How To Conduct a Content Analysis in Google Analytics 4

Intro

GA4 offers a wide range of analytics features to analyze content. However, with so many options available, it's easy to feel overwhelmed or unsure of where to begin your content analysis journey, often resulting in “analysis paralysis.”

Content analysis raises many questions, such as which aspects to prioritize and how to measure content success. Various approaches exist, mainly because each website has different goals.

Despite these differences, there are common questions, answers, and repetitive processes that apply to content analysis universally.

Here’s what we’ll cover, along with step-by-step examples:

• What Is Content Analysis and How Is It Done
• Prerequisites
• How Do You Analyze Your Content Performance
• Practical Examples of Content Analysis
• Efficiency Hacks
• Can Google Analytics Help With Content Ideas

Prerequisites

Goals, Objectives and KPIs

You would be surprised to see the vagueness that organizations have with their digital objectives, including even established companies. To effectively analyze data that directly impacts your company, you must be aware of your key performance indicators (KPIs).

If you're not a decision-maker, you are not responsible for creating the company's objectives, goals, and KPIs, since the company should have already established them. However, your experience can be valuable in helping to establish these, particularly for smaller projects or for a client you need.

Tracking

After establishing your goals, objectives, and KPIs, you'll have a clear idea of what to track. For optimal results, it's recommended to utilize a tag management tool such as Google Tag Manager to track interactions ranging from basic to advanced complexities.

Forecasting

Content analysis resources rarely include the concept of prediction, but producing content without knowing your direction can be counterproductive. Understanding the combination of metrics, such as pageviews, is essential for further analysis.

Although statistics and regression analysis can be involved in predicting outcomes, it doesn't have to be that complicated.

By simply keeping a log of a metric in a spreadsheet, such as pageviews, conversions, or revenue for each month, you can identify patterns and predict averages or targeted numbers, as well as determine necessary actions.

For instance, if you consistently receive 800 to 1000 pageviews per month for six months, you can establish a performance threshold and predict future outcomes.

Example:

• Our average page views are around 800-1000, so if we don't implement anything new and keep doing what we do next month, we can anticipate the same range of page views.
• If we get more than 800 to 1000 page views, we’re overperforming (let's do more of what worked).
• If we get less than 800, we’re underperforming (need to investigate why).

How Do You Analyze Your Content Performance

Content analysis can be done in many ways, by different people, in no particular format.

Nonetheless, it is possible to follow the steps below to detect trends and patterns, content themes, conversion opportunities and achieve more accurate figures.

🚨 Note: We will spend some time looking at the Content group, but all the steps and methods described apply to any Primary dimensions of the Pages and screens report and the Landing page reports. They also apply to Explorations.

GA4 Content Analysis Steps

Here is an overview of the content analysis steps in GA4:

1. Date range
2. Content Analysis Overview (ABC model)
3. Segmentation
   1. Conversions
   2. Secondary dimensions

These steps apply mainly to these 3 reports:

• Pages and screens
  1. Content group
  2. Page title
• Landing page
• Explorations

Let’s take a closer look at the Content analysis steps through a real-world example.

1. Date range

Before starting your analysis, it is important to know which date range to choose. The date range can be based on the average time it takes for a user to convert or the duration of your marketing campaigns. This can also be your business cycle.

However, for the content analysis precisely, we look at 2 types of date ranges which produce 2 types of periodic data:

1. Trending data
2. Historical data

For Trending data, 3 months is a common practice to identify trends that are going up, down or flat.

Three months of data is also the ideal time if you are starting to collect data. This is because the 1st and 2nd months in general tend towards planning and tracking implementations, and the 3rd month is therefore the result of the previous efforts. In the fast-paced world of the internet, trend analysis plays a crucial role to adapt to the latest changes.

As far as historical data is concerned, 1 year is enough to identify seasonality and the most popular pages over the long term. In other cases, you can use 6 months and more than a year.

An important point for both trending and historical data analysis is to use date range comparisons.

You can select your dates in all standard GA4 reports. Go to the left navigation and select Reports. Then click on the date range.

Finding the GA4 date range selector

Select your date range and click Apply. We selected the last 90 days.

Selecting a date range

2. Content Analysis Overview

Analyze any content to have a quick and general understanding of how good your content is at generating traffic, engagement and conversions.

The idea (borrowed from GA3) is to divide the metrics of your table into 3 parts:** Acquisition**, **Behavior **and Conversions. This also serves as a visual aid while conducting your analysis. The following image shows where these separations are:

Dividing reports into ABC

Acquisition: Determine how your content is performing in terms of awareness with the amount of Views, and bringing in Users. For the Landing page report in GA4, you’ll have instead - Sessions, **Users **and New users.

Behavior: Determine how users interact or engage with your content. The definition of engagement may vary depending on your preferences. For a quick analysis, analyze the Average Engagement Time and Event Count metrics. I included Event Count in here, as these interactions may not always lead to conversions, but are still significant indicators of user interest.

Conversions: Determine what pages contribute to what matters the most to your business (e.g., purchases, sign-ups, your own way of measuring awareness and engagement, etc.).

The idea with the ABC model is to look for trends and patterns.

Whichever column you look at, try to see the numbers that standout vs those that don’t. Look for pages with similar numbers to have an idea of your averages and take notice of those that are either above or below them, because each could be a conversion opportunity.

For example, pages with a high number of views but low conversions could indicate that users expectations were not met. These pages would need to be investigated and optimized for conversions (A/B testing can apply here). In conjunction with your GA4’s ABC. Always make sure to ask these 2 questions:

1. Does it make sense?
2. So what?

🚨 Note: The “does it make sense” question doesn't have only negative connotations. A few posts ranking higher than expected could indicate new content opportunities that interest your audience.

You can start applying the ABC model to Content groups in the first stages of your content analysis in the Pages and screen reports.

Segmentation

While it's helpful to have a general understanding of our results, it's important to note that the level of aggregation we're currently at may not provide the specific details that can make a significant difference.

The issue with larger aggregations is that outcomes (both positive and negative) are often attributed to the group as a whole rather than to individual contributors.

For instance, you might assume that Canada is your most profitable market. But in reality, it's a specific city like Mississauga, where the bulk of your customers reside. As a result, attempting to optimize for all of Canada may not yield the desired outcomes.

To gain a clearer understanding of your reports, here are some typical segments to consider:

Conversions

Secondary dimensions

Some useful secondary dimensions:

Demographics (Age and gender)

**Geography **(Town / city, country, region)

Platform / Device (Device category, browser, operating system, etc.)

**Traffic source **(Session source / medium)

Let’s see how all of this works starting with Content groups.

Practical Examples of Content Analysis

🚨 Note: We are starting with the Content group, however, the same process applies to all Primary dimensions of the Pages and screens report and the Landing page reports.

Content Group

By organizing your content into different groups at first, you can gain a global view and quickly identify which areas require more detailed analysis. This process is made easier through content grouping, as opposed to manually analyzing each page.

Let’s take a digital marketing blog that covers topics such as social media, ads, and SEO. By organizing these themes into groups within Google Analytics 4 (GA4), you can easily analyze the performance of each group.

However, you have to set this up. One way is to use Google Tag Manager.

Here's a quick example of how you could do this with Regex.

By using Regex, once a word in the input patterns finds a match in the page path, it will return one of the outputs.

So, if a user goes to _wwww.example.com/how-to-do-seo/, _then the output will be SEO, which will be our SEO content grouping.

In GTM, we created a Regex Table variable using the following configuration:

Content grouping configuration as Regex variable in GTM

Then use this variable in your Tag Configuration:

Content grouping in the GA4 tag configuration

In Field name, we typed content_group (this can be anything you want).

In Value, select the Regex Table variable that we created prior.

Once you save everything, then you are successfully sending data to GA4. The data is immediately available in the **DebugView **but not in your standard reports. You’ll have to wait a day before it gets to your standard reports.

There are 2 places where you can view and analyze your Content grouping data:

1. Pages and screens
2. Explorations

In Reports, go to Engagement, and** then Pages and screens**.

Pages and screens report

Click on the dropdown arrow of the primary dimension and select Content group.

Clicking the dropdown arrow in pages and screens

Select Content group from the dropdown list.

Selecting content group as a dimension

Now you can analyze your data using the table’s metrics.

Content group report in GA4

What can we learn from our Content groups?

🚨 Note: This data is for demonstration purposes only, to familiarize you with the thought process during content analysis.

Using our ABC, we see that the SEO content group is the most popular in terms of views (96001 views). It is also the most engaging content since users spend 1m 14s on average on our SEO pages (most are around 20 to 30 seconds).

That being said, you would think that a content group so successful at captivating users would lead to many conversions, but it’s not the case (only 62 conversions in our example).

The SEO content generated only 62 conversions which is much lower than the social media, which has almost 10 times less views (10,438) and 5 times less engagement (users spend on average only 26 seconds).

However, despite users spending an average of only 26 seconds on our social media pages, the conversion rate is remarkably high with 599 conversions. How could this be?

Lastly, our Ads are quite low in terms of views. Users are more engaged with this content than social media, but the Ads content doesn’t convert at all.

How can we interpret these results?

Let’s use the questions mentioned earlier and apply them in this scenario:

1. Does it make sense?

Let’s take the weird social media case where users convert the most, but have low page views and the lowest engagement.

Upon investigation, we discovered that the social media audience was already familiar with the website's products and services through their exposure on platforms such as Facebook.

As a result, when they arrived at the site, their focus was primarily on signing up rather than engaging with the content.

2. So what?

For the Ads content group, we could increase the content production volume. On the other hand, it could also be business as usual and you may leave it the way it is.

When it comes to the Ads content, the website could definitely use more of this content. The engagement is above the other content groups (besides SEO). In addition to creating more posts on Ads, we could promote these pages to social media audiences, as they have proven to be good at converting..

Remember to use these 2 questions frequently in your analyses.

We can continue our exploration by dissecting the data further and prioritizing the factors with the maximum impact on your business.

For this, we’ll look at conversions.

Conversions

To achieve profitable results, it’s best to focus on conversions.

Conversions are all the actions that you want your users to take and which are related to your goals. The good thing about conversions is that you don’t have to operate in the dark and try to find insights from nowhere.

Starting with conversions means you have a game plan, and you can see right away which content (be it Content groups, Page titles, landing pages etc.) generates the results you want.

To continue with our example, rather than looking at All events in Conversions for our content, we can narrow it down to the Events that matter to our business. This website has a significant amount of subscribers, therefore, one conversion goal that was set up was to track sign-ups.

Here we replaced All events with the** sign_up** conversion event.

Changing events in conversions

We can see that social media is definitely the content group with the most conversions.

At this point, you can add secondary dimensions to dive even deeper into your report.

On the same report, click on the plus sign near the primary dimension.

Adding a secondary dimension

Select the secondary dimension of interest.

Pages and screens secondary dimensions

Repeat the process to analyze the data for each.

Page Title and Screen Class

This is the default dimension in the Pages and screens report.

Feel free to change it with Page path and screen class, but the page title works fine for us.

With the page title and screen class dimension, your report now includes all the pages of your website or app.

Repeat the process covered with the Content group.

Remember:

• The ABC model
• Asking the 2 questions: Is this supposed to happen and so what?

Then consider the following, which are specific, to examine Page title and screen class and find conversion opportunities for your content:

1. Look at the pages that have the highest conversions, compare them with those that don’t and try to understand why that is. Do more of what works and increase the value of the low conversion pages by emulating the features of the pages that are successful.

Pages with high conversions but low Views should be promoted more via different marketing channels.These pages require greater exposure to attract more attention.

2. If you notice pages with a lot of views but low conversions, consider finding ways to increase the conversions of these pages. Adding lead magnets can boost your conversions for example.

But be careful that these high-traffic pages also have reasonable engagement levels. Reasonable is best defined by how things work for you (you can also use industry benchmarks). If engagement is very low, this could mean that you managed to get users attention but did not meet their expectations. This will require further investigation on your part.

Always remember to ask yourself if the pages are doing the job they're supposed to do.

Landing page

Unlike the Pages and screens report which takes into account all the pages of your site, the Landing page report shows you the pages that users see first when they visit your site.

For instance, if you stumbled upon this page while searching for instructions on performing content analysis in GA4, GA4 would classify this page as a landing page if it was your first visit to the site.

Since landing pages are the first impression that users have of your site, they are critical to optimize. Users tend to judge your website based on their experience with the landing page.

🚨 Note: the Landing page report may not be readily accessible in the standard GA4 report. You can include it in your standard report with a few clicks or create it via Explorations, which offers additional metrics like Entrances and Exits that are not available in the default report. We will use Explorations, as it allows us to further customize the report.

We’ll create a Landing page report using these metrics:

• Entrances: The number of times the first event in a session occurred on a page or screen. Most times that event will be a page_view event. So, Entrances will show you how many times users entered your website through these pages.
• Engaged sessions
• Engagement rate: The percentage of sessions that were engaged. In GA4, an engaged session is a session that lasts longer than 10 seconds, has a conversion event, or has at least 2 pageviews or screenviews. These show that something happened on that page, that the user took some kind of action and didn’t just leave once they arrived there. So, there was some kind of interest.
• Average engagement time per session
• Conversions
• Total revenue

Other metrics you should use are:

Views

Sessions

Total users

New users

Returning users

In the navigation panel, go to Reports then Blank.

Starting a blank Exploration

Let’s add first the Landing page dimension.

In the Dimensions variables, click the + sign.

Adding a dimension in Explorations.

Type Landing page in the search bar. Select the metric and click Import.

Importing a dimension in explorations

The process is the same for adding Metrics. Add all the metrics mentioned above.

Double click on the Dimension and Metrics to build your table in the visualization.

Building the landing page report in the visualization area

You should end up with the following:

Landing page report in Explorations

Efficiency Hacks

Comparisons

Comparisons enable you to get quick insights by comparing subsets of your data. This involves comparing different segments to identify the root causes of issues or what is causing the best results. With GA4, you can look at different dimensions side by side and conduct "this vs that" comparisons.

Let’s use our example of the Canadian audience with residents from Mississauga. This subset of our data can be compared with all our users to make a determination.

To create a Comparisons, go to any of the GA4 Reports, click the Edit Comparisons pencil icon, and follow the example from the image below. Then click Apply.

Example of comparisons in GA4

Comparisons will remain as you navigate through your reports. But once you close them or your page, that’s it. You’ll have to recreate them all once you go back to GA4. This is why I often use Explorations if there are subsets that are used frequently.

Explorations

Contrary to Comparisons, **Explorations **can be saved for future uses. We’ve covered how to build one in the Practical Examples For Content Analysis section of this post.

Automate With Looker Studio

While the primary focus is content analysis in GA4, the process of analyzing content can be arduous due to the repetitive steps involved.

To alleviate this issue, you can develop a dashboard that can automatically address your most crucial queries. Hence, let’s mention Looker Studio.

Furthermore, I'd like to share this Content Analysis Dashboard that includes almost all the essential elements required for gauging content performance.

Despite varying content objectives, this dashboard is an attempt to consolidate them into a single platform. Additionally, an accompanying guide is available to assist you in comprehending how to use the dashboard, which will undoubtedly shed light on the most precise methods of analyzing content and discovering insights.

Can Google Analytics Help With Content Ideas

As we've demonstrated in the beginning, Content grouping can reveal what performs well, giving you insights into what content you should create more of.

In the event that the "Other" group makes up a significant or increasing percentage of your content, it may be worthwhile to investigate further and identify patterns in the content that users engage with or convert from, and then produce more of that type of content.

By analyzing Page title and screen class dimension, you can discover pages that unexpectedly attract a lot of attention, which can inspire new content ideas.

It's also essential to deconstruct successful articles to understand what makes them engaging. You can start by analyzing their headlines, which may use a catchy title or effective copywriting formula that you can apply to future posts.

Additionally, examining articles with above-average engagement times can reveal valuable insights into their structure. For example, you may find that your most engaging articles include videos, suggesting that adding video content to your future articles can increase user engagement.

Finally, leveraging the site search feature in GA4 can help you understand the intent of users and generate fresh content ideas based on their search queries. Setting up site search is straightforward, and you can quickly learn here to learn how to track site search in GA4.

Summary

Together we have discovered that regardless of a site's specific goals, a systematic content analysis using GA4 is achievable.

We saw how to get the most out of the different GA4 reports by following an analysis process to effectively identify high-performing content, uncover conversion opportunities, and generate new content ideas.