not enough executing.

For sure, bigger companies need to allocate budget more "efficiently"...

but in most cases they don't have a measurement issue, they struggle with marketing understanding!

I've seen them all in my life:

  • wasting 10Ks in paid ads because everyone else does it
  • crazy budget spent in physical events
  • AI content slop because "our competitors do it"

None of these is a data problem!

My advice is always the same, before you get any close to the data, you must understand marketing.

Which is what I teach btw in my dedicated course about thinking like a web analyst:

The Relationship With LLMs

Look, I am not against them, it's stupid to go against an entire market just to be edgy.

Claude make me easier to produce technical content for my audience and code for my work.

I think that many have a twisted relationship with AI.

I am not a super fan of MCPs because they are unrealiable but they have their use cases.

Interfaces or the upcoming Analytics Advisor (GA4 chat) don't fix any issues and actually create more problems.

Use LLMs to start moving to the proper stack that businesses need.

As usual, more cool stuff next week ;)

πŸ‘₯ Join Our Community

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If you hate all the noise of social media, then this place is for you.

I will start posting more there as we have a forum channel now.

This is the best way to stay updated in real time on Seotistics:

πŸ”Ž Analytics For SEO Ebook - Course / Ebook

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βœ… Use GSC and GA4 Data to their fullest potential

βœ… Learn Python/SQL for your needs

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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!

Also in ebook:

πŸ“š Recommended Reads - Peak Content πŸ—»

Not many changes, be sure to check these out:

As usual, my most recent LinkedIn content is here.

❗️ 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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Seotistics - Web Analytics + Business + Strategy

by Marco Giordano

The Seotistics newsletter is written by Marco Giordano, a Data/Web Analyst with the goal of combining business and web data. Tired of the usual boring Analytics content without any business impact? Seotistics teaches you how to use Analytics, web data and even content in your workflow while helping you with Strategy.

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