Analytics play an important role for most businesses; after all, data informs business decisions that improve future performance. Similarly, learning analytics can improve employee performance.

When: Before, During and After Training

Where does the data come from for successful learning analytics? For meaningful analyses, consider collecting data before, during and after training.


Create a uniform system for training requests from managers and employees to help identify knowledge gaps and other needs. Within that system, ask about prior training and what’s worked and what hasn’t worked not. These questions will help you design an effective curriculum roadmap that integrates analytics.

Get to know your employees to understand which interventions work for whom. Unless all your employees have the same age, gender, education, attitude, cultural background and level of experience, you might want to know how those variables modify learning outcomes. You can then use that information to better assess the needs of specific learner types and address a diverse audience.


If you are using digital media such as video, virtual reality (VR) or other eLearning, it can be easier to include data collection in the design of your training. For instance, you can track participation and completion to find out if you need to promote or modify the training.

For example:

    • Who participates in each type of training and why?
    • Do learners frequently drop out of a course during a particular type of interaction or after a certain amount of time?
    • Do learners skip over items?

You can also track more complex behaviors like decisions within games and scenarios. Use all of this data to inform future instructional design. By analyzing specific interactions, you may discover that you only need to redesign one part of a course rather than the entire course.


Summative assessments that are aligned to learning objectives give you valuable information. Combined with in-course tracking and learner profiles, assessments can help you identify which course elements contributed to learning outcomes — and for whom. A well-designed assessment can also help you determine appropriate course levels for specific programs and support adaptive learning solutions.

It’s also important to consider collecting data on job performance that is linked to the training. For example, does sales training translate into more sales? Does safety training translate into fewer accidents or violations? You might also consider analyzing how employee satisfaction with training relates to job performance. Are satisfied employees better at their jobs?


Successful analytics helps learning and development (L&D) professionals better understand employee behavior and provide better job support. The type of analysis performed is determined by your goal.

First, analytics can describe what happened in a training program. It’s a simple question, but without collecting the right data, it’s difficult to accurately answer. You may have the general sense that more employees are participating in optional developmental training programs, but by how much — and who is participating?

You can also use analytics to diagnose why something is happening. Look for correlations and patterns. For example, what variables might be influencing an increase in training participation? Are the employees participating in more training looking for promotions? Do you have a new vendor developing custom training that more employees are finding engaging? Be sure to collect information about the individual learners and their context to develop a full picture of the situation.

Predictive Versus Prescriptive Analytics

You can use data patterns to predict outcomes. A predictive model goes beyond simple correlation and looks for effect sizes that indicate how well an intervention predicts an outcome. Predictive analytics can both explore and confirm the success of an intervention.

Prescriptive analytics, on the other hand, can help you determine solutions by guiding the design of evidence-based learning solutions. In addition, you can integrate prescriptive analytics into your solutions for adaptive learning and just-in-time simulations.


At the most basic level, learning analytics involves data collection, analysis and interpretation. As with other business analytics, it includes:

    • Collecting meaningful data about learners and their behavior.
    • Using appropriate methods to analyze the data for trends and outcomes.
    • Interpreting and reporting on the analysis to learners (For behavior change) or managers (for other action).

Hopefully, the final step occurs when managers and/or learners act on the information to improve outcomes.

The Importance of Technology

For successful learning analytics, it’s critical to have the right tools and technology. Do you have the learning management system (LMS) and other technology to collect the data you need? Do you have the technical support to use that technology for data collection and analysis?

When choosing technology, consider which data you want to collect. For instance, mobile platforms can trigger learning interventions based on location, time or images (such as strategically placed QR codes). VR platforms can track movements involved performing physical tasks like machine repair or even surgery. Games can use embedded assessments that track decisions in scenarios and behavioral adjustments based on feedback.

Incorporating learning analytics into your L&D processes can be challenging, but it’s worth the effort. Depending on the complexity of your desired analytics, you may want to hire an expert, or you may have someone in house who can offer statistical or technical support. In either case, the data is there, and successful learning analytics can improve employee performance in training and on the job.