Guide to Content Marketing Analytics

Content marketing analytics is measuring and analyzing content marketing efforts. The metrics are shown in an online dashboard. Content marketers can use this data as a feedback loop to enable them to measure progress, find areas of improvement and link back to business results. 

Before proceeding further, if you do not know enough about content marketing metrics, I recommend the following articles.

• What are some content marketing metrics to follow?
• Why type of content marketing metrics does Nudge provide?
• Why is bounce rate important for content marketing?
• What are the best free content marketing measurement tools?
• What are the best tools that work with agencies?
• What are some good content marketing KPIs?
• What are content marketing metrics?
• How do you measure the success of content marketing?
• What is a better tool than Google Analytics for content marketing?
• How do I make an effective content marketing report? 
• What considerations do you need to make when selecting content marketing analytics?
• Factors which Drive Content ROI
• Content Marketing ROI
• 10 Benefits of Content Marketing
• Guide to Content Marketing

Table of contents:

• A preview of what a content analytics dashboard looks like
• How do you use analytics to measure the success of content marketing?
• What are the best practices for content marketing analytics?
• What are the different types of content analytics?
• What are the common Content Marketing Metrics? 
• What considerations do you need to make when selecting content marketing analytics?
• Site Analytics vs Content Analytics
• What role does analytics play in content strategy?
• What are consumption metrics?
• What role does content analytics play with SEO?
• 3 ways to increase sales with content marketing analytics
  
• 22 ways to use your content marketing analytics
• What is the future of Content Marketing Analytics?

A preview of what a content marketing analytics dashboard looks like

Many use Google Analytics for their content marketing then pair it with other software solutions to obtain a higher fidelity view. With Universal Analytics retiring this year and the shift to GA4, many are seeking alternative solutions.

In fact, 49% of marketers expect to be using 2 or more solutions this year. 49% of respondents rely on multiple analytics

Common software tools for content analytics include:

• Nudge
• Chartbeat
• Parsely

They might be paired with software like:

• Google Search Console
• Hubspot or Optimizely
• Intercom or Segment
• Clarity, Hotjar or CrazyEgg

Read more in our Guide to Website Analytics.

Why the need for specialist software? 

The need for content marketing analytics grew as a byproduct of the high adoption of content marketing. Older web analytics systems like Google Analytics and Adobe were not fit for purpose. And content marketing bridges more of art and science; how good the content is but also how well it engages customers. This created the need for specialized solutions. This also has meant that many companies use 2 or more analytics platforms to get the data they need. 

Content marketing analytics enables marketers to achieve a number of things at once:

• Better understand the performance of their content.
• See how customers engage with their content or see how it is consumed.
• Link content marketing to strategic goals.
• Link the success of content to other business goals, like MQLs, leads, sales.
• Set key performance indicators (KPIs) for the team
• Accountability.
• Understand ROI.
• Help with setting budgets and securing new budget.
• Find areas of improvement.

Without content marketing analytics software marketers would be operating in the dark. They wouldn’t understand how effective their content is resonating with customers. And would struggle to prioritize where to focus. Often analytics is an area they turn to when they are struggling with performance. Analytics can help uncover areas of opportunity. 

Each year one of the world’s leading analytics agencies, Cardinal Path releases a report on analytics used by brands. They skew towards big brands, and at the moment focus on site analytics and less so on content marketing analytics. But it’s a good starting point, this is where most of us reading this post have/are starting.

You probably have site analytics, such as Adobe Analytics, or Google Analytics- but now you’re in search of something more specific, to help with content.

We have found that these traditional page analytics tools are great for your website – but, when content is a key part of your business, they have clear gaps.

Related reading:

• Content Marketing Metrics

Sign up to Nudge, 70% of users get set up within 10 minutes. Access content metrics like scroll, drop off, engagement thresholds & engaged bounce. Nudge balances the art & science of content, so both creators & the measurement teams are happy. And helps them find ways to drive performance. 

How do you use analytics to measure the success of content marketing?

Analytics helps to quantity how many customers engaged with your content and what happened next. This then informs a marketer how successful the content has been. 

Similarly, analytics helps to inform content strategy. To see how effective it is in delivering results.

What are the best practices for content marketing analytics?

If we were to distill it, it would look like this:

• The best tool is the one you use.
• Use tools built for purpose.
• Does the company itself dog food? I.e. use the tool themselves.

That should get you ahead of the pack.

Different types of content analytics

There are many different types of content analytics tools. The primary use case, is measurement and analysis of the content. With some more specialized tools for other workflows. 

1. External tools, e.g. Nudge, to get meaning across many different types of content (built for content marketing, branded content, and content distribution).
2. Built-in tools, platform-specific analytics provided by the social platform itself, to help analyze social posts (e.g. Facebook Insights).
3. Creative analysis, i.e. heat maps, or similar. Like RealEyes.

Other examples within the wide spectrum of content analytics tools include data/text mining, pattern matching, visualization, semantic analysis, neural networks, and complex event processing. 

These are mainly quantitative tools, you can also get more qualitative analysis of your content through surveys & panels.

What are the common Content Marketing Metrics? 

The most common metrics marketers are adopting for content marketing are:

• People, how many real people are consuming your content.
• Attention, seeing how much attention people pay to your content.
• Bounce, whether people are engaging with your content or not.
• Avg Scroll, the average scroll on each piece of content.
• Social engagement, seeing how many shares your content creates.
• Conversion rate, the rate at which people take action after consuming your content.
• Time of day, seeing what time of day is best for your audience to engage.

Read more:

• Content Marketing Metrics

What considerations do you need to make when selecting content marketing analytics?

Six consideration you should make when selecting a platform:

1. The best tool is the one you use
2. Third-party measurement is vital
3. Benchmarks
4. Time to insight
5. Distribution impacts success too, so make sure it is measured effectively
6. A good tool will help improve your old content too

You can read a full rundown of these considerations. 

Site Analytics vs Content Analytics

Web analytics help understand how a website has performed. Content marketing analytics go a bit deeper to find out how engaging it has been. This higher fidelity is needed to ensure the content is effective. Content lives on websites, The two are intertwined, you can’t have one without the other (in most use cases). And this is very common; marketers today use multiple tools to deliver on their goals.

The key thing is that content analytics has metrics which are fit for content. 

What role does analytics play in content strategy?

Analytics helps to inform content strategy. To see how effective it is in delivering results.

What are consumption metrics?

Consumption metrics are helpful for content marketing, as for content to be effective, it needs to be consumed. That’s where consumption metrics like Attention, Scroll, Bounce & Earned Impressions can help. They show where and how it is being consumed.

What role does content analytics play with SEO?

For search engine optimization, Google rewards those that provide a relevant experience with increased ranking. Which is where content analytics can help play a role, to identify the most engaging content, but to also identify content that isn’t getting that much engagement. Therefore you can go and improve that content, to increase the ranking.

3 ways to increase sales with content marketing analytics

1. Use consumption data to identify the best location for calls to action

Using consumption data like ‘scroll’ you can see where people are scrolling to on your most popular pages. Then strategically place your CTAs before the average person drops off.

Optimizing your calls to action, can then lead to more sales, without changing anything.

2. Learn from your top performing content to create a blueprint

Use your analytics to identify your top converting content. Then use that as a blueprint to go and improve your low performing content.

This helps to lift your average performance and in turn more sales.

3. Optimize your distribution

Use your analytics to identify your most effective content distribution sources. And adjust budget to them.

To do that, make sure you track your content distribution and benchmark against the attention for each click. With the view, more attention means a higher conversion rate. So optimizing your content distribution to the highest sources, can then lead to more sales.

Tools like Nudge, also help make sure you are promoting the right piece of content. To drive maximum ROI, it’s best distribution to best content.

High performing content marketers spend more on analytics

In this study, it was found that the top 25% of companies spend more on analytics. And that’s because, it helps them find areas of performance.

Analytics as a business process is an invaluable feedback loop. And exactly what content marketing teams should adopt, a continual feedback loop, to find areas of performance.

The sports analogy is that, athletes don’t play a whole season and look at the data at the end of the game. Analysts look at performance throughout the whole game and do a de-brief. They even analyze the competition, to help provide input into the games playbook. This arms the athletes with the right information to perform at their best.

And this is exactly what content marketing teams can miss out, by not making analytics a key business process. Done right, arming them with the right data, can supercharge performance.

A common problem is content teams being haphazard with their content data.

Imagine taking a run – and tracking it with your tracker. But every time it gave you different metrics. Oh this week you ran 3 miles, last week you ran 4 km. Or even better, what if you used a different APP every time to track your runs. Nike Run Club this week, Strava last – and built in Apple the week before.

There would be no way to progress. Content has grown iteratively in our marketing mixes and investment. It’s worth revisiting, is our feedback loop the right one. Or do we need to reset.

22 ways to use your content marketing analytics

This is sourced from some of the ways we see people using content analytics. The top pro-tip is to pin the dashboard in your browser, as a tab, and visit each day to see how performance is going today.

1. To identify urls that need improvement, or see how your content is performing.
2. To get insights onto how customers are engaging
3. To prove the ROI of efforts
4. To predict the success of future projects, using older data
5. To build benchmarks
6. To help articulate to others how well your work is doing
7. To justify current budget, or to help acquire more budget
8. To upskill juniors on the team
9. To optimize content distribution or PPC efforts.
10. To optimize social media clicks.
11. To set KPIS, or keep track of KPIs.
12. To see how work creates business results (conversions/leads/purchases).
13. To quantify the media value of a campaign
14. For internal competitions
15. To see how documentation performs
16. To identify products which are resonating
17. See how employees are engaging with intranets
18. To measure branded content with partners
19. To see how campaigns are performing
20. To get transparency with influencers
21. To validate and test content strategies or tactics
22. To power internal scorecards

What is the future of Content Marketing Analytics?

Some of the future trends in content marketing analytics:

• Diversity in specialized tools. It is common to use 1-2 tools. As organizations become more digitally-centric, this will only increase, as business demands do. At some point, it will consolidate but not in the next 2-3 year horizon.
• Privacy-centric or privacy respectful tools. Marketers are finding that this isn’t a trade-off that is productive for either the brand or the consumer.
• Better content workflows. In recent history, marketers have aimed to get data into dashboards, the next trend will be towards, getting data into action, through integration into marketing workflows.
• Increased maintenance. With more digital tools, every organization will need to increase its technical maintenance to keep all their data up to date and reflective of the organization’s needs.
• Tighter feedback loops with paid distribution of traffic. I.e. Google Adwords has begun to integrate more data from Google Analytics.
• Digital content expanding into connected TV and digital out of home. Our content will now branch out from the regular screens. Requiring new ways of understanding content success.
• The merging of brand and performance. All brand advertising and marketing should perform. All performance should grow the brand. They still have different roles but will be more integrated than in the past.
• Machine learning democratizes insights, data collection and the feedback loop to creating more effective content. Now small businesses can really compete with big businesses.
• The shift from data scientists to insight curators, people that understand the data and how it is created but can then link it to business objectives.
• The intersection of content, personalization, and distribution will begin to blur. As they all get further integrated.
• Adoption of AI, in the workflows of content creation and making suggestions from data.

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