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Reading Time: 3 minutes

When traffic reaches a web page titled “Page not found” in Google Analytics 4, you know something went wrong, but you usually do not know much else.

Which URL was requested? Did the visitor come from your own website, another website, or bot traffic? Was it a real broken link or just noise?

That is the gap this setup solves. With a small Google Tag Manager and GA4 configuration, you can capture the attempted URL and referrer whenever a 404 web page loads. That gives you the context needed to diagnose the issue and decide what to do next.

What this setup captures

This setup sends a custom GA4 event called page_not_found whenever a 404 web page loads.

Along with the event, it sends the full attempted URL, the requested path, and the referrer. That means you can see what the visitor tried to access and where they came from.

Instead of seeing only a generic “Page not found” title in your reports, you can see the actual destination that was requested.

Why better 404 tracking matters

Some 404s are real problems. They can reveal broken internal links, outdated destinations, or incorrect external links that cost traffic and hurt user experience.

Others are just noise, such as bots requesting junk URLs that never existed.

The goal is not to treat every 404 the same. The goal is to capture enough context to know which ones deserve action.

How to set up 404 tracking in Google Tag Manager

The first step is identifying your 404 web page condition. On many WordPress websites, the browser title contains the phrase “Page not found.” If that is true on your website, you can use it as your trigger condition.

If you do not already have a Page Title variable available, create one in Google Tag Manager as a JavaScript Variable using:

document.title

Next, create a new trigger in Google Tag Manager.

Name the trigger something like:

404 - Page Not Found

Set the trigger type to:

Page View

Choose:

Some Page Views

Then use this condition:

Page Title contains Page not found

That tells GTM to fire only when a 404 web page loads.

How to send the 404 event to GA4

After the trigger is in place, create a new GA4 Event tag in Google Tag Manager.

Name it something like:

GA4 - 404 Error

Use your existing GA4 configuration tag.

Set the event name to:

page_not_found

Then add these event parameters:

page_location = {{Page URL}}

page_path = {{Page Path}}

referrer = {{Referrer}}

Attach the 404 - Page Not Found trigger to the tag and publish the container.

At that point, GA4 will start receiving a dedicated 404 event with enough context to investigate what happened.

How to build the GA4 report

The easiest long-term approach is to build an Explore report in GA4 focused only on the page_not_found event.

Go to Explore and create a Free Form exploration.

Add these dimensions:

Event name

Page path and screen class

Add this metric:

Event count

Then apply a filter where:

Event name exactly matches page_not_found

This gives you a simple report showing which broken URLs are being requested most often.

Once data is flowing, add referrer-related dimensions if they are available in your property.

How to interpret the data

Once 404 tracking is live, the next step is deciding what kind of problem each broken URL represents.

If you see a clean-looking path that resembles a real article, category, or resource, that is often a legitimate issue. It may be an outdated internal link, a changed URL, or an old destination that still receives traffic.

If you see a bad path with an external referrer, that usually points to an incorrect backlink. In many cases, a redirect is the right fix.

If you see bizarre paths that never looked like real website content, especially with no meaningful referrer, that is often just spam or automated scanning. In those cases, the right action may simply be to ignore it.

What action you can take from this data

The value of better 404 tracking is that it gives you a short list of decisions instead of a vague warning.

If the broken URL is caused by a bad internal link, fix the source link.

If the URL used to exist and still gets meaningful traffic, consider a 301 redirect.

If the request comes from another website, decide whether the traffic is worth recovering with a redirect.

If the path is obvious junk, treat it as noise and move on.

Why this setup is worth it

Out of the box, GA4 can tell you that a 404 happened. This setup tells you what was requested and where it came from.

That makes it easier to fix broken internal links, recover traffic with redirects, and ignore junk requests that do not matter.

It is a small implementation, but it turns vague 404 reporting into something you can actually use.

Reading Time: 4 minutes

Google Analytics (GA4) provides useful traffic source reporting, but referring domain data can quickly become messy. The same platform may appear in multiple forms such as linkedin.com, www.linkedin.com, or lnkd.in. Google referrals may appear as google.com, mail.google.com, or docs.google.com.

When these variations are not cleaned up, referral reporting becomes fragmented. Instead of clearly seeing which platforms drive traffic, analytics reports fill up with dozens or even hundreds of inconsistent domains.

This article explains how to categorize referring domain traffic in GA4 using Google Tag Manager. The approach normalizes messy referrer values and assigns them to clear traffic categories such as social platforms, search engines, internal traffic, AI tools, or spam domains.

The result is much cleaner referral reporting and a much easier way to analyze where your traffic actually comes from.

The Problem With Referring Domain Data in GA4

Referring domains represent the website that sent a visitor to your website. In theory this sounds simple, but in practice the data can become messy very quickly.

One platform may appear under multiple domain variations. Mobile apps, redirect services, and shortened links can all generate slightly different referrer values. The result is fragmented reporting that makes it harder to compare traffic sources over time.

For example, LinkedIn traffic may appear under several variations.

linkedin.com
www.linkedin.com
lnkd.in

Each variation appears as a separate referrer in GA4, even though they all represent the same platform.

The same issue appears across many other platforms including Google, Pinterest, Medium, and social networks.

Normalization and Categorization Explained

This solution uses two related steps.

First, referring domains are normalized. This means multiple domain variations are cleaned into a single consistent value.

Second, those normalized values are categorized into meaningful traffic groups.

The workflow looks like this.

Raw referrer
→ Normalized domain
→ Traffic category

For example.

lnkd.in
→ linkedin
→ social

mail.google.com
→ google
→ search

marketingwithdave.com
→ internal
→ internal

startraffic.online
→ spam
→ spam

This process dramatically simplifies referral reporting.

Why Categorizing Referrers Is Valuable

GA4 already identifies traffic channels such as Organic Search, Social, and Referral. However, those channels do not always show which specific platform generated the visit.

Categorized referring domains allow you to see traffic at a much more meaningful level.

Instead of only seeing social traffic, you can more clearly separate traffic from platforms such as LinkedIn, Instagram, Pinterest, or X.

This is especially helpful if you promote content across multiple platforms and want to measure which ones actually drive visits.

It also allows you to quickly separate legitimate traffic from spam referrals or internal visits.

Categories Worth Tracking

A good first implementation should classify several common types of traffic.

Search engines such as Google, Bing, DuckDuckGo, and others.

Social platforms such as LinkedIn, Instagram, Pinterest, TikTok, Medium, Reddit, and X.

AI tools such as ChatGPT, Claude, and Perplexity. These platforms are increasingly appearing in referral data as AI assistants begin linking directly to websites.

Internal traffic coming from your own domain.

Spam domains that generate junk referrals.

Any domain not yet reviewed should fall into a category called not-classified. This makes it easier to identify domains that need to be reviewed later.

When This Approach Is Especially Useful

This setup is particularly helpful if you publish content regularly across multiple platforms.

It is also valuable if you want to identify AI-generated traffic, remove spam domains from reports, or clearly separate internal traffic from real visitors.

If you build dashboards in Looker Studio or export traffic data to spreadsheets, normalized referrer categories make analysis much easier.

Creating the Referrer Normalization Variable in GTM

The normalization logic is implemented using a Custom JavaScript variable in Google Tag Manager.

Create a new variable using the Custom JavaScript variable type and paste the following script.

function() {
  var ref = document.referrer;

  if (!ref) return 'direct';

  var host = '';
  try {
    host = new URL(ref).hostname.toLowerCase();
  } catch (e) {
    return 'not-classified';
  }

  host = host.replace(/^www\./, '');

  if (host.indexOf('marketingwithdave.com') > -1) return 'internal';

  if (host.indexOf('google.') > -1) return 'google';
  if (host.indexOf('bing.com') > -1) return 'bing';
  if (host.indexOf('duckduckgo.com') > -1) return 'duckduckgo';

  if (host.indexOf('linkedin.com') > -1 || host.indexOf('lnkd.in') > -1) return 'linkedin';
  if (host.indexOf('facebook.com') > -1) return 'facebook';
  if (host.indexOf('instagram.com') > -1) return 'instagram';
  if (host.indexOf('pinterest.com') > -1 || host.indexOf('pin.it') > -1) return 'pinterest';
  if (host.indexOf('tiktok.com') > -1) return 'tiktok';
  if (host.indexOf('medium.com') > -1) return 'medium';
  if (host === 't.co' || host.indexOf('twitter.com') > -1 || host === 'x.com') return 'x-twitter';

  if (host.indexOf('chatgpt.com') > -1 || host.indexOf('chat.openai.com') > -1) return 'chatgpt';
  if (host.indexOf('claude.ai') > -1) return 'claude';
  if (host.indexOf('perplexity.ai') > -1) return 'perplexity';

  if (host.indexOf('startraffic.online') > -1) return 'spam';

  return 'not-classified';
}

Name the variable something descriptive such as JS – Referrer Normalized.

Adding the Variable to Your GA4 Tag

Open your GA4 Google Tag or page_view tag in Google Tag Manager.

Add a configuration parameter with the following values.

Parameter name: referrer_normalized
Value: {{JS – Referrer Normalized}}

This sends the normalized value to GA4 with each page view.

Test the implementation in GTM Preview mode before publishing.

Creating the GA4 Custom Dimension

After publishing the GTM changes, create a custom dimension in GA4.

Use the following settings.

Dimension name: Referrer Normalized
Scope: Event
Event parameter: referrer_normalized

This allows the normalized value to appear in GA4 reports and explorations.

Important GA4 Limitation

GA4 custom dimensions are not retroactive. Historical referral data will not be reprocessed.

The categorized values will only appear for traffic collected after the implementation goes live.

Expanding the Classification Over Time

Your first version does not need to classify every possible domain.

Start with the platforms you already know are important. Over time, review domains that appear under not-classified and add additional rules as needed.

This gradual approach allows the classification system to evolve alongside your traffic patterns.

Visualizing the Workflow

A simple diagram can help illustrate the process.

Raw Referrer
→ Normalized Domain
→ Traffic Category

This visual representation works well as a blog image and helps readers quickly understand how the transformation occurs.

Final Thoughts

Referring domain data in GA4 often becomes fragmented and difficult to analyze. Normalizing and categorizing referrer values provides a simple way to transform messy domain data into clear traffic insights.

With a small amount of logic in Google Tag Manager, referral traffic can be organized into meaningful categories that are much easier to analyze in GA4 dashboards and reports.

If you regularly share content across multiple platforms or want to better understand where your visitors originate, categorizing referring domain traffic is one of the most useful analytics improvements you can implement.

Reading Time: 6 minutes

If you want better content analysis in Google Analytics 4, tracking just URLs is not enough. A list of individual web pages can tell you what got traffic, but it does not make it easy to understand which kinds of content are actually driving your website forward.

That is where page type tracking can help. Instead of only measuring individual URLs, you can classify each web page by format and send that value into GA4 as a custom parameter. This makes it possible to analyze performance by content type, such as case studies, book summaries, calculators, site search, the home page, and anything that does not yet fit into a defined bucket.

In my case, I used Google Tag Manager to identify page types based on URL patterns and page titles, then passed that value to GA4 using a custom parameter called page_type.

What page type tracking does

Page type tracking adds a structural content layer to your analytics. Instead of only seeing that a specific web page got traffic, you can now understand whether that traffic came from a case study, a calculator, a book summary, a home page visit, or some other kind of content.

Examples of page type values might include:

home-page
case-study
book-summary
calculator
site-search
404
not-classified

Once the custom dimension had been collecting data for a few months, I could finally analyze traffic by page type instead of individual URLs. Here’s what that report looks like on my own site.

Why page type tracking is useful in GA4

Once page type is available as a custom dimension, GA4 becomes much more useful for content analysis.

You can answer questions like:

1. Which page types attract the most sessions?
2. Which page types drive the strongest engagement?
3. Which page types are most likely to bring in organic traffic?
4. How much of my website still falls into a general not-classified bucket?

This becomes even more powerful when combined with topic tracking, because you can analyze both the format of the web page and the subject of the content.

Before you start

This walkthrough presumes:

1. You are using Google Tag Manager.

2. Your GA4 page_view event is firing through GTM.

3. Your website has consistent URL patterns or titles that can be used to identify different page types.

4. You want page types to be stable over time and mutually exclusive.

Why page type rules need to be precise

A page type should describe the format of the content, not the topic. For example, case study is a page type. Martech is a topic. Those are different things and should be tracked separately.

The goal is to give each web page one clear page type value. That keeps the classification stable and makes reporting easier to trust.

It is also important not to create too many page types too quickly. A small, meaningful set is usually better than trying to classify every edge case on day one.

Step 1: Define your page type values

Start by deciding which page types your website actually needs. In my setup, I used these values:

home-page
case-study
book-summary
calculator
site-search
404
not-classified

The first several values represent meaningful content structures. The final value, not-classified, acts as an intentional catch-all bucket.

Why not-classified matters

If no matching rule is found, the script returns not-classified.

This is not a mistake. It is a useful fallback. It gives you a deliberate “other” bucket so that uncategorized web pages do not get confused with GA4 system labels like not set. Over time, monitoring the percentage of sessions tied to not-classified can help you decide whether more page types are needed or whether the current taxonomy is already healthy.

Step 2: Create a Custom JavaScript Variable in Google Tag Manager

In GTM, create a new User-Defined Variable using the Custom JavaScript variable type.

Name it something like:

Page Type

Then use logic that checks the current URL path and page title to determine the correct page type.

Here is an example:

function() {

var path = window.location.pathname.toLowerCase();
var url = window.location.href.toLowerCase();
var title = document.title.toLowerCase();

if (
  title.includes("marketing with dave - all things digital marketing") ||
  title.includes("marketing with dave | all things digital marketing")
) {
  return "home-page";
}

if (title.includes("404")) {
  return "404";
}

if (url.includes("/search/?q=")) {
  return "site-search";
}

if (path.includes("case-study")) {
  return "case-study";
}

if (path.includes("book-summary")) {
  return "book-summary";
}

if (path.includes("the-a-to-z")) {
  return "the-a-to-z";
}

if (path.includes("the-evolution-of")) {
  return "the-evolution-of";
}

if (
  path.includes("calculator") ||
  path.includes("analyzer") ||
  path.includes("tools")
) {
  return "calculator";
}

return "not-classified";

}

How the script works

This script checks a small set of rules in order.

1. It looks for the home page title.

2. It checks for a 404 title.

3. It checks for a site search URL pattern.

4. It checks for specific URL structures like case-study, book-summary, the-a-to-z, and the-evolution-of.

5. It checks for calculator-related words in the URL.

6. If no match is found, it returns not-classified.

The order matters. More specific rules should always come before the fallback bucket.

Step 3: Add page_type to your GA4 page_view tag

Once the variable is created, open the GA4 page_view tag in Google Tag Manager.

If you have both a standard page_view tag and an internal version, update both so the data stays consistent.

In Event Parameters, add:

page_type = {{Page Type}}

This tells GTM to send the resolved page type value with each page_view event.

Step 4: Preview your changes in GTM

Before publishing, use Preview mode in GTM.

Visit several web pages on your website and verify that the variable returns the right values.

Examples:

A case study URL should return case-study.

A book summary URL should return book-summary.

A calculator or analyzer web page should return calculator.

The website home page should return home-page.

A regular article that does not match any defined rule should return not-classified.

Step 5: Publish the GTM container

Once Preview mode confirms the values are correct, publish the GTM container.

At that point, GTM is sending the page_type parameter to GA4, but GA4 still needs one final setup step before you can use it in reporting.

Step 6: Register the custom dimension in GA4

In GA4, go to Admin, then Custom definitions.

Create a new custom dimension with these settings:

Dimension name: Page Type

Scope: Event

Event parameter: page_type

Save the dimension.

From that point forward, GA4 will store and report on the page_type parameter.

Important note about historical data

GA4 custom dimensions are not retroactive.

This means page type data will only be available for traffic collected after the GTM changes are published and the custom dimension is created.

If you want a historical view, you would need to recreate it manually using existing URLs in a spreadsheet or another reporting layer.

How to analyze page type in GA4

Once the data starts flowing, one of the simplest and most useful reports is an Exploration showing sessions by page type.

A basic starting point is:

Rows: Page Type

Values: Sessions

This quickly shows what percentage of traffic is going to case studies, calculators, book summaries, site search, and everything else.

You can also combine page type with content topic to build a matrix like:

Rows: Content Topic

Columns: Page Type

Values: Sessions

That helps you understand both what the content is about and what format it takes.

Why this works well for content-driven websites

If your website includes multiple recurring content formats, page type tracking gives you a much better structural view of performance.

Instead of relying only on individual URLs, you can now see how the website performs by content model.

That is useful for editorial planning, content investment decisions, and identifying which kinds of web pages are becoming your strongest entry points.

Final takeaway

Page type tracking is one of the most useful content upgrades you can make in GA4. It adds a structural layer that makes your reporting far more meaningful than a simple list of URLs.

If your website already has recognizable URL patterns or stable page titles, Google Tag Manager can classify those web pages automatically and send the values into GA4 with very little maintenance required later.

And by keeping not-classified as an intentional fallback, you retain visibility into the portion of your website that still sits outside your current content taxonomy.

Reading Time: 5 minutes

If you run a content-heavy WordPress website, there is a good chance you care about more than just web page views. You may also want to know which content topics actually drive sessions, engagement, and returning visitors.

That is where category tracking can help. In my case, I wanted Google Analytics 4 to capture the WordPress Category assigned to each article so I could analyze traffic by topic. This is especially useful when your content spans areas like analytics, martech, paid advertising, SEO, content marketing, and more.

The important detail is that WordPress Categories and WordPress Tags are different. If your website uses Categories as the main topic label on articles, your Google Tag Manager setup needs to pull from the Category link, not from a Tag link.

What this setup does

This approach reads the article’s visible WordPress Category from the web page, normalizes it into a GA4-friendly value, and sends it with your page_view event as a custom parameter.

For example:

Martech becomes martech

Paid Advertising becomes paid-advertising

Search Engine Optimization (SEO) becomes search-engine-optimization-seo

Artificial Intelligence (AI) becomes artificial-intelligence-ai

Why use Category tracking in GA4?

Once this is set up, you can analyze website performance by topic instead of just by URL.

This lets you answer questions like:

Which categories attract the most sessions?

Which categories drive the longest engagement time?

Which categories perform best for case studies, calculators, or other content types?

If you already have page type tracking in place, category tracking becomes even more powerful because you can compare format and topic together.

Before you start

This walkthrough presumes:

1. You are using WordPress.

2. Your article category appears visibly on the web page as a link.

3. The category link uses a URL structure containing /category/.

4. You already have Google Tag Manager installed.

5. Your GA4 page_view tag is firing through GTM.

Step 1: Confirm that your website uses Categories, not Tags

This part matters more than people realize.

If your website displays the topic label under the title and that label links to a URL like /category/martech/, then your GTM script should look for Categories.

If your setup uses WordPress Tags instead, the link would usually contain /tag/.

Step 2: Create a Custom JavaScript Variable in Google Tag Manager

In Google Tag Manager, go to Variables and create a new User-Defined Variable.

Choose Custom JavaScript as the variable type.

Name it something like:

Content Topic

Then use this script:

Some tutorials detect categories using URL patterns like /category/. That works on some WordPress websites, but many themes remove the category base from URLs. A more reliable approach is to target the category element directly in the HTML.

function() {
  var category = document.querySelector('.ast-terms-link a');
  if (!category || !category.textContent) return 'not-classified';

  return category.textContent
    .trim()
    .toLowerCase()
    .replace(/[()]/g, '')
    .replace(/\s+/g, '-')
    .replace(/[^a-z0-9-]/g, '')
    .replace(/-+/g, '-')
    .replace(/^-|-$/g, '');
}

Note: The selector .ast-terms-link a works for Astra theme. If you are using a different theme, inspect the category link on the web page and adjust the selector accordingly.

How the script works

This script does five things:

1. It looks for the first link on the web page that contains /category/.

2. It grabs the visible text of that link.

3. It trims extra spaces.

4. It converts the value to lowercase.

5. It removes special characters and replaces spaces with hyphens.

If no category is found, it returns not-classified.

This acts as a deliberate “other” bucket. Instead of mixing uncategorized traffic with GA4 labels like not set, you can clearly see what portion of your content taxonomy is missing or incomplete. Over time, the goal is not to eliminate not-classified, but to keep it at a healthy percentage.

Step 3: Add the parameter to your GA4 page_view tag

Next, open the GA4 page_view tag in Google Tag Manager.

If you have a standard page_view tag and a separate internal version, make sure you update both so the data stays consistent.

In the Event Parameters section, add a new parameter:

content_topic = {{Content Topic}}

That tells GTM to send the normalized category value with every page_view event.

Step 4: Preview the changes in GTM

Before publishing, use Preview mode in Google Tag Manager.

Open a few different article web pages and confirm that the Content Topic variable returns values you expect from your WordPress Categories.

For example, on a Martech article, the variable should return:

martech

On a Paid Advertising article, it should return:

paid-advertising

If you see a value that does not match one of your approved categories, your selector may be pulling from the wrong part of the web page.

Step 5: Publish the GTM container

Once preview mode looks good, publish the container.

At this point, GTM is sending the custom parameter to GA4, but Google Analytics 4 still needs one more step before you can use it in reports.

Step 6: Register the custom dimension in GA4

In GA4, go to Admin, then Custom definitions.

Create a new custom dimension with the following settings:

Dimension name: Content Topic

Scope: Event

Event parameter: content_topic

Save the custom dimension.

From that point forward, GA4 will store and report on the content_topic parameter.

Important note about historical data

GA4 custom dimensions are not retroactive.

That means this setup will only classify data collected after the custom dimension is created and GTM is published.

If you want historical analysis, you will need to build it manually using a spreadsheet or another reporting layer.

How to use the data in GA4

After the data starts flowing, the easiest place to analyze it is in Explorations.

A simple starting report is:

Rows: Content Topic

Columns: Page Type

Values: Sessions

This makes it easy to see which topics are driving traffic and how those topics map to different kinds of content.

Examples might include:

Case studies in martech

Book summaries in leadership

Articles in analytics

Calculators in website-related topics

Why this works well for WordPress websites

The biggest advantage of this setup is that it uses the taxonomy you already maintain in WordPress.

You are not inventing a separate analytics classification system. You are simply exposing your existing editorial structure to GA4.

That makes the reporting much easier to trust.

Can this work outside WordPress?

Yes, but the implementation details change.

The broader concept is the same: identify the topic label on the web page, extract it with GTM, normalize it, and send it to GA4 as a custom parameter.

What changes is the selector. Instead of looking for a WordPress Category link containing /category/, another platform might use a different class name, data attribute, or metadata element.

So the process is portable, but the selector is platform-specific.

Final takeaway

If your WordPress website uses Categories as the primary topic label for articles, GTM should pull from Categories and not Tags. That one detail can be the difference between clean, trustworthy GA4 topic reporting and a messy dataset you cannot rely on.

Once you set this up, GA4 becomes much more useful for understanding what your content is really doing by subject area, not just by individual URL.