Use Data Or Be Used By Data!

The August 4 issue of Seotistics is here for you!

Last week I've talked about the foundations of Analytics...

now I will show you how it usually works with B2B enterprise businesses.

This is my direct experience and I had to make some concepts more "generic" to avoid being too specific.

Most of the skills you need are relatively basic but the real issue is how you combine them together.

P.S. Maybe I will cover this topic in more depth in the next issues 👀

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📣📣 Important Announcements 📣📣

I am updating my Discord community to make it a reference for my readers.

Since social media have lower reach and emails can bounce, communities are the only way to ensure you get my message.

We do have a baby forum too now and I will try to organize my thoughts there.

The Reality Of Many Companies

Before we go on, it's time to remember what you usually see in the wild.

• legacy systems with Excel
• close to no definition of metrics
• no unification of systems whatsoever

Now, your job as an analyst is not to drive architectural change but to be sure what gets recorded makes sense for the business.

Marketing data isn't even taken seriously by many and it's your role to push it 👀

The priority is defining what needs to be done and what is useful for business purposes.

Data Sources

The part that many love, the stack!

As I said in some past issues, the stack for big websites could look like this:

In B2B, it's normal to have a CRM, aka the system you use to manage your customer relationships and opportunities.

Common choices include Salesforce and Hubspot but the ideas are the same.

The hardest part is connecting these data sources and hopefully getting something of value out of them.

Web data often sits alone but don't worry, there are some ways to connect it to the rest.

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Once you have enough experience, there won't be any need to go through docs or testing.

You should already know how it works and why.

The same isn't always true for data sources you aren't familiar with, like ERP data.

How many of my readers have experience with SAP BW?

Well, not many and that's 100% normal, you can't be tasked with everything!

The takeaway here is being comfortable with knowing there is more than marketing and web in a company.

A Recap Of B2B

It's impossible to talk about B2B without recalling how it works (more or less).

Unlike B2C, you have other issues to face like:

• lower search volume (SEO)
• longer sales cycle
• campaigns aren't as effective as in B2C without a brand (PPC)

All of this has an impact on the data we use and how we use it.

Countless B2B companies overspend in Ads without even having a solid brand...

would you buy a $10K product just because you saw it on Instagram? Maybe not.

Understanding your sales cycle is quite important when analyzing data and giving advice.

You can't expect immediate improvements or sales if you have a long sales cycle (e.g. 2 years).

Sure, leading indicators like your traffic metrics can point you are going in the right direction and even form submissions.

But it doesn't actually mean you will see $$$ immediately.

What Has To Be Done

A good amount of time is spent on defining metrics and definitions.

Building understanding across departments is no easy feat, especially outside of Web.

A Marketing Qualified Lead (MQL) can have many definitions and distinctions based on how qualified it can be.

I've showed a simplified version of this in the past:

For enterprise use cases, it's much more than a simple list. Remember, it's mostly about people and processes.

Speaking of which, documentation is key to ensure everything is recorded and available to other people.

Otherwise, this knowledge gets lost forever.

I've met people who sit on the other end of the spectrum... those obsessed with documenting processes.

Look, the important thing is proving the value of data, documentation can wait if you don't have any budget.

Controversial take, I know, but stakeholders want to see results.

Data Modeling

Yes, if you have different data you must consider how to model it.

From an engineering perspective, performance makes a big difference and so does cost optimization.

I won't bother you with this... we care about analysis after all.

Having too many tables can make the easiest of analyses super hard.

The model above is simple and straightforward, it's easy to understand it even without much context.

Now, what if we considered other sources, like:

• GSC
• Google Ads
• CRM
• CMS
• Transactional data and/or ERP

you'd need to model them too, right?

This is the part when web data is no longer siloed and gets mixed with the others.

A nice trick is to find which questions every data source can answer before going too far.

The (Very) Obvious Value

Unlike marketing, the value of data work is quite immediate to grasp.

Not having data at all is a massive boost compared to before... and this is why companies are obsessed with data ingestion.

Where many quickly lose it is in which data is used and how.

And this is why you always follow this approach:

So you can speed up the process and be quicker with what you need.

Not having to overpay enterprise tools and waste time on tickets/back and forth is massive value.

More specifically for Web Data

• not having to double check or waste time on data validation
• not having to pull the data manually
• not using GA4 or obsessing over specific tools

The reality of many is wasting time between data and do PLENTY of manual work that can be avoided.

Ever had that "accuracy" anxiety?

I did, it's fully avoidable.

This is why in my course "Think Like a Web Analyst" I teach how to avoid manage projects:

The Foundational Skills

The usual stuff that I mention every single issue:

• SQL + notions of data warehouses
• Looker Studio / PowerBI knowledge
• Data modeling
• Marketing/business understanding
• Knowedge of GSC/GA4/Google Ads schema in BigQuery

No Python at all since all the data can be often pulled via SQL.

While all of this looks simple, you need a good mentor and some experience with the data you play with.

I unfortunately see people that attend generic courses (cough cough Udemy) that are completely unrelated to what we need.

The answer is simple and effort is require on your end to make sure the project goes well.

I've met many agencies that didn't even know how to work with the BigQuery schemas... or have basic SQL knowledge.

Folks, don't be like them or you may lose your contracts 👀

The Most Common Myths

All of the influencers things you hear on LinkedIn, like:

• Data Vault (for engineers, won't even bother)
• Self-service Analytics (the worst imho)
• Agile/Scrum BS

The illusion that every person can use data without any support is present in most companies.

In the last year, I've seen more and more people voice their concerns over this concept.

If you have some experience with it, you'd know that people need guidance and/or don't have the time to play with data.

Agile/Scrum is the other curse of many data projects I've seen with my own eyes.

This can work with software, not with Analytics work.

There can't be any schedule or sprint, embrace chaos instead.

Most of these topics are idealistic. They sound nice while in reality they barely work.

👥 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:

🔎 Analytics For SEO Ebook - Course / Ebook

This is the ultimate resource on GA4 and GSC.

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!

Also in ebook:

📚 Recommended Reads - Peak Content 🗻

The usual good stuff to read to start your week:

• ​Analytical Skills for AI and Data Science : Building Skills for an AI-Driven enterprise​
• ​Data Literacy In Practice​
• ​Becoming a Data Head​
• ​Storytelling with Data​
• ​Small VS Big Websites: Stack, Strategy & Action Plan​
• ​Strategy + Web Analytics: How To Tie Data To Results​
• ​A Business-Oriented Framework For Web Analytics​

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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