Learning Analytics: What L&D Teams Should Measure Beyond Completions

Learning analytics is the practice of collecting and analyzing data about learners and learning programs to understand what is working and improve it. In most companies, learning analytics starts and ends with completions, enrollments, and satisfaction scores. Those numbers show activity. They rarely show whether anyone got better at their job.

L&D teams are under pressure to show impact, and activity metrics do not make that case. This post covers the learning analytics that matter, how to collect skill data, and how to report results in a way business leaders find useful. It also covers how Yoodli approaches measurement for practice-based programs.

Why Completion Metrics Fall Short

Completion rates are easy to collect and easy to report. They answer one question: did people finish the training? That matters for compliance programs, but it says nothing about behavior change.

Satisfaction scores have the same problem. Learners can enjoy a course and change nothing afterward. Business leaders know this, which is why L&D reports built on completions and satisfaction often get a polite nod and little budget.

The stakes are rising. The LinkedIn Workplace Learning Report 2025 found that 49% of L&D professionals say their executives worry employees lack the skills to execute strategy. Answering that worry requires skill data.

A Framework for Learning Analytics

The Kirkpatrick Model gives a useful structure. It describes four levels of evaluation: reaction, learning, behavior, and results. Most L&D reporting lives at level one and level two. The value sits at levels three and four.

• Reaction: did learners find the training relevant and useful?
• Learning: did they gain the knowledge or skill?
• Behavior: are they using it on the job?
• Results: did business outcomes change?

Yoodli’s breakdown of the Kirkpatrick Model covers each level in more depth. For learning analytics, the key point is that behavior data is the bridge between training and results, and it is the level most programs skip.

The Learning Analytics That Matter

A useful learning analytics setup tracks a small set of metrics at each level. Here are the ones that tend to matter most.

Skill scores

Rubric-based scores from realistic practice show how well a learner performs a skill. Track them by criterion, so you can see which parts of a skill are strong and which need work.

Skill progression

Scores over time show improvement. A learner who moves from a low score to a passing score across several attempts is building the skill. A learner whose scores stay flat needs different support.

Time to proficiency

How long it takes a learner to reach a defined standard is one of the most useful numbers for onboarding and certification programs. Shorter time to proficiency means people contribute sooner.

On-the-job behavior

Signals from real work, such as call reviews, manager observations, or quality scores, show whether the skill transferred. This is the hardest data to collect and the most persuasive to business leaders.

Spread between performers

The gap between your strongest and weakest performers on a skill tells you how consistent the team is. A narrowing gap means training is lifting the middle and bottom of the team.

How to Collect Skill Data

Skill data requires observing performance. For human skills such as sales conversations, coaching, and customer support, that means scored practice and reviewed real conversations.

Yoodli Roleplays score each practice conversation against your rubric. Yoodli Coach reviews real calls from Gong and Kaia against the same rubric. Together they give you practice data and real-work data in the same format, which makes it possible to compare them.

Yoodli Measure brings the results together. It shows progress by learner, team, and program against your rubrics, in one dashboard with role-based access.

Reporting Learning Analytics to Leaders

Business leaders want to know three things: are people getting better, is the business seeing a difference, and where should we invest next. Build reports around those questions.

• Lead with skill change: show the before-and-after scores for the skills the business cares about.
• Connect to the business: pair skill data with a business metric the leader already tracks, such as ramp time, quality scores, or customer satisfaction.
• Show where to act: highlight the teams or skills with the biggest remaining gaps.
• Keep it short: one page with three or four charts beats a long deck.

Report on a regular rhythm. A quarterly skills update, sent before budget conversations start, keeps learning analytics in front of leaders when decisions get made. Include one short story alongside the numbers, such as a team that moved from below standard to certified in six weeks. Stories make the data memorable.

Use customer language. Sales leaders care about ramp and win rates. Support leaders care about quality and handle time. HR leaders care about manager effectiveness and retention. Frame the same skill data in each audience’s terms.

Examples of Skill-Based Reporting

Harness used Yoodli scoring to review rep practice and cut sales training review time by 75%. Clari used Yoodli AI roleplays and improved GTM conversation quality by 36%. In both cases, the reporting focused on skill and efficiency, which gave leaders a clear picture of what the program delivered.

Ochsner Health used Yoodli for frontline leader conversations and saw the biggest gains in the lowest-scoring skills. That is the kind of insight learning analytics should surface: where the program helped most.

Building a Learning Analytics Practice

Start with one program and one business question. For example: are new support agents reaching quality standards faster? Define the skill rubric, set up scored practice, and pick the business metric to compare against. Run the program for a quarter and report the results.

Then build out. Add programs one at a time, keep rubrics consistent where skills overlap, and standardize the report format. Within a year, L&D can show skill progress across the business instead of activity counts.

For data handling, choose platforms that meet your security standards. IBM’s overview of learning analytics covers broader considerations for collecting and using learner data responsibly.

Learning Analytics for Sales and Support Teams

Revenue and support teams are often the easiest place to show learning impact, because their work already produces data. Calls are recorded, quality is scored, and outcomes like meetings booked or customer satisfaction are tracked.

For sales teams, pair practice scores with ramp time, win rates, or meetings booked. USB Payments used Yoodli and booked 19% more meetings within 30 days. Yoodli’s post on measuring sales coaching effectiveness covers which sales metrics respond to coaching.

For support teams, pair practice scores with quality assurance scores and customer satisfaction. The Yoodli customer support use case shows how support teams practice difficult calls before they happen.

Mistakes to Avoid

Do not measure everything. A dashboard with fifty metrics hides the three that matter. Pick the metrics tied to your business question and ignore the rest.

Do not compare scores across different rubrics. A score on a discovery rubric and a score on a negotiation rubric measure different things. Keep comparisons within the same skill.

Do not report in isolation. Share drafts of your learning analytics with the business owner before a formal review. They will tell you which numbers matter to them and which ones need more context, and they will arrive at the review already invested in the results.

Do not wait for perfect data. Many teams delay measurement until they have every system connected. Start with the data you have, report what it shows, and improve the setup over time.

Do not use practice scores to punish. Learners need room to fail in practice. If early scores feed performance reviews, people stop practicing honestly, and your data loses its value.

Learning Analytics by Kirkpatrick Level

This table maps useful learning analytics to the four Kirkpatrick levels.

Level	Question	Example metric
Reaction	Did learners find it useful?	Post-session survey
Learning	Did they gain the skill?	Rubric scores in practice
Behavior	Are they using it on the job?	Scores on reviewed real calls
Results	Did business outcomes change?	Ramp time, quality scores, or win rate

Frequently Asked Questions

What is learning analytics?

Learning analytics is collecting and analyzing data about learners and training programs to understand what works and improve it. Useful learning analytics goes beyond completions and satisfaction to measure skill progress, on-the-job behavior, and business results connected to training.

What metrics should L&D teams track?

L&D teams should track skill scores by criterion, skill progression over time, time to proficiency, on-the-job behavior signals, and the spread between top and bottom performers. Completion and satisfaction still matter for some programs, but they do not show whether people improved.

How do you measure behavior change from training?

Measure behavior change by observing performance before and after training against the same rubric. For conversation skills, that means scored practice and reviewed real calls or meetings. Compare scores over time and pair them with manager observations to confirm the skill shows up on the job.

How do you show training ROI with learning analytics?

Connect skill improvement to a business metric leaders already track, such as ramp time, quality scores, or customer satisfaction. Show the skill change first, then the business change, and explain the link. A focused one-page report built around one business question is usually most persuasive.

Learning analytics earns L&D a seat in business conversations when it shows that people got better. Skill data from scored practice and real work gives teams the evidence to make that case.