Can you prove your learning programs actually matter?

If your learning programs disappeared tomorrow, which business metrics would decline? And how would you prove it?  

Most L&D and CEd teams can't answer this with confidence. Not because their programs aren't valuable, but because they're measuring the wrong things; metrics that feel productive but don't predict business outcomes.  

Cookie-cutter measurement frameworks fail because they ignore your unique business model, customer journey, and strategic priorities. What matters for a SaaS company's customer onboarding is entirely different from what matters for a manufacturing company's safety training.  

This week, we're showing you how to stop measuring learning activity and start measuring business influence, with a practical roadmap you can adapt to your organization.

A version of this was first published in the All things learning newsletter. Want more content like this directly to your inbox? Subscribe here.

Stop measuring learning. Start measuring influence.

Think about most of the metrics you report: 85% completion rate, 4.5/5 satisfaction score, 10,000 hours of learning consumed. These activity metrics tell you people showed up, but don't tell you if showing up mattered.  

Influence metrics answer a different question: Did behavior change? Did performance improve? Did customers stay? Did revenue grow?  

Here's the difference:  

• ‍Activity metric: "750 sales reps completed product training this quarter."  ‍
• Influence metric: "Sales reps who completed product training within their first 30 days have a X% higher win rate and reach quota Y weeks faster."  

The future of L&D measurement is about mapping patterns of influence across your business, connecting learning behavior to the operational and financial outcomes that define success. But you can't find these patterns by accident, you have to build them.

The ROI roadmap: building your measurement system

Drowning in learning data but starving for insight? A lack of data is rarely the issue, it's the lack of a coherent plan for what to collect, how to connect it, and when to act on it.  

ROI is an iterative process, not a one-time project. Here's how to structure that cycle:  

Phase 1: Design your data strategy  

Start with business outcomes. What keeps your executives up at night? Revenue growth? Customer retention? Employee attrition? Your measurement strategy should ladder up to 2-3 outcomes that genuinely matter.  

Map backwards from outcomes to behaviors. What employee or customer behaviors directly influence those outcomes? For retention, maybe it's manager effectiveness and career development conversations. For revenue growth, maybe it's sales rep activity levels and deal progression.  

First, connect behaviors to learning, and be specific. Not leadership training improves retention but managers who complete our coaching skills program hold X more career development conversations, and teams with those managers have Y% lower attrition.  

Then, choose your engagement signals. Pick 2-3 that are predictive and accessible. This might be completion within a timeframe, assessment scores above a threshold, or a composite engagement score.  

Phase 2: Create your measurement infrastructure  

This is where most organizations stumble. They launch programs, then try to cobble together ROI data six months later. By then, it's too late. Connect your systems before you need the data.

Your LMS needs to talk to your CRM, HRIS, performance management tools, and customer success platforms. These integrations are non-negotiable for influence measurement.  

Build engagement scoring models. Raw activity data is noisy. An engagement score synthesizes multiple signals into a single predictive metric. Even a simple model works: (completion rate X recency X assessment performance). You'll refine it as you learn what predicts impact.  

Establish baselines. Before you claim improvement, document where you started. What's the current attrition rate? Win rate? Renewal rate? Capture these before your program launches.  

Create your hypothesis document. Write: We believe [learning intervention X] will influence [behavior Y] which will improve [business metric Z] within [timeframe]." This becomes your measurement blueprint.

Phase 3: Run, measure, iterate  

Launch with measurement infrastructure in place. Review data monthly, but be patient and give programs at least one quarter before major pivots. Influence takes time to show.

Look for correlation patterns, then investigate causation. Is it truly causal, or is there a confounding factor? Control for variables by comparing similar cohorts or running controlled pilots.  

Make adjustments and start again. You'll learn which engagement signals predict outcomes, which program elements drive change, and whether influence happens faster or slower than expected. Use these insights to refine your model and run the cycle again.  

What to measure in 2026  

The most valuable data reveals patterns of influence, connections between learning engagement and business performance that let you forecast impact, not just report it. But what you measure depends on your priorities.  

For L&D teams  

If your priority is retention...

‍Build an engagement scoring model for your learning programs. Correlate engagement scores with attrition data by team, role, and tenure.

You're looking for: Employees with learning engagement scores below X are Y times more likely to leave within Z months."  

Now you have a predictive tool to identify flight risks and intervene before they leave.  

If your priority is performance...

‍Track time-to-proficiency by cohort. Compare those who engaged deeply with onboarding versus those who did the minimum.  

Quantify it: New hires who complete onboarding plus role-specific training within X days reach full productivity Y weeks faster and have Z% higher performance scores.

This transforms onboarding from a checklist into a competitive advantage.  

If your priority is leadership effectiveness...

‍Connect manager learning engagement to team outcomes.  

Look for: Teams led by managers who completed coaching skills training have X% higher engagement scores and Y% lower attrition.

This proves leadership development is a lever for team performance.  

For customer education teams  

If your priority is retention...

‍Build a customer engagement score for learning. Map it against customer health scores, NPS, and renewal rates.  

Quantify it: Customers with high education engagement scores (>X) have renewal rates Y points higher than those with low engagement (<Z)."  

Now education engagement becomes an early warning system for churn risk.  

If your priority is product adoption...

‍Connect training completion to product usage data.

Compare customers who engaged with training versus those who didn't: Customers who complete advanced feature training adopt X times more features within Y days and generate Z times more expansion revenue.

This proves education directly drives revenue growth.  

If your priority is reducing support costs...

‍Track customers who engage with proactive education versus reactive support.  

Prove the relationship: Customers who complete our onboarding certification submit X% fewer support tickets in their first year, and tickets are resolved Y% faster.

This transforms education into a strategic lever for operational efficiency.  

The critical connection: None of these metrics matter on their own

Here's what separates organizations that prove ROI from those that struggle: integrated insight models, not isolated dashboards.  

You can't demonstrate influence with learning metrics alone. And you can't understand business metrics without knowing what drives them. The power is in the connection, linking learning behavior to operational outcomes through data integration and engagement scoring.  

When your learning platform talks to your CRM, HRIS, and business intelligence tools, you can finally answer the question executives care about: "What happens to business performance when learning engagement changes?"  

That's when learning programs stop being seen as a cost center and start being seen as a measurable growth lever.

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