What gets measured gets managed. Unfortunately, what L&D typically measures—completion rates, satisfaction scores, training hours—doesn’t tell us much about actual impact.

It’s time to rebuild L&D measurement from the ground up. Here’s how to create a metrics framework that matters for 2026.

What’s Wrong with Current Metrics

Let’s be honest about typical L&D dashboards:

Completion rates tell us people clicked through something. They don’t tell us anyone learned anything or changed behaviour.

Satisfaction scores tell us people didn’t hate the experience. They don’t correlate well with actual learning or application.

Training hours tell us how much time was spent. They don’t indicate whether that time was well spent.

Enrollment numbers tell us how many signed up. They don’t indicate capability development.

These metrics are easy to collect, which is why they persist. But they answer the wrong questions.

Leadership wants to know: Is L&D developing the capabilities our organisation needs? Current metrics can’t answer that.

The Right Questions

A good metrics framework answers questions that matter:

Are people learning? Is actual knowledge and skill development occurring?

Are people applying? Is learned capability being used in actual work?

Is capability improving? Are individual and organisational capabilities growing?

Is business impact occurring? Are business outcomes improving as a result?

Is investment efficient? Are we achieving outcomes at reasonable cost?

Build metrics that answer these questions.

The Metrics Framework

I recommend organising metrics into four tiers:

Tier 1: Learning Effectiveness

Did learning actually occur?

Knowledge acquisition:

• Pre/post assessment score changes
• Knowledge retention over time
• Assessment pass rates (meaningful assessments, not trivial ones)

Skill development:

• Demonstrated skill improvement in practice environments
• Certification achievement
• Competency assessment results

Confidence and self-efficacy:

• Pre/post confidence changes
• Perceived readiness for application

These metrics require more than completion tracking. They require actual assessment of learning.

Tier 2: Behaviour Change

Is learning being applied?

Application indicators:

• Self-reported application rates
• Manager-observed behaviour change
• System usage data (for technology skills)
• Work product evidence

Transfer success:

• Time to application
• Consistency of application
• Quality of application

Sustainability:

• Application rates at 30, 60, 90 days
• Maintenance of behaviour over time

Behaviour change metrics require follow-up after training, not just measurement during.

Tier 3: Business Impact

What outcomes result from capability development?

Performance metrics:

• Productivity measures
• Quality indicators
• Error rates
• Cycle times

Business outcomes:

• Customer satisfaction
• Revenue indicators
• Cost metrics
• Innovation measures

Strategic progress:

• Strategic capability assessments
• Readiness for strategic initiatives
• Competitive capability comparison

Impact metrics require partnership with business stakeholders to identify and track relevant outcomes.

Tier 4: Efficiency and Reach

Is L&D operating efficiently?

Reach:

• Percentage of target population developed
• Development equity across demographics and locations
• Coverage of critical skills needs

Efficiency:

• Cost per learner
• Cost per capability developed
• Time to competency
• Resource utilisation

Sustainability:

• L&D team capacity
• System and platform health
• Vendor and partner performance

Efficiency metrics ensure L&D operates sustainably while achieving impact.

Building the Dashboard

Create a dashboard that communicates across audiences:

Executive View

High-level indicators:

• Capability readiness for strategic priorities
• ROI summary for major investments
• Critical skills gap status
• Comparison to benchmarks

Executives need strategic perspective, not operational detail.

L&D Leadership View

Operational indicators:

• Program effectiveness across portfolio
• Learning effectiveness trends
• Behaviour change rates
• Efficiency metrics

L&D leaders need visibility into what’s working and what isn’t.

Program-Level View

Detailed program metrics:

• All tier 1-3 metrics for specific programs
• Comparison to targets
• Improvement over time
• Specific areas for enhancement

Program owners need detail to improve specific programs.

Learner View

Individual perspective:

• Personal capability progress
• Learning activity completion
• Skills development trajectory
• Recommendations for next steps

Learners need visibility into their own development.

Data Collection Strategies

Better metrics require better data:

Learning assessments:

• Build meaningful assessments into programs
• Use spaced assessments to measure retention
• Include practical demonstrations, not just knowledge tests

Follow-up measurement:

• Survey learners about application after programs
• Gather manager input on observed behaviour change
• Access system data where available

Business data integration:

• Partner with analytics functions to access relevant business data
• Connect learning data with HR and performance data
• Build analysis capability to find relationships

Sampling approaches:

• You don’t need to measure everything for everyone
• Strategic sampling can provide insight at lower cost

Implementation Roadmap

Building comprehensive metrics takes time. Phase the work:

Phase 1: Foundation (3-6 months)

• Define key metrics for each tier
• Establish baseline data collection
• Build initial dashboards
• Train team on metrics use

Phase 2: Enhancement (6-12 months)

• Improve data quality and coverage
• Add behaviour change measurement
• Begin business impact analysis
• Refine dashboards based on use

Phase 3: Integration (12-18 months)

• Integrate learning data with HR and business systems
• Build predictive capabilities
• Establish ongoing measurement processes
• Demonstrate strategic value through data

Phase 4: Optimisation (Ongoing)

• Continuous refinement based on learning
• Advanced analytics and prediction
• Automated data collection where possible
• Evolving metrics as needs change

Common Pitfalls

Avoid these mistakes:

Measuring everything. Focus on metrics that matter. Too many metrics creates noise.

Perfect as enemy of good. Imperfect data that you act on beats perfect data that you never collect.

Metrics without action. Data is useless unless it drives decisions. Connect metrics to actions.

One-time projects. Measurement must be ongoing to track trends and improvement.

Ignoring qualitative data. Numbers need narrative. Include stories alongside statistics.

The Cultural Shift

Better metrics require cultural change:

From activity to outcome. Shift focus from what L&D does to what L&D achieves.

From anecdote to evidence. Ground claims in data, not just stories.

From defense to improvement. Use metrics to get better, not just justify existence.

From L&D-centric to business-centric. Frame metrics in business terms, not learning terms.

This shift is challenging. Many L&D functions have survived on activity metrics because those were easier. Outcome metrics require more work and more accountability.

But outcome metrics also create more credibility and more strategic relevance. The shift is worth it.

The Bottom Line

Most L&D dashboards measure the wrong things. They track activity, not impact.

Building metrics that matter requires:

• Defining metrics across learning, behaviour, and business impact
• Collecting data that goes beyond completion tracking
• Building dashboards that serve different audiences
• Creating a culture that values and uses data

The organisations with strong L&D metrics will:

• Demonstrate strategic value credibly
• Improve continuously based on evidence
• Allocate resources to highest-impact investments
• Build L&D credibility with leadership

Those without will continue struggling to prove their value.

Build the metrics that matter. Your L&D function—and your organisation—will be better for it.