Content analytics are broken: What's the solution?

Brands like to be able to explain why content works, and why it doesn’t. To understand their successes and failures, digital marketers have an array of content analytics tools to choose from, each providing data-driven insight into the performance of the content they put out.

But modern content analytics platforms need to be able to continually connect published content to outcomes in increasingly complex digital ecosystems, generating actionable guidance that enables marketers to keep up with evolving consumer needs and trends. In this environment, analytics tools have to be nimble to support the interpretation of rapidly changing data, and engaging experiences efficiently and consistently.

That’s where things get challenging. Even as advances in technology push analytics capabilities further, studies suggest that over 60% of marketers are unhappy with their ability to measure their content’s performance, while only 53% even factor marketing analytics data into their decision-making. The impact of analytics tools is also questionable, with 56% of marketers reporting that they need technical assistance activating their data.

So, what’s holding content analytics back?

In this post, we’ll explore that question. We’ll take a look at the agentic content analytics landscape, discuss why so many organizations are struggling with their analytics tools and workflows, and explain why a mindset shift may be needed in order to unlock the true potential of content data.

What is content analytics?

Simply put, content analytics is the process of collecting, measuring, and analyzing how audiences engage with content across channels and touchpoints.

That analysis helps content teams develop insights, which marketers use to shape content strategy and create better, more personalized content experiences for customers.

Content analytics is structured around metrics which brands use to gauge content performance — answering questions like: How are customers finding our website? How are they engaging with it? What content is popular? What content turns people off? Where are the gaps in our content experience?

Anyone who’s rubbed shoulders with a digital marketer is probably aware of the key metrics, which include:

• Engagement: Scroll depth, social interactions, session time, etc.

• Conversion: Depends on your goal — call-to-action clicks, email sign-ups, survey completions, purchases, etc.

• Distribution: Where traffic is coming from — organic, paid, referrals, etc.

• Audience: Who is engaging with your content — demographic segmentation, journey types, etc.

Essentially, analytics is about enabling brands to understand, as objectively as possible, what’s working with content, what isn’t, and what could be improved.

But, if it’s that straightforward, why are so many brands unhappy with their content analytics solutions?

The problem with content analytics

The content analytics landscape isn’t standing still, and, in order to measure the performance of content accurately, brands now have to develop and navigate extensive toolchains, and deal with vast amounts of data.

That’s a big part of the issue. The sheer volume of analytic data available across an array of disconnected platforms makes it more difficult, and less efficient, for brands to identify, target, and interpret the data they need to connect content performance to business outcomes — and, in turn, generate meaningful insight to improve content experiences.

For example, a page-view statistic, siloed on a third-party dashboard, needs to be accessed, integrated into a brand’s wider analytics workflow, and compared with past performance before any useful insight can be communicated to the relevant stakeholders. That process often involves lots of manual copy-paste work which can be time-consuming and tedious, delay the activation of important data, and delay the generation of timely insight.

Keeping up with innovation

The disconnection becomes more problematic when you factor in the need for content analytics to keep pace with the era of agentic artificial intelligence (AI) — that is, the introduction of self-service AI tools that generate insight via user prompts and questions like “How many views did this page get?”, “Which audience segments is this product popular with?”, and so on.

Agentic AI tools represent a way to make content analytics faster and more accessible. The problem is, traditional analytics tools aren’t built for them, and so brands struggle to integrate them with their existing solutions. That friction locks brands out of the significant benefits they offer, which include more precise personalization, more effective experimentation, and, ultimately, more engaging content.

Essentially, conventional analytics solutions are struggling to bridge the gap between content performance and business outcomes, and falling behind emerging solutions that offer effective ways to overcome that challenge. And, as the gap grows, content teams have to scramble to add new tools to keep up with the demand for analytic data, which often adds to the collective burden and damages the cohesion and impact of the analytics process.

How to fix content analytics?

Good question.

In a world where hundreds of different kinds of data have become relevant (not to mention urgent), we need to focus on closing the gaps between the content we produce, the tools we use to measure it, and the conclusions we draw from that process.

Instead of hunting for siloed knowledge, we need to be able to access analytics data seamlessly. Instead of delayed reporting, we need real-time data collection and analysis. Instead of inflexible, contextless dashboards and fragmented toolchains, we need unified metrics at our fingertips, integrated into the digital experience optimization stack.

We also need to create an environment in which brands can take advantage of agentic AI by making it possible to access native AI tools within analytics workflows.

It’s time for a mindset shift — one that repositions content analytics as part of the content workflow, rather than something that happens outside of, or after, it.

In the Contentful Platform, interoperability isn’t an add-on, it’s a feature. Brands don’t have to juggle separate tools or copy and paste data between different environments. Instead, you’re free to integrate the apps and tools you need like modular building blocks, leveraging application programming interfaces (APIs) to ensure the free flow of data across your tech stack without risking incompatibility or disruption to services.

This approach allows you to position analytic tools in close proximity to the content in your publication workflow, and even integrate them natively with your headless content management system (CMS). That change can reduce or even eliminate the gaps between technology platforms, transform the accessibility of analytic data, and accelerate your response to insights. It also provides an environment in which AI agents can integrate seamlessly with other AI agents as part of a flexible, responsive, adaptable analytics solution.

In other words: No more siloed knowledge and data management friction. No more out-of-context, vanity metrics and delayed reporting. No more barriers to agentic innovation.

In a headless environment, built around the interoperability of tools and services, the insight you’re looking for only needs to be a click away.

What’s next?

That kind of transformation may sound daunting, especially if you’ve relied on the same fragmented, cumbersome approach to content analytics for years. But bringing analytics into your content workflow doesn’t have to be painful — you don’t need to overhaul your tech stack or find new tools every time the market shifts. You need a platform that can keep pace with the data landscape, and new content management tech, over the long term.

Contentful is designed to power this kind of change. Our digital experience platform provides a scalable, extensible environment in which brands can integrate third-party tools, including analytics apps, while tailoring workflows to the needs of the content team, and streamlining every step of content creation.

Our platform gives brands the freedom and the capability to rethink their analytics approach, leveraging the composability of an API-first tech stack to integrate dashboards and data seamlessly, generate real-time insight, and seize the opportunities of the agentic AI era.

Rethinking your approach to analytics is only the first step; Contentful can make your insight go even further with powerful, dedicated content management features, including A/B testing and personalization tools, and an array of AI automations.

Want to know more? Check out our latest features here, or get in touch with our sales team for a Contentful demo.

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