How sales, training, and revops leaders use learning analytics to boost results

Sales teams are pouring more time and money into training than ever, yet many leaders struggle to understand what’s moving the needle. Are sales reps absorbing the right skills? Is coaching making a difference? Is training translating into closed deals? And what type of training works best—in-house, third-party, or e-learning?

Learning analytics bridges this gap, turning all this effort into practical knowledge to support learners. Collecting data is not just for big data analyst experts; it can be a shared tool that empowers sales enablement, training, and revenue operations (RevOps) teams to make smarter decisions.

This article breaks down an introduction to learning analytics, its benefits, and how it helps revenue leaders align training with real business goals. You will also find practical examples and a training impact checklist to analyse your training data sets, helping you identify patterns of what works and what doesn’t.

What is learning analytics?

Learning analytics collects and analyses data to track student progress and enhance learning outcomes. It reveals what’s working, what needs improvement, and the next steps to take.

Think about it as educational data mining with a purpose. It removes the guesswork from intuition-based training to informed decision-making. Instead of sifting through raw numbers, learning analytics focuses on patterns that reveal how training impacts skills, engagement, and revenue. For example, it can show whether a sales methodology training leads to more closed deals or if a coaching session improves rep confidence, providing new insights for early intervention.

The Society for Learning Analytics suggests that historical data analytics in higher education and corporate training can improve outcomes—particularly when enhanced by data science methods like artificial intelligence and machine learning.

Types of learning analytics

There are four types of learning analytics. Each one offers a unique lens into performance. From tracking past behaviors to predicting future outcomes, these data analytics will help leaders improve learning.

• Descriptive analytics: Looks at what happened based on past data. For example, 80% of sales reps completed a course in your sales onboarding software, but only 50% applied the necessary skills in customer calls.
• Diagnostic analytics: Digs into why something happened. For example, sales reps who skipped interactive role-plays in training struggled with objection handling.
• Predictive analytics: Forecasts what might happen. For example, sales reps with low engagement in sales coaching sessions are 30% less likely to meet their quota.
• Prescriptive analytics: Recommends what to do next. For example, tailored coaching should be assigned to sales reps with low content engagement because engagement boosts deal-closing rates.

These categories are used across various industries, where educational technology and digital learning continue to evolve, spanning enterprise training, higher education institutions, and online learning environments, such as massive open online courses (MOOCs). The goal: to personalise experiences, support students’ learning experiences, drive student engagement, and improve teaching and learning outcomes.

Who benefits from using learning analytics?

Learning analytics may sound complicated or reserved for data experts, but it’s beneficial for anyone focused on growing revenue. Sales enablement, training, and RevOps leaders can all use the same data to get valuable insights and stay aligned on what drives performance.

With the continued rise of digital and remote training, using analytics to measure success is more important than ever, especially when direct, face-to-face feedback isn’t always possible. By tracking engagement, skill development, student progress, and business outcomes, teams can identify what works and make adjustments in real-time.

In fact, educational research shows that 61% of high-performing organisations monitor performance metrics, compared to fewer than one-third of low performers. This reinforces the value of embedding analytics programmes across your training courses and sales enablement strategy. Here’s how each role utilizes analytics to tackle their biggest challenges and achieve goals.

Sales enablement leaders use data-informed training to boost sales rep performance

Sales enablement leaders want to set sales reps up for success. They give them the right training, content, and learning tools to close deals. However, without precise data collection, determining what to provide is merely guesswork.

Learning analytics tools show which training programmes, content, and skills drive the best performance. By seeing what’s effective, enablement teams can improve everything from onboarding to supporting sales reps throughout every customer interaction and stage of the buyer journey.

Analysis starts by identifying high-impact skills. For example, McKinsey shares a case study of a telecom company that used analytics to pinpoint 15 skills associated with above-average customer productivity. They built tailored learning journeys for 1,000 sellers. The result? A 10% increase in deal size per sales rep, proving the power of data-driven training. The learning plans were flexible, using self-paced and asynchronous modules, which allowed sales reps tofocus on relevant content and skip what they already knew, optimising resource usage.

How do sales enablement leaders use learning analytics?

• Content engagement: Identify and prioritise high-performing content, such as pitch decks or product playbooks. If data shows that a pricing guide boosts close rates by 20%, prioritise this in training.
• Training completion: Find and fix content drop-off points. For example, analytics in sales may reveal that sales reps are skipping interactive role-plays in training. Adding engagement features could increase completion and retention.
• Quota attainment: By linking training and content to sales outcomes, sales enablement analytics show what works. For example, sales reps with access to high-engagement content and onboarding hit quota 30% more often. To develop a winning sales engagement strategy, ensure your sellers know how and when to reach customers.

Coaches and trainers accelerate skill growth with personalised learning

Generic training rarely sticks. Learning analytics helps identify skill gaps and tailor learning paths for each sales rep based on their individual needs. For example, if data shows a sales rep struggles with objection handling, coaches can deploy targeted role-plays or microlearning modules. This personalised approach to training a sales team, backed by learning analytics research and data, reflects the latest human-centred design and improves student success, skill retention, confidence, and motivation.

Metrics for coaches and trainers include:

• Skill mastery rates: Are reps learning the essential sales skills faster through personalised training?
• Time-to-productivity: Does tailored onboarding speed up time to quota?
• Behavioural changes: Are analytics-driven interventions leading to lasting behaviour changes and more confidence?

RevOps leaders align training to pipeline and revenue goals

RevOps leaders care about predictable revenue. Learning analytics programs show how alignment across functions impacts the bottom line. Gartner research shows companies with a revenue enablement strategy—integrating sales, marketing, and customer success—are 75% more likely to exceed performance targets like seller revenue and upsell rates.

To make these connections seamless, many organisations rely on a revenue enablement platform that brings together training, content engagement, coaching, and performance data in one place. For example, analytics might show that sales reps who complete a strategic selling course increase deal velocity by 10%, or that consistent just-in-time coaching improves forecasting accuracy.

Metrics for RevOps include:

• Enablement ROI: Measure and prove the impact of training investments on revenue growth.
• Deal velocity: Do training programmes enable sales reps to close deals faster and with less friction?
• Forecasting accuracy: Does coordinated training and coaching lead to better sales forecasting and fewer pipeline disruptions?

These new insights also help guide strategies to increase sales by focusing attention on what directly impacts pipeline progression, deal success, and creates competitive advantage.

Sales training impact checklist

Use this checklist to determine if your sales training and development programmes drive real results. It’s designed for sales enablement, coaching, and RevOps teams to align around shared goals and identify opportunities for improvement.

First, review your current training and development programmes. Then, gather learning data from your existing systems, including your learning management system (LMS), sales enablement metrics from tools like Highspot, and your CRM. You will need training completion reports, content engagement metrics, quota and performance dashboards, and coaching session logs.

This isn’t a one-time audit. It’s a repeatable learning process. Revisit the checklist quarterly with all stakeholders to ensure your training strategy continues to support business outcomes and sales performance.

Question	Why it matters
Are sales reps completing the full onboarding journey?	Completion is the baseline for performance impact.
Are high-performing reps engaging more with certain content?	Reveals what’s working and worth scaling.
Is training linked to improved quota attainment?	Connects learning to revenue results.
Have you identified the top 3 skills tied to sales success?	Focus ensures ROI on training effort.
Is coaching tailored to individual skill gaps?	Personalisation improves retention and confidence.
Do RevOps dashboards track enablement ROI or deal velocity?	Shows the strategic value of training investments.

Bring your revenue teams together with learning analytics

Too often, teams operate on instinct, secondhand feedback, or trends that don’t reflect real-world challenges. While these inputs may seem helpful, they’re anecdotal, not actionable. To create meaningful improvement, you need data that shows your strengths, gaps, and next steps for your team, industry, and go-to-market strategy.

By mining insights from learning and enablement, you can stop guessing and start making a real, measurable difference. Even small, data-informed steps forward, like identifying high-impact content, can lead to bigger wins over time.

With Highspot, you don’t need to be a data scientist to connect training, content, and sales activity to business outcomes. Our platform’s robust scorecards and unified analytics, backed by strong data security, provide your revenue teams a clear view of what’s working and where to improve so you can act quickly and stay aligned.