The Analytics ROI Your CFO Won't See


Use Data Or Be Used By Data!

The May 25 issue of Seotistics is here for you!

Data, data and data... but is it even true that Analytics helps with making money?

You see, there are many ways Analytics helps you.

And I will show you how data goes far beyond simple advice.

The term ROI in the email subject is improper but most of you use it soooo.

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

You can now preorder my new course at 30% off until June 15, the official launch date.

This will be for Analysts or everyone wanting to explore Web Data in general, not only SEO.

The course is split in 4 amazing modules:
​
βœ… GA4, GSC and other data sources: how to use them
βœ… LLM Workflows, BigQuery, SQL & Python
βœ… Dataform & Pipelines
βœ… Real Use Cases at work

I will speak at Web Analytics Wednesday (June 2) and MeasureCamp Copenhagen (June 6).

I will talk about Data Modeling at WAW and for MeasureCamp... let's see!

I am back from my "holiday" so expect more to come soon πŸ‘€

The "Stereotypical" View

The wrong view of data, especially from marketers, is that you use data to drive "data-driven" decisions.

Well, this isn't wrong! The problem is that data-driven doesn't mean anything.

That's because data doesn't decide anything, it's YOU still doing the work.

And not only that, automation is often measured incorrectly or disregarded in favor of "revenue".

We should all know that revenue is often misreported or attributed to the wrong model.

Automation Example

The real power in automation is when you actually measure it.

It's easy to underestimate the impact of a data product like a dashboard and call it useless.

But you have to remember that manual data extraction is one of the most common causes of time waste.

How do I know? Well I've done it for years, I lived that feeling.

Let's consider the most common scenario:

  • data extraction happens manually
  • no storage of data whatsoever
  • no processes in place

By running some simple Math, we can deduce that you are wasting hours of a work week doing useless activities.

Yes manual data extraction doesn't bring any benefit, it's just a hurdle.

One of the first things I do with a new project is reduce this hurdle and work on adding value ASAP.

A simple way to measure the success of automation is with efficiency gains.

So if your solution saves 40 hours a week, you can reinvest those in better activities.

Not only that, each hour has a price since you are paying someone to work.

And even though (most) managers don't care, the most important argument is psychological.
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Not having to hate your jobs with manual task is a HUGE boost for productivity.

Example 1: Data Pipelines

The most important example is having your web data available.

Only a few organizations can boast having usable data that is used for something.

Yes maybe you can see that Data Studio dashboard but the backend is terrible.

A data pipeline is a series of transformations on your data.

In our case, it looks something like this:

Your stakeholders don't care about the steps but the final output, which in this case is saving TIME.

Extracting data from the Google APIs, combining them and all takes time.

You also don't have a solid way to compare results, unlike an actual pipeline.

In most of my projects, this alone saved dozens of hours of weekly work and tool subscriptions.

Assuming 20 hours saved for one person, priced at $20/hour, you would have $400/month in savings.
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But this isn't how it works normally, consider a whole company, like one with 500 employees using web data:

500 x $400/month = $200K/month

For sure, this amount of money doesn't mean you spent $200K less!!!

It's just an estimation of wasted work you saved, it's not like you will get $200K back in your P&L.

So it's better to consider them as CAPACITY you save for people to work on better opportunities.

The Price Of Mistakes

Let's consider a big Ecommerce or an aggregator of hotels...

we agree both businesses heavily rely on data, right?

Now what happens if you don't have a proper process or if something breaks and you don't catch it in time?

Yes, you lot an immense amount of money!

Negativity sells better than positive emotions.
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In this case the math also makes sense, Analytics can help you prevent costly mistakes.

I helped a LOT with website migrations and their assessment.

Recently, I noticed that a B2B SaaS brand redirected a home page to the wrong URL on the new website...

You can imagine this had a huge negative impact on their traffic.

Example 2: Content Automation

We all know (I hope) that good content makes you money and keeps your company alive.

This is especially true for B2B as potential clients need to trust you first and usually go through multiple touchpoints.

Think about companies like Ahrefs, Stape or Semrush...

why do you think they invest in offline events and a LOT of content?

So proper content automation is one of the best use cases for companies.

Ryan Law shared one example of how they do it in Ahrefs.

Mind you, the average company is NOT Ahrefs so adapt it based on your needs.

I assume my audience is reasonable enough to get that.

Recently, I've been working on improving this with n8n.

It's a WIP but the idea is simple:

  • scrape SERPs with DataForSEO
  • cluster keywords with my public script
  • perform several checks and assess intent, competitors' content, etc.
  • formulate suggestions for your content

The idea is quite simple: great content requires research and effort and you can use automation to cut the time.

Writing is still required though, this is why I never share or tell people to write content with LLMs.

OK... But Products?

The same goes for your Ecommerce catalog, updating it promptly makes a big difference.

I will share more examples in my next issues, especially with Google Merchant Center.

The Fake "Data-Driven"

Every company/agency loves to claim they use data to make decisions.

Except it's not even true and you will spend your life inside a spreadsheet with bad data.

I give you a very common scenario:

  • 10 different dashboards
  • a lot of tools

This would be considered data-driven by many because they look digits on a screen.

Having some data doesn't tell anything about how good your decisions are...

or how fast you can work.

Managers got sold the idea that a decision without data can't be trusted.

It's actually common to make decisions with no data and it works at times, e.g. your personal experience.

The best advice in these cases is to build less and do more.

You gather data by doing, not watching!

The Right Balance

If you are just starting out, here is what to avoid:

  • Over-documenting
  • Excessive focus on stack

Look, for Web Analytics it's super simple, the stack in most cases is all about Google.

For example, when I was working on an enterprise use case, we spent around 6 months to build ONE dashboard.

Needless to say, that use case didn't go as planned.

It's also true that LLMs increase efficiency... temporarily!

You can be more productive than before but you will reach a plateau and be back to square one.

This is also called Jevons Paradox.

This is why despite AI innovation you still work the same or even more.

I am already preparing my next issue, catch you next week!

πŸ‘₯ 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.

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

You will:

βœ… Use GSC and GA4 Data to their fullest potential

βœ… Learn Python/SQL for your needs

βœ… Get a complete blueprint for auditing websites

βœ… Learn how to 10x your productivity

βœ… 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!

Also in ebook:

Think Like A Web Analyst

This course teaches you to:

βœ… frame Analytics problems

βœ… understand which metrics matter

βœ… managing Web Analytics projects successfully

πŸ“š Recommended Reads - Peak Content πŸ—»

Read these peaks:

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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Bernerstrasse SΓΌd 167, Zurich, Switzerland
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Seotistics - Web Analytics + Business + Strategy

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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