Learning measurement is not an easy feat. We have all struggled to demonstrate the value of training to stakeholders. This is because learning professionals are creative creatures by nature: Thinking about insights and analytics is outside of our general expertise. However, measuring training effectiveness is an essential part of our job, and determine how learning makes a difference within our organizations.
Most training functions barely scratch the surface when it comes to measuring training effectiveness. They often fail to leverage their data for meaningful insights, comparisons, or predictions that align with the organization’s development needs. One of the biggest pitfalls learning organizations fall into is relying on vanity metrics.
Vanity metrics are metrics that give a sense of success or value about our solutions, but don’t give the real picture of whether these solutions have solved a problem. Examples of vanity metrics can include:
- How many learners completed the content?
- How many hours of learning were consumed?
- Was the learner satisfied with the training?
- Would the learner recommend the solution to a friend or colleague?
These types of metrics are used daily in our learning functions and many learning teams do not go further. Sometimes these metrics give us a first view of engagement that can be a data point of how we continuously improve. However, this analysis is missing a key component of how our outcome is evidenced. So, how do we move from vanity metrics to something more concrete? Why is this essential?
To go beyond vanity metrics, think about the data needed to evidence the outcome (what you expect from the learner post-training). For example:
- How did the solution enable skill growth or application? Why is this important to the business?
- How did this solution meet the outcome?
- What was the impact either directly or indirectly on the business problem that was identified in the needs analysis?
Tip #1
It begins with a thorough learning needs analysis. A good learning needs analysis should not only include the training problem to solve, but it also must provide a clear concise depiction of the change you expect. A poorly written learning outcome prevents moving beyond vanity metrics, as it fails to clearly define the expected learner achievement needed to solve the training problem. Below you will find an example of an outcome and how it could be improved to ensure you can attain greater insights.
| Original Outcome | Improved Outcome |
| Learners will understand the importance of marketing.
| At the end of this module, learners will be able to identify and describe the key components of a successful marketing campaign. |
Tip #2
Once you have a good learning outcome that can be evidenced, think about what metrics are needed to evidence your outcome. Consider what metrics will help articulate the outcome is met. See the example below as a guide.
Learning outcome.
By the end of this module, learners will be able to identify and describe the key components of a successful marketing campaign.
Success metrics.
- 80% or higher score on an assessment evaluating learners’ ability to describe the key components of a successful marketing campaign.
- Number of learners who launch a marketing campaign after completing the training course.
- Percentage increase in website or webpage traffic after the marketing campaign.
- Percentage increase in sales after the marketing campaign.
Tip #3
To collect the data you need to support the metrics/targets you hope to achieve, it’s important to identify the metrics and embed the right measurement tools. Measurement tools come in many formats. However, it’s important to take time to consider during your needs analysis what tools will need to be applied in your solution, before your solution is scoped or designed. Look at the example of a learning outcome and what data could provide the evidence to support the outcome. Think about where this data is held. Does this data exist? Is it L&D sourced data or will the data need to be sourced from other teams or data repositories?
Data required to assess the outcome.
Some examples of data for this learning outcome could be:
- Post-course assessment report (minimum score of 80%).
- Website traffic data (pre and post-campaign launch).
- Sales reports (pre and post-marketing campaign launch).
In this scenario, not only L&D data will need to be assessed, but also data typically managed by the Marketing and Sales teams. By analyzing these combined insights, your data story could highlight:
Learners who completed the marketing training successfully identified key components of a winning campaign. As a result of post-training, the company saw a measurable X% increase in website traffic, which in turn contributed to an X% boost in sales. This demonstrates the direct impact of effective training on marketing performance and business growth.
This approach can shift the focus from simply reporting course completions to demonstrating L&D’s tangible business impact. Instead of just stating how many employees took the course, this story clearly illustrates the problem, the solution, and the measurable business outcome.
Let’s summarize. Shifting from vanity metrics to meaningful training analytics requires three key steps:
- Define clear learning outcomes – Establish well-written learning objectives as part of your needs analysis.
- Identify relevant metrics – Choose metrics that directly measure the achievement of those outcomes.
- Embed smart measurement tools – Implement the right tools to capture only the necessary data.
By following these steps, learning professionals can move beyond basic completion rates and deliver valuable insights that showcase the true impact of training on skill development, real-world application and business success.
