In the rush to digitise and deliver learning at scale, many organisations have focused on metrics that are easy to track like course completions, attendance rates or time spent on a module. While these metrics serve a purpose, they offer only a surface-level view of effectiveness. If organisations are serious about learning as a lever for transformation, then the question shouldn’t just be “Did they complete it?” but it should be “Did it make a difference?”

That’s where learning analytics comes into play. Not as a report card of participation, but as a tool to measure behavioural change, performance uplift and the true return on learning investment. If learning doesn’t lead to different decisions or better outcomes, then it hasn’t really landed.

Why Completion Rates Aren’t Enough

Completion rates might tell you who clicked through a course. But they don’t reveal if the content was relevant, retained or applied in practice. In fact, an over-reliance on completions can lead to a false sense of success, especially in compliance-heavy industries, where the goal becomes finishing the training rather than learning from it.

Imagine rolling out a cybersecurity awareness module to thousands of staff. A 100% completion rate looks impressive on paper. But if phishing response times don’t improve or incident reports increase, the training hasn’t fulfilled its purpose. What matters more is the change in behaviour and confidence, not the box being ticked.

What Learning Analytics Should Really Be Measuring

To move beyond vanity metrics, organisations need to design learning with measurement in mind. Learning analytics when applied strategically can track:

• Behaviour change – Are learners applying the knowledge in real-life scenarios? Has there been a shift in decision-making, collaboration or adherence to processes?
• Performance impact – Are there measurable improvements in KPIs, such as productivity, error rates or quality of service, tied to the training?
• Engagement quality – Not just who completed the course, but how they engaged. Did they revisit content? Were assessments challenging enough to indicate understanding?
• Confidence levels – Do learners feel more capable after completing the program? Has their self-assessed proficiency improved?
• Manager feedback – Are direct supervisors observing change on the ground? Do they see a difference in how their teams operate?

The goal is to generate insight that feeds continuous improvement both in how learning is designed and in how it supports broader organisational goals.

Designing for Measurable Outcomes

This shift requires a mindset change, from delivering learning as an event to viewing it as part of a continuous performance system. Our approach begins with defining the end state: What specific business or behavioural outcomes should result from this learning experience?

Once those are identified, the learning design is shaped around them and so is the data strategy. This might mean incorporating pre- and post-training assessments, linking training completion to performance metrics or building in check-ins 30 or 60 days after training to evaluate uptake.

By way of an example: an organisation implemented a targeted learning intervention for team leaders struggling with operational reporting. Instead of relying on test scores, the team tracked whether the quality and timeliness of reports improved over the next two quarters. It wasn’t the fastest feedback loop but it was far more meaningful.

Combining Data with Dialogue

Data alone doesn’t tell the whole story. We are a strong advocate of combining analytics with qualitative insight. That means interviewing stakeholders, running focus groups and collecting sentiment data alongside quantitative indicators.

Analytics can highlight that something isn’t working; conversations can help explain why.

This blended approach is especially valuable in change-heavy environments, where learning uptake can be impacted by competing demands, fatigue or lack of clarity. If learning analytics are showing low engagement, the response shouldn’t just be to redesign the module, it might require revisiting change readiness or manager involvement.

The Role of Managers in Making Learning Stick

Another underutilised source of learning impact data? Frontline managers, who are the link between learning and performance. They are well-positioned to observe whether new behaviours are emerging, whether employees are trying (and failing) to apply what they’ve learned or whether additional support is needed.

Incorporating simple feedback mechanisms for managers such as post-training check-ins, team discussions or short observation templates can dramatically improve the quality of learning analytics. Not only does this create richer data, it reinforces the idea that learning isn’t just HR’s job, it’s everyone’s responsibility.

Building a Culture of Meaningful Measurement

For learning analytics to be effective, they can’t be an afterthought. Organisations need to build the habit of asking the right questions up front:

• What does success look like beyond completion?
• How will we know if this learning made a difference?
• Who needs to be involved in measuring and reinforcing this learning?

This doesn’t require an enterprise LMS overhaul. It starts with a shift in intent, from tracking outputs to understanding outcomes.

In the end, the most powerful learning metric isn’t how many people finished a course. It’s how many people changed what they do because of it.

Want to learn more? It all starts with a conversation. Speak to us here.