Use Data Or Be Used By Data!

The September 16 issue of Seotistics is here for you!

Everyone talks about data but not many showcase actual processes that support the business.

Seotistics exists to tie (Web) data to business and I am here to show you some nice examples.

You should adjust them based on your specific use cases, so follow the logic!

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Optimizing/Managing Content

This is one of the most common use cases because pretty much everyone has some content.

What shown below is more suitable for medium-large websites though:

I wil go over each separate process and show you how it's done.

1. Tagging Pages

Now, this is what most of the time I get asked and what I deem a crucial activity for any website.

It's impossible to analyze the entirety of a website page by page if it's decently sized.

For this reason, we resort to groups to get aggregated metrics.

In most real-life scenarios, you won't have much order, excluding the tags and categories in the CMS.

If you want a different type of classification, things get even messier.

The solution here is to break down the problem and to think methodically.

There are several approaches:

Clustering
Fuzzy Matching
Rule-based

Prevention: use Airtable to do it from scratch. New websites with labeled data are a joy to work on.

P.S. If you need tags/categories but can't use APIs, then you need to scrape the whole website!

2. Page Performance

I briefly mentioned last time that you should classify pages by performance as a way to speed up decision-making.

Now, how you do it can vary a lot but I've found the following methods to be quite consistent.

Once you can categorize pages, you can tie a group to actions, as shown below:

This means that you should already know what to do with such groups!

There isn't anything advanced here, it's the usual same stuff under a different flavor.

I am just showing you the power of Operations.

The script for running Content Decay is contained in my ebook and showed with even more detail in my course (yes, Python + SQL).

For evergreen content, I invite you to check the BigQuery handbook you received via email.

3. Research Flow

Keyword Research is a spicy topic because everyone has a different process.

The one I outline below is a generic one that can fit the needs of many:

There are too many sources you can use for Keyword Research and it's pretty much the usual SEO stuff.

Reddit/Quora/Forums offer great insights and I always recommend checking what they are ranking for.

Or... you can scrape subreddits yourself with Python!

Once you have all the data, you combine them and feed them into a SERP Clusterer.

With that output, you can take the name of each group and feed it into AI... to generate ideas, briefs and then output it to GDocs or your CMS.

This is what automation looks like.

If you want to be even more methodical, you can add Airtable as a middleman to store your data and use Make/Zapier to connect to OpenAI.

Dealing With Products

Content and products are separate topics that deserve different attention.

Anyone with basic SEO/CRO experience knows that they follow completely different rules.

Sure, categories can be similar to content to some extent but they require different actions.

I will simply talk about products here.

You can use the process below:

Evaluating products is largely dependent on the conditions you define.

A product getting 0 organic traffic is normal, as long as other channels bring traffic.

At the same time, your most visited product pages aren't necessarily the most profitable.

There are too many considerations to make on products and traffic alone doesn't make the cut.

But you can have some nice process to decide what can be trimmed or not and do some nice Product Line Simplification.

P.S. GA4 gives you the option to play with Path Exploration and create custom events.

Use it accordingly!

Speaking of business, some considerations about products:

some you need for price discrimination so selling the most expensive variants
others are severely affected by seasonality
some products act as baits/magnets to attract people

So don't limit yourself to data alone... but don't trust your gut too much either!

My Stack

All of what I mention here is contained in my article about my SEO Analytics stack.

Of course, you can integrate different tools for other marketing channels.

To be quite honest, GA4 is fine for the majority of cases!

Even if you are non-technical, you can easily make your way through with ChatGPT and a healthy dose of Make/Zapier to connect to APIs.

Really, now writing Python/SQL code is super simple and more advanced use cases are handled by engineers anyway!

BigQuery, GSC and GA4 will get you far... so I'd bother learning them now!

If you want to learn all of this and how to add value with Web Analytics, check my course:

[Analytics For SEO Course - v2]

If you want to be guided and start from scratch, this is the course for you!

All about Analytics and using data for SEO with consistent updates!

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!

P.S. (Ab)use the referral system and get additional discounts 👀

Learn Analytics for SEO Now!

Tying Everything To Action

The big problem of data (or Python) when applied to SEO is that most stuff is pointless or is a shiny object.

This is the main reason why I focus on processes and analysis rather than testing the craziest models.

After a LOT of failures and years of experience, I can say:

Reject fluff
Simplify as much as you can (just not too much)
Question the status quo

The reason I write is because the current SEO and even Web Analytics landscape isn't able to meet our needs.

As long as you keep it grounded, your work will produce much more value than "traditional" standards.

The Pitfalls Of Traditional Processes

The real issue about current processes in Web Analytics is that they are not scalable.

Most of what we read online works for small websites or boutique use cases, it just doesn't scale up.

The most common pain points I see are:

Agencies/Professionals can't report on meaningful metrics quickly
High % of inaccuracies across reports (thanks, spreadsheets)
More time is spent on pointless tasks than on what drives growth

Actual digitalization goes through progress but many seem caught in the AI trap.

As you have learned today, engineering is where the gold is at.

Automation doesn't mean low quality. Many confuse this step with analysis.

After you automate, you still have to analyze. Automation is as good as the people behind it.

The Solution

The best solution to all these problems is investing in data, in this case, processes and domain knowledge.

You must know your industry and understand the business implications.

If not, you are just another technical worker.

Speaking of processes, you should get familiar with stuff like Google Cloud Platform if you love them.

It's the most accessible cloud platform out there if you like web data.

(Plus, I can help you lol).

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