LMS Analytics: What to Track and How to Use Data to Improve Learning

The more data your learning platform generates, the harder it can be to know what it’s telling you. Completion rates, login frequency, assessment scores… The numbers are there, but connecting them to the outcomes leadership cares about is a different challenge entirely.

This guide introduces the LMS Analytics Ladder: a four-stage framework for understanding where your analytics maturity currently sits and what to focus on next to make a stronger case for learning.

What Are LMS Analytics?

LMS analytics refers to the collection, measurement and interpretation of data generated within a learning management system (LMS). It captures what’s happening inside your platform and helps you understand what that behavior means for learning across your organization.

It’s worth distinguishing LMS analytics from the broader term “learning analytics,” which covers data from any learning environment, including informal and on-the-job experiences. LMS analytics is specifically scoped to activity within your platform, which makes it more structured and easier to act on.

There are three layers of LMS data to understand:

LMS reporting tools generally cover the first layer, but without the other two, it’s difficult to see the real impact of learning programs. When you can measure across all three layers, connecting learning data to business outcomes becomes a much more realistic goal.

How and Why L&D Measurement Is Changing

According to the World Economic Forum’s Future of Jobs Report 2025, the share of workers who’ve completed training has grown from 41% in 2023 to 50% in 2025. Skill instability has slowed as a result, but 39% of existing skill sets are still expected to be transformed or become outdated by 2030.

Pressure to close the skills gap has become a boardroom concern. The LinkedIn 2025 Workplace Learning Report found that 49% of L&D professionals say their executives are concerned that employees don’t have the right skills to execute business strategy. Course completion rates and learner satisfaction scores don’t address that concern on their own, creating a need for a more structured approach to measuring results.

That’s where corporate learning analytics comes in. For L&D teams focused on aligning learning strategy with business goals, ROI measurement and performance analytics require a more rigorous data strategy and a clearer sense of where to start.

The Kirkpatrick Gap: Where Measurement Gets Harder

The Kirkpatrick Model is one of the most widely used frameworks for evaluating training programs. It measures learning across four levels: 

• Level 1 captures learner reactions
• Level 2 measures knowledge recall
• Level 3 tracks behavior change on the job 
• Level 4 connects training to business results

When it comes to putting this framework into practice, the challenge is that Levels 1 and 2 are much easier to measure. Completion rates, quiz scores and post-course surveys are straightforward to collect, but they only tell you what happened inside the learning experience and not what changed because of it. They can’t show whether training affected productivity, retention or any other outcome leadership tracks.

Reaching Levels 3 and 4 requires a different approach. It means tracking behavior change over time, partnering with business leaders to define success and building the data infrastructure to connect learning activity to operational outcomes. 

The LMS Analytics Ladder below provides a methodical way to understand LMS analytics maturity and prioritize next steps.

Introducing the LMS Analytics Ladder: A Four-Stage Framework

This LMS Analytics Ladder is built to help you understand which metrics are useful to track and why. The framework covers four stages, each with specific metrics and a clear next action. 

Crucially, each stage builds on the last. You can’t predict learner behavior without first diagnosing why learners are struggling, and you can’t optimize for business outcomes without predictive data to work from. Working in sequential stages gives you a clear picture of where you are and what to focus on next.

Stage One — Reporting

Stage One is the natural starting point, and there’s nothing wrong with being here. At this stage, you’re working with surface-level data, such as: 

• Who logged in
• Which courses were completed
• How much time employees spent in the platform

These are your course completion rates, login frequency and time-in-platform metrics.

On their own, they tell you who is and isn’t engaging with your training programs and not much more. Still, while you can’t draw conclusions about whether learning is working from reports alone, you can use this data to establish baselines.

Knowing your starting point is what makes every subsequent stage meaningful. Once you have a clear picture of engagement patterns across your organization, you can identify the lowest-engagement cohorts and start determining what’s getting in the way of progress.

Stage Two — Diagnosing

In Stage Two, you move beyond completion data and start understanding why learners are struggling. The metrics here are more granular: 

• Quiz scores
• Drop-off points
• Time on task
• Content revisit rates

Together, these paint a more useful picture. A high drop-off rate on a specific module might signal that the content is too dense or poorly sequenced. Low quiz scores across a cohort could point to a gap in prerequisite knowledge. High revisit rates on certain materials might mean learners are finding them genuinely useful — or that they’re confused and circling back.

The next action at this stage is to flag underperforming modules, run a content audit and identify at-risk learners early. The earlier you spot a pattern, the more options you have to address it. At this point, you’re building the diagnostic picture that Stage Three depends on.

Stage Three — Predicting

Stage Three is where your analytics shift from reactive to proactive. Rather than identifying problems after they’ve occurred, you’re using historical patterns to get ahead of them. The metrics at this stage include:

• Predictive risk scores
• Skill gap data 
• Cohort performance trends

This data helps you predict which learners are likely to disengage or underperform before it happens. Instead of waiting for a learner to fall behind, you can intervene early, personalize learning paths based on individual risk profiles and reallocate content resources to where they’re needed most.

This is also the stage where purpose-built tools start to make a real difference — specifically those that support predictive learning analytics and at-risk learner identification. These can give L&D teams the visibility they need to act before problems escalate.

Identify at-risk learners before they fall behind.

Discover how Brightspace Performance+ uses predictive analytics to help you intervene early.

Learn about Brightspace Performance+ >

Stage Four — Optimizing

At Stage Four, the focus shifts from what’s happening inside your LMS to what’s changing because of it. This is where learning data connects directly to business outcomes, captured by the following metrics:

• Performance improvement post-training
• Retention correlation
• Certification impact on productivity

According to McKinsey’s research on the future of the CLO, the most important question for CLOs is no longer how many people completed a course, but instead whether learning is helping the organization adapt, innovate and grow. Stage Four is where you start to answer it.

That means building reporting around the numbers leadership already tracks — productivity, retention and revenue impact — rather than metrics that only make sense inside the learning function. 

How to Use LMS Analytics to Improve Learning Outcomes

Knowing which stage you’re at is only useful if you act on it. The good news is that moving up the ladder doesn’t necessarily mean investing in new technology — it starts with being more intentional about the data you already have.

These four steps can help you get there:

• Start with an honest audit. Pull your existing reports and ask what they’re actually telling you and what they’re leaving unanswered. By mapping those gaps against the four stages, you’ll quickly see where the biggest opportunities are.
• Set one measurable outcome target. A narrower focus gives you a much cleaner story to tell leadership. Pick a single business KPI your next learning initiative will aim to influence.
• Build cross-functional partnerships. In Stages 3 and 4, you’ll need to work with people outside the learning function — finance, operations, HR — to agree on what success looks like before training begins.
• Shift from data collection to data storytelling. Analytics only become valuable when they tell a compelling story that’s relatable, accessible and actionable for the people who need to act on it.

Brightspace can help with the data side of this through its analytics dashboard and the AI-enabled insights available through D2L Lumi.

How Brightspace Supports Your Analytics Journey

Every organization’s analytics journey looks different, but your learning platform should be able to support you at every stage. Here’s how Brightspace can support you at each step of the ladder.

For organizations that need dedicated predictive analytics capabilities, Brightspace Performance+ adds a deeper layer of at-risk learner identification and pattern recognition. D2L’s Optimization Services are also worth exploring for teams that want hands-on support translating analytics data into actionable strategy.

Turn Your LMS Data Into a Business Case

The data you need to make a compelling case for learning probably already exists inside your LMS. Knowing what the data is telling you and how to translate it into language leadership responds to is the real challenge.

That’s what the LMS Analytics Ladder is built around. Each stage moves you closer to the metrics that matter most to the business. As a starting point, there are three key principles worth keeping in mind:

• Connect to a KPI leadership already tracks. Retention, productivity and time-to-competency are all measurable. Pick one and show how your training data relates to it.
• Show before and after. Comparisons give leadership a clear picture of what changed and what learning contributed to it.
• Quantify the cost of not training. Onboarding delays, compliance failures and skill shortfalls all carry a price. Making that visible strengthens the case considerably.

Generally speaking, the further up the ladder you go, the stronger the case you can make that learning activities are driving positive business outcomes. 

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