LMS Data Analytics: What to Track and How to Use It

Every learner has unique skills, challenges, and ambitions. To nurture and develop that talent, you can’t rely on a one-size-fits-all approach. Personalization is the name of the game—and L&D teams can play that game using their own brand of analytics: LMS data. 

Wondering what LMS data is, what metrics are useful to track, and how to leverage that data within your L&D function? Read on to find out.

LMS data refers to the behavioral and performance information collected by a learning management system as learners interact with your content — including completion rates, engagement scores, assessment results, time spent, and content preferences. When analyzed, this data tells you whether your education program is working, who it's working for, and where it's falling short.

Why Is LMS Data Important?

LMS platforms help you create and host educational content—but that’s only half the battle. To know whether the education you’re providing is effective, you need data to prove it. When looking for the right LMS features, it’s important to prioritize an LMS that supports detailed reporting and data analysis.

With LMS data, you can: 

• Learn whether your audience is engaging with your content.
• Develop learner personas that reflect the diversity of your audience.
• Modify your learning content to make it more engaging and accessible.
• Personalize your content to speak to each learner’s unique interests or needs.
• Tie L&D efforts to business ROI and prove the value of your learning program.

By taking a data-driven approach to L&D, you can capitalize on your education program’s biggest successes—and turn “failure” into a stepping stone for future wins.

Benefits of LMS Data Analytics

According to our 2024 Customer Education Benchmarks & Trends report—in which we commissioned Forrester to survey 300 education leaders at the director level or above—80% of high-success organizations consider “access to multiple types of data” important to running a successful customer education program. 

That was the top response, outranking answers such as “mobile accessibility,” “platform scalability,” and even “content creation support.” Think about that: Educators consider the availability of data more important than having top-of-the-line creation or scalability tools.

Why is LMS data so coveted? Here are four compelling reasons:

1. Personalize with greater precision.

Every L&D team wants to create effective education that provides value. But doing so takes time, capital, and expertise. As you probably know well, all three can be tough to come by—especially when budgets are tight and bandwidth is low.

Yet, having less doesn’t have to equate to doing less. If anything, LMS data becomes more valuable when resources are limited, because you can use that data to ensure every bit of content you create matters. 

How? You use data to personalize your training to the exact needs and goals of your audience. If you’re building a product certification, you might use customer education metrics (e.g., CSAT, course completion rates) to identify which areas of your product clients struggle with most. If you’re developing internal training, you can leverage employee training statistics to align each employee’s learning with their career goals. 

Back your personalization with learning analytics, and you help ensure no piece of content goes to waste.

2. Engage your audience more easily.

To modernize training and avoid the “death by PowerPoint” effect, educators must extend beyond the lecture-and-slide style of delivering information. If you’re including text on a page, you can vary up the content by introducing the following:

• Visuals (infographics, photos, etc.)
• Interactive media (quizzes, sliders, etc.) 
• Video clips, GIFs, and other multimedia

(Great news: Intellum’s inline authoring tool, Evolve, allows you to create all these interactive elements and more!) 

LMS data analytics can help you understand how your audience likes to learn—so you can serve up the right materials at the right time. For instance, if your clients are showing stronger engagement rates with videos than with written courses, it may be an indicator for you to invest more heavily in multimedia education.

3. Create more accessible eLearning.

Personalizing content for your audience doesn’t just help with engagement—it also ensures the content you’re producing is accessible to the people you’re trying to reach.

No two people learn the same way. How they process information may vary depending on the topic, medium, or specific learning objective. By understanding each person’s accessibility preferences, you can design content that makes learning a breeze, rather than a chore.

4. Prove the ROI of your program.

You could have a stellar vision for your education curriculum, but without the funding needed to deliver that program, your greatest ideas stay on paper.

LMS data gives you the tools to make a business case for education. By sharing what’s working well—which courses are being completed, which audience segments are engaged, etc.—you can build confidence in your program and secure additional funding. If you have less-engaged learners, pitch how the program could deliver more value to these audiences.

Don’t have a current LMS in place? Start small. Create a minimum viable product for your education curriculum using tools your company already uses, like a content management system (CMS). Measure your efforts with a pilot group, then use those results as a springboard to make the business case for an LMS.

What LMS Data Metrics Should You Track?

With so much LMS data at your disposal, you might ask: Where do I even start?

Whether you're researching LMS options or looking to optimize your existing data collection, here are five LMS analytics worth tracking:

1. Completion and drop-off data

Usage data is the bread and butter of your LMS analytics. With the right LMS, you can discover which courses learners are completing, which they're dropping, and how long they're spending on individual pieces of content. Use this data to understand what content is resonating and what might need some healthy iteration.

Don't just look at overall completion rates. Look at where drop-off happens. A learner who exits at module one has a different problem than a learner who makes it to module seven and stops. When you map drop-off points against your content structure, you start to see whether the issue is motivation, relevance, difficulty, or something in the learning environment itself. Completion rate as a single number is a scorecard. Drop-off data by module is a diagnostic.

2. Engagement scores

Completion rates don't tell you the whole story. To know whether your audience is truly benefitting from your content, you'll want an LMS that supports engagement data. If you're creating a certification for customers, make sure your LMS supports feedback collection through surveys like the CSAT. Even better if your LMS can gather both quantitative feedback (e.g., Likert scores) and qualitative feedback (e.g., open-ended responses).

Watch for the gap between completion and engagement. A learner who finishes a course but rates it poorly, or who clicks through without meaningfully interacting with the content, is telling you something important. High completion with low engagement is one of the clearest signals that your content needs to change.

3. Assessment and knowledge retention data

Assessment data is where you find out whether learning is actually happening, not just whether the course was opened. Track pass rates overall, but also look at which specific questions learners are getting wrong most consistently. A question that a disproportionate number of learners fail tells you one of two things: either your content isn't teaching that concept clearly enough, or the question itself is poorly written. Both are worth investigating before drawing conclusions about learner performance.

Also pay attention to whether learners can pass assessments without engaging with your curriculum. If they can, your assessment isn't measuring learning; it's measuring prior knowledge. Pre- and post-assessment comparison is your most useful signal for proving measurable knowledge gain.

4. Time-on-content data

Time spent on individual pieces of content is one of the most underused metrics in LMS analytics. Unusually low time-on-content can signal that learners are skimming or skipping — a disengagement problem. Unusually high time-on-content can signal that learners are struggling to understand the material — a clarity problem. Neither shows up in completion rates alone.

Use time-on-content data in combination with drop-off and engagement data to build a complete picture of how learners are actually moving through your curriculum, not just whether they finished it.

5. Content preferences

To offer personalized learning journeys, it's important to understand your audience's content preferences. Just like how Netflix allows users to give a TV show a thumbs up or thumbs down, consider an LMS that allows learners to rate content they enjoyed or disliked. In doing so, you can offer timely and relevant recommendations based on the person's role and goals.

Content preference data also helps you make smarter investment decisions. If your learners consistently engage more deeply with video than with written courses, that's a signal about where to direct your content creation resources — not just a nice-to-have insight.

Tip: Daunted by the prospect of building out dedicated learning paths? Some education platforms, like Intellum, pair LMS data with AI to make personalized content suggestions based on each person's learning activity.

How to Use LMS Data to Personalize Your Education

So, you have an LMS, and you’re tracking the LMS data analytics that matter most. Now, the question becomes: How do you capitalize on that data to provide a best-in-class learning experience? Here are a few ways to do that:

Be transparent about your data.

As with any business intelligence, it’s important to use your data responsibly. Be transparent with your audience about the data you’re collecting. Explain why the data is relevant to the program, and reassure them that you’re only storing information to improve their learning experience. This can help build trust between the organization and its learners.

Embrace collaboration.

Analytics alone can only get you so far. Instead of dictating learning plans based on data, make learners a part of the process. If you’re working with employees, share the results and ask them how L&D can provide a more personalized learning path. If you’re creating client education, launch a discussion forum to communicate your curriculum roadmap and invite suggestions. 

Analytics aren’t infallible; having a conversation can provide more nuanced insights that inform the process moving forward. These conversations also empower learners to drive their own learning journey, which can result in greater satisfaction and engagement.

Harness the Power of LMS Data With Intellum.

Most L&D teams are comfortable designing content—but far fewer are comfortable poring over spreadsheets of data. With Intellum, you can connect learning data to business outcomes with ease. Build courses, certifications, and other eLearning materials using native authoring tools, then analyze performance from our in-app dashboard. For deeper analysis, use our integrations and robust API to export data to your BI tool of choice.

Frequently Asked Questions (FAQs)

What is LMS data?

LMS data is the behavioral and performance information your learning management system collects as learners interact with your content: completion rates, time on content, assessment scores, engagement patterns, and drop-off points. Education teams use this data to personalize learning experiences, prove business impact, and continuously improve their programs.

What metrics should I track in my LMS?

The core metrics to track are completion and drop-off data, engagement scores, assessment and knowledge retention data, time-on-content, and content preferences. The right combination depends on what your program was built to achieve,  but tracking these consistently gives you the foundation for a data-driven education program. (Learn about data-driven optimization of your education program in this blog.)

How do I use LMS analytics to improve learning outcomes?

Start by connecting your LMS analytics to the outcomes your program was built to drive. If your training is designed to reduce support tickets, compare ticket volume between learners who completed the program and those who didn't. If it's designed to accelerate onboarding, measure time-to-productivity across the same cohorts. LMS analytics on their own tell you what happened inside the platform: completion rates, engagement, assessment scores. But learning outcomes are proved outside the platform, in the business metrics your training was designed to move. The organizations that improve learning outcomes most consistently are the ones that define those external metrics before they launch, establish a baseline, and use their LMS analytics as one input in a broader measurement story.

What's the difference between LMS data and learning analytics?

LMS data is the raw information your learning management system collects, including completions, time on content, assessment scores, engagement rates. Learning analytics is what you do with that data: the process of analyzing, interpreting, and applying it to improve your education program and prove its business impact. Think of LMS data as the inputs and learning analytics as the practice of turning those inputs into decisions. A robust LMS gives you access to strong data. Learning analytics is the discipline that makes that data meaningful, connecting it to learner behavior, business outcomes, and program strategy. The distinction matters because collecting data and acting on it are two different capabilities, and most education teams are better at the former than the latter.