When most people hear the term learning analytics, they immediately think about proving the impact of learning: did the program work, did it change behavior, did it deliver results? While these are important questions, they’re also limited. They lock analytics into the role of auditor — checking after the fact whether an investment paid off.

But analytics can be so much more. It’s not just about demonstrating value after delivery; it’s about creating value before, during, and beyond the learning experience.

Learning data can act as an early warning signal for organizational health, a predictor of workforce movements, a measure of business agility, or even a lens on hidden influencers who shape adoption.

This newsletter is intended to inspire you to move beyond the traditional notion of analytics as “proof” and explore its role as a strategic capability: one that anticipates needs, accelerates response times, and connects learning directly to the pulse of the business.

Please do read until the end, as arguably the most valuable application of learning analytics is the 6th and final one!

Predicting Workforce Fatigue

Over the past quarter, you notice a growing number of employees starting courses late, dropping out halfway, or taking longer to complete than before. At first glance, this looks like a learning problem: maybe the program design is poor, or the topics aren’t engaging. But what if it’s not about the program at all?

These small signals can be early signs of something much broader: organizational fatigue. When people are overwhelmed by workload, constant change, or simply running on empty, one of the first things that slips is discretionary activity — like learning.

And here’s where L&D has a unique role to play. You’re not the owner of workforce health, but you are sitting on data that others don’t always look at. Learning analytics can provide a first indication that the organization is tired. If you combine these learning signals with other workforce metrics — such as sick leave, late arrivals, overtime, or even drops in productivity — the picture could become very clear.

This reframes L&D from being a reactive service provider to being an early radar for HR and business leaders. Instead of waiting for engagement surveys or performance reviews to reveal the cracks, you can proactively flag a risk much earlier.

Think of the impact:

• HR can use these insights to adjust workload distribution.

• Business leaders can plan change initiatives with more sensitivity.

• L&D can time program launches more strategically, avoiding rollout during peak fatigue.

Of course, this isn’t easy. You need access to broader people data, and you need to make sure compliance learning or role-critical programs don’t distort the signal. But the point is: L&D can move beyond measuring learning to contributing to organizational resilience.

Forecasting Talent Mobility: Reading Between the Learning Lines

One of the most underestimated powers of learning analytics is its ability to act as a leading indicator for talent mobility. Most organizations look at turnover after it happens. They track exit interviews, attrition rates, or external job market signals. By then, it’s too late.

But what if you could spot the signals earlier, hidden in your learning data?

Think about the employees who suddenly start signing up for programs outside their role or beyond current company priorities. A financial controller who enrolls in “interviewing skills” or “speaking with confidence.” An engineer exploring project management or external certifications. These patterns often say more about a person’s career trajectory than their current role ever will.

Now add a layer of analytics: correlate learning activity with actual movers and leavers. Do the people who left your organization show a spike in certain types of learning in the months before they resigned? Do internal movers consistently prepare themselves with the same kind of courses before promotion or lateral shifts? If you can answer “yes” to these questions, you’re no longer looking at learning for learning’s sake — you’re looking at the future state of your workforce.

Of course, there are challenges. You need to strip out compliance training (nobody is taking GDPR refreshers to prepare for a career leap). You also need to connect learning topics to roles, so you can spot what is truly “outside the job scope.” But the potential is enormous.

This insight doesn’t just belong to L&D. It feeds straight into HR and business strategy:

• For HR: early warning of potential churn, so interventions can happen before resignation letters hit the desk.

• For managers: visibility into career aspirations of their team, allowing for better talent conversations.

• For L&D: a chance to position learning not as a cost center, but as a predictive tool for workforce planning.

Time to Market: Learning’s Business Speedometer

When the business faces a challenge, speed matters. New compliance requirements, a sudden shift in the market, or a critical skills gap — none of these wait for L&D to finish lengthy design cycles. This is where Time to Market (T2M) becomes a game-changing metric for learning organizations.

Time to Market in L&D is the number of days between identifying a business need and launching the learning solution that addresses it. It’s the equivalent of how fast product teams move from idea to launch. And just like in product development, T2M is not just about speed for the sake of speed — it’s about competitive advantage.

Consider two scenarios:

• Company A takes six months to launch an AI upskilling program. By the time employees get trained, competitors are already streamlining workflows, automating reporting, and cutting costs. Company A falls behind.

• Company B delivers in six weeks. Employees start using AI to reduce reporting effort, accelerate customer response, and create smarter insights. Company B stays ahead of the curve.

That difference is measurable, and it’s where L&D demonstrates real business impact.

Now, where does learning analytics come in? Analytics helps you dissect T2M:

• Knowledge & Skills: Do your teams have the capability to build at a fast pace?

• Processes: Are messy processes, unnecessary approval cycles and governance slowing you down?

• Tools: Is your tech stack enabling rapid prototyping and deployment, or holding you back?

• Resources: Are projects consistently underfunded or under-resourced?

• Opportunity Windows: Are you launching at the right time, aligned with business rhythms?

By tracking T2M over time and linking it to business outcomes, you create a feedback loop. If shorter T2M correlates with fewer compliance breaches or faster sales enablement, you’ve proven impact in terms the business understands.

This is why T2M deserves a place on the executive dashboard. It shifts the conversation from “How many people completed?” to “How quickly can we protect revenue, reduce risk, or seize opportunity?” It reframes L&D as a partner in business agility.

Uncovering Invisible Influencers & Supporters.

Every organization has them: people who quietly make the system work but rarely show up on an org chart. They’re not always the formal leaders or subject matter experts. Instead, they are the ones everyone goes to for quick answers, shortcuts, or reassurance. The hidden network of knowledge creators, brokers, and supporters is the real engine of capability building — and learning analytics can help us find them.

Creators are the employees who develop new content, share expertise, or answer questions on platforms like SharePoint, Teams, or an LXP.
Brokers are the ones who pick up that knowledge and spread it, making connections across teams and functions.
Supporters are the people who actively promote learning opportunities, encourage peers to join programs, and normalize learning as part of work.

These people rarely get recognized formally, yet they are often the difference-makers when it comes to adoption and engagement.

Learning analytics provides a way to surface them. By analyzing:

• Content activity: Who is creating materials, answering questions, or uploading resources?

• Engagement ripple: Whose posts or contributions trigger further activity?

• Referral patterns: Who is consistently promoting programs to peers?

What’s fascinating is that these patterns don’t always match traditional hierarchies. Sometimes, the most influential person in a function is not the manager, but the “go-to” colleague who has built credibility through micro-contributions over time.

Why does this matter? Because once you know who the invisible influencers are, you can:

• Engage them as change agents in transformation programs.

• Support them with better resources so they can amplify impact.

• Recognize their contribution, turning invisible influence into visible value.

And there’s a deeper insight here. By mapping knowledge creators and brokers, you uncover the knowledge flows in your organization. You see where expertise originates, how it travels, and where it stalls. That’s not just L&D data — that’s organizational intelligence.

Designing for Analytics: How Data Makes Better Learning Design

Think of it this way: analytics doesn’t just measure learning. It measures the choices you made in the design.

But here’s the bigger play: analytics is also one of the best tools you have to improve design. Too often, design is treated as a one-time creative exercise. You build the course, ship it, and hope it works. Analytics changes that game. When done right, it can give you a continuous feedback loop that tells you what actually works for learners — and what doesn’t.

Here’s what analytics can tell you about design:

• Navigation patterns show whether the flow of your learning journey makes sense or causes friction. If learners drop off after the third screen, it’s not necessarily about motivation — it could be poor design.

• Time-on-task highlights where content is either too heavy (cognitive overload) or too light (not challenging enough).

• Dropout clusters reveal exactly which sections cause disengagement.

• Application data shows if workplace tasks were too complex, too vague, or not embedded well into daily work.

This is feedback you could never get from a smile sheet. It’s not opinion, it’s evidence. And once you see it, you can make design sharper, faster, and more relevant.

Of course, the reverse is also true. If you don’t consider analytics during design, you limit yourself. You’ll be stuck reporting on completions and evaluation scores only, with no way to understand why learners behaved as they did. By embedding smart checkpoints — scenario questions, reflection prompts, peer interactions — you design for better insights as well as better learning.

The shift is subtle but powerful: design is no longer just about what learners see, it’s also about what data you capture to learn from them. That means design and analytics reinforce each other. Analytics makes design better. And design choices expand what analytics can do.

Proactively Identify Learning Needs with Predictive & Prescriptive Analytics.

This is one of my favorites and in my opinion one of the most powerful applications of advanced learning analytics: proactively identify learning needs.

Most L&D teams focus their analytics on what has already happened: who completed, how satisfied learners were, what the test results show. But the real power of analytics is not just to describe the past — it’s to anticipate the future and prescribe the next move.

One of the richest opportunities lies in the gap between learning demand and learning supply. Employees are constantly signaling what they need — through search queries, requests for programs, and even informal learning activities.

There are many more potential sources for not just identifying needs, but predict them as well. And in this case more data is always better:

Potential Data Sources to Spot Learning Needs

• Learning Systems (LMS / LXP)
  Search queries, course requests, and trending enrollments show what employees are actively looking for. This is the most direct expression of learning demand.

• Employee Portal & Intranet
  Search logs here often reveal broader needs, not always captured in L&D systems. If employees type “AI in marketing” on the intranet homepage, it’s a signal of curiosity and unmet demand.

• Collaboration Tools (Teams, Slack, Yammer, etc.)
  Questions asked in open channels provide unfiltered insight into where people are struggling. If “data visualization help” keeps popping up in Teams, that’s demand data.

• HR & Talent Systems
  Promotion patterns, role changes, and career aspirations give context: if many people are moving into leadership roles, leadership skills demand will follow. If there are many open positions, you can count on increased needs for onboarding.

• Performance Data
  Sales performance, customer satisfaction scores, production quality metrics — when these dip, they could signal skill gaps. Poor customer experiences, quality issues, or rising error rates can point to blind spots in people capabilities that you can actively address with the right programs.

• Risk & Incident Data
  Safety incidents, compliance breaches, or IT security lapses are strong indicators of where knowledge or skills are missing or insufficient.

• External Signals
  Labor market analytics, industry trend reports, and competitor offerings can complement internal data to forecast future needs.

Now add predictive and prescriptive analytics:

• Predictive: Spotting demand signals early and forecasting which skills or topics will rise in importance, based on employee searches, enrollment trends, or even external labor market data.

• Prescriptive: Recommending concrete actions — should you build internally, buy externally, or curate content quickly to close the gap?

The business impact is huge. By addressing blind spots proactively, you reduce the lag between business need and program availability. In other words: you shorten time-to-market (see also here).

This is where L&D shifts from reactive order-taker to proactive advisor. Instead of waiting for the business to articulate needs (often late or even too late), you anticipate them. You walk into the conversation with data that says: “Here’s where demand is rising, here’s what we don’t yet cover, and here’s what we recommend doing about it.” That’s a different seat at the table.

And there’s an added benefit: these insights can also inform other functions. If you see an uptick in demand for negotiation training, it could be a signal for Sales. If “resilience” searches spike, HR might need to look at well-being initiatives. L&D becomes not just a provider of learning, but a strategic radar for the organization.

Let’s make data work for you.

Best,

Peter Meerman