3 Content Marketing Analytics Frameworks for SaaS

Published on

May 14, 2023

3 Content Marketing Analytics Frameworks for SaaS

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Reporting on content marketing is notoriously difficult.

Content marketing is all about brand and product awareness, but most companies (understandably) want to achieve positive ROI - or at least measure the contribution.

Every other tool out there promises to fix this issue, but they seldom (if ever) deliver in a real-world setting.

The reason is that a tool is only as good as what you use it for.

The process you follow is more important than the tool you use. Instead of looking at specific Content Marketing analytics tools here, I want to focus on the process.

I want to look at content marketing reporting from the trenches by providing 3 different frameworks that correspond to 3 different levels of commitment to content marketing measurement:

  • Getting started with Content Marketing Analytics
  • Ramping Up Content Marketing Analytics
  • Becoming a Content Marketing Analytics superstar.

📃 If you want to dig even deeper into the topic of content marketing attribution, follow this link

Aside from the obvious Google Search Console/Google Analytics, I won’t be giving any specific tool recommendations.

📈 This guide has been updated for Google Analytics 4. You won’t find any Universal Analytics information as it is obsolete as of July 1st, 2023.

What Questions Should Content Marketing Analytics Answer?

Before diving into the frameworks, let’s take a step back.

The first step of any good measurement program is to know which questions you’d like to answer.

This is an important step because it will define:

  • KPIs to track
  • Measurement Frequency
  • Trends to follow
  • Etc.

So, what questions do we generally want content marketing analytics to answer for us?

It will differ for every brand depending on the level of investment, but some questions are universal: 

  • Is our content marketing program effective? (i.e. do we reach our goals? If you don’t have goals, go back to defining goals)
  • Is our content driving conversions?
  • Is our content marketing program impacting our top/bottom line?
  • Can we notice patterns in the way people interact with our content?
  • Is our content getting consumed/shared?
  • Can data inform which pieces of content to produce next to reach our goals?
  • What areas of content marketing (existing content, new content, video, social) should we focus our limited resources on?

I can list much more, but those are already a good start.

Start by listing out your specific questions. And whenever you find yourself diving head first in data, always take a step back - don’t go in without a specific question to answer.

Let’s talk about goals

One of the problems with the modern world is that we’re swamped in data.

New tools promise access to new data all the time, but the main problem that we face is that we’re overwhelmed with data.

In order to know what to track, we need to understand what our goals are.

Different companies have different goals when it comes to content marketing. Your goals may include increasing net-new traffic, reclaiming lost rankings, launching a program on a new social platform, etc.

Alignment needs to exist within your team to reach these goals - so communicate beforehand on your goals, and how they should be tracked.

You can use the SMART goals method to structure the goal-defining process (nothing new here, but it’s a good starting point)

In content marketing, these goals might look something like:

  • We want to double organic traffic by the end of this year
  • We want 50% of our leads to come from organic before the end of next year
  • We want our LinkedIn company page to reach 10,000 followers before the end of Q3
  • We want our sales team to have content that answers the main objections from our prospects by the end of Q1 (remember, sales enablement content is content too!)

Quantitative vs Qualitative Metrics in Content Marketing

As you probably know from your past data analytics endeavor, one can differentiate between qualitative and quantitative metrics.

  • Quantitative: Objective numerical metrics used to determine content marketing performance
  • Qualitative: More subjective analysis based on data/feedback that you receive.

Quantitative content marketing analytics

Quantitative metrics are objective, and cannot be interpreted.

Think about it this way: organic sessions can’t be debated. No one can argue that your website didn’t receive more traffic when they go up, or less when they go down.

It’s the same for other objective metrics like clicks, impressions, demos booked, etc.

There’s no need for subjective interpretation to understand what’s going on.

Qualitative content marketing analytics

Qualitative metrics, on the other hand, require interpretation.

Examples include time-on-page, bounce rate, heatmaps, user recordings, comments on social media, etc.

For example, a glossary page with high bounce rate isn’t really problematic because people come in for a definition, and bounce back.

It’s ok for your glossary to have a “high” bounce rate, because it’s designed to capture low intent, Top-Of-The-Funnel (TOFU) traffic.

On the other hand, if your 10,000 words long “Ultimate Guide To Customer Success” blog article has an average time-on-page of 30 seconds, you may have an issue.

These qualitative metrics need to be subjectively analyzed - you need to use your knowledge and expertise to determine whether there is an issue, what the cause may be, and how to fix the said issue.

Although I’m not going to dwell on qualitative vs. quantitative, it’s a good distinction to keep in mind when analyzing content marketing results.

3 Actionable Content Marketing Analytics Frameworks for SaaS Companies

Again, I could write a 5,000 long tool review, but the internet does not need it.

Plus, I don’t really want to write about tools I know nothing about like all these other people do.

So I thought I’d approach the topic a little differently and give you 3 frameworks of increasing complexity to measure content marketing performance.

This isn’t about giving away specific plug-and-play dashboards, but rather guiding you to understand what you can measure, how you can do it, and how to use that knowledge to answer your questions.

I’ve chosen to divide metrics in 3 parts: 

  • Traffic metrics: determining how many people see your content
  • Engagement metrics: determining how people interact with your content (reading, sharing)
  • Conversion metrics: determining how your content influences conversions

Framework 1: Getting Started With Content Marketing Analytics

Who this is for: This framework is going to be for people just getting started with Content Marketing Analytics. All content marketers should have this basic knowledge, and all companies producing content should have some form of measurement of these metrics in place.

What it is about: The idea for now is to track essential content performance metrics. We want to dip our toes in the world of content analytics and we’ll get started with the basics.

Basic Traffic Metrics

Let’s start by looking at basic traffic metrics.

This set of metrics is designed to provide a bird’s eye view of content performance. You should be looking at them often as they’re really easy to get, and super easy to understand too.

Traffic Metrics from Google Search Console (GSC)

In Google Search Console (GSC), we’ll look at clicks, impressions and average ranking.

  • Clicks: number of times a page is clicked in the search results
  • Impressions: number of times a page is shown in the search results
  • Average Ranking: average position a page is ranking in for all the queries it is ranking for

These metrics will give you a decent idea of your search performance without being too hard to look at.

Just log in Google Search Console, and watch your data.

💡 Average ranking can be a very counter-intuitive metric. Average ranking includes all the keywords your site ranks for, but we don’t know how that average is weighted, or if it even is. If you start ranking for more and more keywords, it’ll be lower in the SERPs, and your average ranking will worsen, even though that’s a good thing!

Traffic Metrics from Google Analytics

After GSC, let’s look at data from Google Analytics 4 (GA4).

To get basic traffic data from GA4, go to Reports - Engagement - Landing Page

Traffic Metrics from Google Analytics

Here we’ll be looking at 2 metrics:

  • Sessions: the number of times a page has been visited
  • Users: the number of unique people visiting a specific page

Sessions and users differ in the sense that users only count unique visitors. If someone comes back multiple times, they will trigger several sessions.

Since we’re measuring content marketing performance, we also want to look at organic sessions (i.e. sessions that come from search engines).

This can be done by applying a filter to your data. Select Filter, then “Session Medium = Organic”.

Filters in google analytics for content marketing analytics

This will let you know how many people came to your site or a specific page from search.

You can do the same with other channels, such as social for example.

💡 Organic Sessions and Clicks can differ slightly. Clicks are measured by Google on the Results Pages, whereas Organic Sessions are measured on your site. The ballpark should be the same, but exact numbers can differ, as GSC data is not really accurate.

Basic Engagement Metrics

Engagement metrics are all about how people interact with your site. They measure if your content is getting read, shared, etc.

At this stage, we’ll only be looking at Average Engagement Time (formerly time-on-page) and Bounce Rate.

  • Average Engagement Time: this metric tells you how long people have seen or read your content, page by page.

This is useful to understand if your content is getting consumed. Be careful to keep in mind which content formats you’re looking at.

As I mentioned before, some content formats (e.g. definitions or glossaries) will have a lower average engagement time, and that’s ok.

  • Bounce Rate: this metric tells us how many people left the site without interacting with it.

This is useful to look at to understand if people actually consume your content, or if they land on it but consider it not valuable enough.

💡 Google has changed the calculation for Bounce Rate between UA and GA4 (see here). Bounce Rate in GA4 is no longer linked to interactions, which makes it a little more valuable than it used to be.

Basic Conversion Tracking

The last set of metrics is about understanding if your content contributes to business outcomes.

To do this, we’ll look at conversions (very easy to set up in GA4).

Keep in mind that conversions should have monetary value. Scrolling past a certain point in the page or clicking a video should not register as a conversion, but rather as an event.

I can’t go into detail too much in this article, but I may address the topic in the future.

Examples of conversions to include at this stage: 

  • Demo bookings
  • Contact forms filled
  • Newsletter subscriptions

Again, all conversions should have monetary value. You should be able to determine a specific cost for each demo, newsletter subscriber, etc. 

You should know how much it costs, and how much it brings in. If you cannot calculate this, it shouldn’t be a conversion.

Conversion data can be accessed via the Conversion report in GA4: 

Conversion tracking in content marketing analytics

That’s it for our first framework. 

It’s very simple and aimed at measuring basic content performance, engagement and conversions, without going into details too much.

Now let’s dive deeper.

Framework 2: Ramping Up Content Marketing Analytics Complexity

With this framework, we’ll start looking at more complex data.

We can start diving deeper and breaking down data by device or demographic (whatever you need at this point).

We’ll also start looking at advanced conversion data to understand not only how your content performs, but how it influences other channels as well.

Advanced Traffic Metrics

In the first framework, we were looking at raw data without going too deep.

Time to dig.

Advanced Google Search Console Traffic Metrics

In GSC, we’ll always be looking at the same metrics, as the tool only gives us Clicks, Impressions, CTR and Average Ranking to work with.

However, we can start breaking down all of this by: 

  • Country
  • Device
  • Search 
  • Regex
  • URL

We do this by using a combination of tabs and filters.

Let’s start with the tabs in the middle of the screen: 

Advanced Google Search Console Traffic Metrics
  • Queries: The keywords you’re ranking for. If multiples pages rank for the same keyword, that data gets aggregated
  • Pages: A list of the URLs on your site and their metrics, independent of which keywords they rank for
  • Countries: Where traffic comes from
  • Devices: Mobile vs Tablet vs Desktop traffic
  • Search Appearance: How many of your impressions come from FAQs, Videos, Translations, etc.
  • Dates: The breakdown of the metrics by date

Now let’s look at the filters at the top: 

Advanced Google Search Console Traffic Metrics: filters

They are the same, but can be stacked on top of one another.

When you click on “New”, you get this screen with several options: 

Advanced Google Search Console Traffic Metrics: query
  • Containing
  • Not Containing
  • Exact
  • RegEx

The Regex one is best left for the last framework, as it has a steeper learning curve.

Now, let’s say you want to know how your blog performs in search, independent of the rest of the site.

You can filter URL by /blog/.

You could also filter out your brand name from the data, etc.

This gives you access to more granular, and thus more precise data to work with.

These GSC features allow you to understand how a specific page has performed following a content refresh, or whether multiple pages rank for the same keyword.

💡 It is useful at this stage to mention that clicking on a specific keyword, URL, country or device in the table automatically applies the filter. If you click on a URL, GSC restricts the data to that specific URL - these are 2 different ways of accessing the same dataset.

Advanced Google Analytics Traffic Metrics

When it comes to GA4 for our second framework, we’ll start looking at acquisition metrics - answering the “where does my traffic come from” question.

Click on “Reports” then “User Acquisition” under “Life cycle”: 

Advanced Google Analytics Traffic Metrics

This screen gives you the detail of your traffic.

This is pretty self-explanatory, and is mixed with the metrics from the first framework by default: 

  • Users
  • Sessions
  • Engagement Rate

This data can tell you what kind of traffic mix you’re getting, what’s working and what’s not, overall.

It can be combined with filters to access a specific page, subset of pages, demographic, etc.

This works almost the same as in GSC. I won’t dig too much in here because my point isn’t to tell you how to do things - but what to track.

If you want to learn how to use GA4 more in-depth, you can look at this course from CXL.

Advanced Engagement Metrics

Advanced Engagement Analytics has to do with event management.

In Google Analytics, an “event” is something that is registered by GA following a specific condition.

That condition can be an interaction from a user, or the result of engagement (e.g. staying on a page for xx seconds).

By default, GA4 gives you a small set of events to work with: 

Advanced Engagement Metrics

It is then very easy to add events in Google Analytics 4 to suit your needs.

This will help you track how users interact with your site.

Here are some things you can track with events:

  • Video Watched
  • Button clicked
  • Time spent on content
  • eBook download
  • Newsletter subscription
💡 It’s very easy to turn an event into a conversion in GA4. My recommendation is to keep the number of events manageable (i.e. don’t track stuff for the sake of it - only what you’re going to be looking at), but there’s no reason to refrain from adding more events.

I can’t really tell you exactly which events to add or how to manage them, as it will depend on what you’re trying to accomplish.

However, tracking engagement that’s specific to your content, such as button clicks on a blog CTA, clicks on a TLN CTA or eBook downloads, is going to help you prove content marketing effectiveness.

Keep in mind that events data can also be filtered by medium, source, URL, etc.

Advanced Conversion Tracking

Similarly, you can start looking at conversion data by filtering it by: 

  • URL (specific page, subfolder, subset of pages)
  • Medium
  • Country/Demographic

I don’t want to spend too much time on this here since I’ve written a complete guide on Content Marketing Attribution that you can read here.

Following the same logic as before, we’re trying to get past the basic metric to start exploring how the data is connected.

Ask questions like: 

  • Do blogs on [topic] perform well?
  • How does [ebook 1] perform compared to [ebook 2]
  • Does organic traffic convert better than PPC?
  • Etc.

Again, the data you need to look at depends on the answer you’re trying to get.

Always start with the answer you’re trying to get to gauge the data you need - not the other way around.

Framework 3: The Content Marketing Analytics Superstar

For our last framework, we’re not going to break it down by type of metric. Instead, I’m going to explain 2 concepts that are going to take your Content Marketing Analytics to the next level: 

  1. Regular Expressions
  2. Content Grouping

Regular Expressions in Content Marketing Analytics

Regular Expressions are a very, very, very (yes, 3 times) powerful tool.

In fact, it’s one of the most powerful tools we have at our disposal.

If you don’t know what a Regular Expressions (RegEx) is, here’s the Wikipedia article.

Didn’t get anything? That’s ok, me neither. Let me explain.

A regular expression is a filtering instruction we can use to return data for complex patterns.

Let’s take an example. Imagine your brand name is Kalamazoo, and you want to filter your brand name out of your GSC data.

If you go in and exclude “kalamazoo” from the data, you may notice some people actually misspelled the name, like “kalamazo” or “kalamazoo”.

Instead of exporting and manually going through the data, you can use a RegEx.

Select “Custom (regex)”, then “Doesn’t match regex” as a GSC filter, and enter: 

ka.*zo

This will remove all the keywords that begin with “ka” and end with “zo”, no matter how many characters are in between, or what these characters are.

💡 Like any good lazy marketer, I quickly realized that learning RegEx was too hard, so I turned to tools like AutoRegex that can generate regex for you in a matter of seconds from a natural language description.

The above example is very simple, but you can complexify it as much as you want - that’s what RegEx is made for.

A RegEx Content Analytics Use Case

Let me quickly give you an example of how I use it in my day job.

We often have to merge content for clients. We take several pages (from 2 to sometimes 15), and put all the content into one, rewriting it in the process.

We even sometimes change the final URL to make it more user-friendly. And we need to report on these actions.

In order to properly report on a merge, I need to pull: 

  • Data from all the old URLs
  • Data from the new URL

All in one graph, to (hopefully) show an improvement.

If I only take data from the new URL, I won’t be able to compare it to the old data - which I have to aggregate.

The best way to do this is to use a RegEx with the pipe (|) character, used as “OR”.

The syntax is something like URL1|URL2|URL3|URL4, etc. Super easy to pull up data that would have required manual work otherwise.

(My team would also tell you that since I’m lazy, I’ve automated our spreadsheet to identify the merges for me, remove anything but the slug, and automatically concatenate that into an up-to-date RegEx that anyone can use, anytime to get a bird’s eye view of our merges’ performance, but shhh…)

That’s as far as I’m willing to go to explain RegEx (it could even be as far as I’m able to go, tbh). 

It’s a super useful tool. Go learn what it’s about, play with it a bit, and you’ll see that you can use it in a lot of situations.

Like, a lot.

Probably every day 🙂

Using Content Grouping To Boost Content Marketing Analytics

Since the introduction of GA4, Content Grouping has been easier to set up.

There’s no reason not to use it now if you’re serious about your content marketing efforts.

So what is content grouping?

Content Grouping is a way to set up Google Analytics to report on specific groups of pages that you have specified in advance.

Let’s say half of your content is about employee engagement, and the other half about cauliflower recipes (don’t do this at home!).

You can tell GA4 to “group” your content in these 2 buckets, and have access to a super easy to use filter.

If you go to “Pages & Screens” under “Engagement”, you can click on “Page title and screen class” and select “Content group” to visualize your content groupings:

Using Content Grouping To Boost Content Marketing Analytics

This can allow you to compare cluster performance, different types of content, etc.

And you know what? The easiest way to set it up is using RegEx.

Summing up our 3 frameworks

So we have 3 frameworks for 3 different levels of engagement.

Framework 1

  • Basic metrics
  • Raw data, not digging
  • Super accessible to get started

Framework 2

  • Advanced Metrics
  • Start using filters 
  • Start understanding how advanced filtering can be used
  • Be mindful of asking the right questions

Framework 3

  • Use RegEx to increase filtering complexity
  • Revolutionize your setup with Content Grouping

Those 3 different levels of commitment are not set in stone.

If you’re already familiar with RegEx, you may use it from the start - and that’s fine.

I wanted to give you an idea of how you can dig deeper and deeper.

There’s so much more to be said here. I could have touched on using heatmaps to analyze user behavior and inform content reworks, using GA4 data to calculate cost per lead and conversion value from content, or setting up custom Looker dashboards to aggregate all that data.

These are all things you can do, but this piece is already long enough.

But as I’ve mentioned before, there’s one thing that’s paramount: always make sure you know what questions you’re trying to answer.

Data is a tool, and should only be used as such.

If you don’t know the question you’re trying to answer, you’ll never know how to get the answer.

And that’s the fun part!

Vince Moreau

I'm the CEO & founder of ScaleCrush. You can often find me ranting way too much about BS marketing advice, fluffy and regurgitated content, and calling out gurus. I also happen to have my very own unoriginal thoughts about the stuff we're going through.

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