Understanding the Pillars of Content Measurement: An Introduction

Given the prevalence of online interaction between citizens and public services, the ability to measure and analyse digital content performance is essential. For government, local authorities, and public services committed to providing useful and accurate advice, understanding which content resonates and proves effective is paramount.

This is the first of five blog posts that deep dive into how organisations can solve this problem and make it easier to optimise content for improved performance with confidence that they are measuring the right things and drawing useful conclusions.

This series will delve into this critical topic, beginning with an overview of the foundational elements that constitute a robust content measurement data model. By refining our approach to content measurement, we can ensure that public and informational services are not only effective but also continuously improved based on solid data insights.

If you want to find out more about how Storm ID can help you audit, analyse or measure your digital service content, get in touch with our team!

The three pillars of content measurement

A comprehensive content measurement data model encompasses three primary pillars:

• The segmentation model
• An interactions and activations events model
• Content reach and impact

Each of these components plays an integral role in enabling analysts to gain deeper insights into content performance. By understanding how these pillars interact and support one another, we can develop a more holistic and accurate approach to measuring content effectiveness.

The segmentation model

At the core of any effective content measurement strategy lies the segmentation model. This model allows analysts to categorise and differentiate content based on various dimensions, such as readability, accessibility, audience, content patterns, and content themes. The objective is to understand how these segments impact performance metrics, thereby uncovering actionable insights.

Segmentation is crucial because it enables a more nuanced analysis of content performance. For example, understanding where the perceived reading age of content causes a drop-off in engagement or interaction helps to pitch complex or technical content at a level that maximises its usefulness while retaining its impact. By segmenting data, we can identify performance nuances and tailor content strategies appropriately.

Furthermore, carefully considered segmentation models allow analysts to aggregate data into useful categories such as content topic, organisational department and target audience, helping to understand performance at a grouped level.

To build a robust segmentation model, consider the following steps:

• Identify important segmentation dimensions
  Determine the most relevant dimensions for your content. These could include factors like topic, reading age, target audience, department, author, content type, and so on.
• Collect and aggregate data
  Gather data from various sources. This includes web analytics (GA4) data, but also consider joining other data sources such as web crawls, accessibility data, and others to complete your analysis. Ensure that this data is clean, accurate, and comprehensive.
• Analyse and interpret results
  Use segmentation to analyse content performance across those different dimensions. Look for patterns and trends that can inform your content strategy.
• Refine and optimise
  Continuously refine your segmentation model based on new data and insights. This iterative process will help you stay responsive to changing audience needs and preferences.

Interactions and activation events

The second pillar of content measurement revolves around events, i.e. what users do with your content. We can consider events in two ways – some events are simply interactions or engagement with the content, and others are more solid indicators of success. We call these simple interactions ‘engagement’ and the more solid indicators events are ‘activations’. These events encompass any actions taken by users that indicate engagement with content, such as clicks, shares, comments, and downloads. What counts as engagement and what counts as activation really depends on a combination of your goals and how the content is laid out, and therefore the definitions of interaction and activation may differ from service to service.

To effectively measure interactions and activation events, it is essential to track and analyse specific user behaviours. This involves setting up event tracking within your analytics platform to capture data on key actions. Consider the following steps:

• Define interaction and activation events
  Identify the most critical interactions that align with your content goals. These could include page views, button clicks, video plays, and form submissions, but also may include softer measures such as how far a user scrolled down the page, or how long they spent on the page.
• Implement event tracking
  Using your web analytics software (for example, Google Analytics 4), implement tracking for events. Ensure that tracking is set up correctly and that all relevant data is captured.
• Analyse event data
  Review the data collected from these events to gain insights into user behaviour. Look for patterns that indicate high engagement or areas where users may be dropping off. Review engagement against the framework of your content model for clues about what helps users to engage with your service.
• Optimise and iterate
  Use the insights gained from event data to optimise your content strategy. Adjust your content to foster better engagement from users, and experiment with different formats and approaches.

Content reach and impact

The final pillar of content measurement focuses on content reach and impact. This dimension assesses how successfully content is discovered by audiences. Key metrics in this area include organic search rankings, social media engagement, site search, and overall discoverability.

Understanding content reach and impact is vital for evaluating the effectiveness of content distribution strategies. By analysing how content is found, we can identify opportunities to enhance its visibility. Consider the following steps:

• Monitor organic search performance
  Use tools like Google Search Console to track how your content ranks in organic search results. Identify high-performing keywords and optimise your content to improve rankings and click-through rate.
• Analyse social media reach and engagement
  Review social media metrics to understand how your content is performing on platforms like Facebook, Twitter/X, LinkedIn, and Instagram. Look for trends in likes, shares, comments, and overall reach.
• Evaluate on-site search
  Use web analytics to monitor how users are finding content through your site's search functionality. Identify popular search terms and optimise your content to match user intent.
• Measure overall visibility
  Assess the overall visibility of your content across all traffic sources. This includes tracking metrics like page views to gauge how frequently the content is accessed.
• Create weighted scoring metrics
  Develop a way of scoring different reach and impact metrics so that when you sum these per content piece you can compare overall performance of one piece versus another, even where the places they performed well in discoverability may be different (e.g. social media v organic search).

Moving from faith to proof in content measurement

Traditional thinking about content engagement often relies on metrics that may not provide a complete or accurate picture. For example, bounce rate is commonly used to gauge engagement, but it can be misleading as it may indicate other factors, such as page load times or irrelevant content, or even simply that a user almost immediately gets satisfaction for their user need without having to further engage with the website. To truly measure content effectiveness, we need to shift from metrics based on faith to those grounded in proof.

This shift requires a more rigorous and data-driven approach to content measurement. By leveraging the three pillars outlined above, we can move beyond surface-level metrics and gain deeper insights into content performance.

Furthermore, we can use well-established data science techniques to review the data we collect and help establish which metrics are better indicators of success than others, which allows us to refine and optimise our content measurement. Doing so ensures we are no longer using blind faith in our choice of metrics, but data-driven proof of what is important.

Data science techniques can assist in learning about content measurement in several ways, for example:

• Categorisation
  Large language models can help apply categorisation across large content sets to help find new content model dimensions, for example, topic tagging.
• Predictive analytics
  Employing data science can help identify underlying trends, correlations, and cause and effect. This can help to predict what might happen when content is improved in different ways.
• Identification of anomalous content
  You can use different strategies to help identify content anomalies and try to understand why that content performs so much differently to expectations.
• Validation
  The uses of various data experiments can help validate and refute hypotheses you have about content performance, fostering better trust in the data you collect, and enabling optimisation.
• Dashboard automation
  Data science techniques can help improve the data quality available for data visualisation and enable automated data processing to provide a richer data narrative.

Digging into the detail – what this blog post series will cover

Over the following weeks, we will continue our series with another four blog posts that explore each of these points in more detail and present a framework for measuring non-transactional content with confidence.

Developing a content model

How to create a comprehensive content model that aligns with your business objectives and provides a clear framework for measurement.

Tracking suitable events and parameters

A detailed look at the key events and interactions that should be tracked to measure content engagement effectively, and how to select the right metrics for your scenario.

Content impact points

Proposing a technique to unify content reach metrics across channels to provide a useful summarised KPI.

Using data science to understand content performance

Leveraging data science techniques to gain deeper insights into content performance and make data-driven decisions, with a case study example.

By following this structured approach, we can make public and informational services much more effective and deliver significant efficiency gains. Ultimately, our goal is to direct continuous improvement where it is needed most, ensuring that our content strategies are both impactful and sustainable.

Look out for the next post in this series, where we will dive into developing a content model that aligns with your business goals and sets the stage for effective content measurement.

Discover how our UX, Content Strategy, and Data Analytics services can help – contact us today.