Training plays an important role in a typical business. Many companies offer employee learning such as on-the-job training and leadership development. Others offer extended enterprise learning, providing education and training to customers, partners and vendors as well as to internal stakeholders. In recent years, the training industry has become more outcome driven and less reactive, shifting away from the check-the-box approach of yesteryear. Yet, there is still much to consider in proving the return on investment (ROI) of these programs.
Measuring ROI for smaller training programs may be easier than measuring ROI for larger programs. The process is typically manual and involves directly assessing the impact of training on the employees’ behavior and performance. Consider a program aimed to improve a new manager’s management skills. To measure the ROI of such a program, one would calculate the value of improving these skills or the cost of not improving them.
For example, perhaps a business learned that poor management skills are a top cause for employee turnover, which stands at 18%. Six months after they implemented the new training, they see that employee turnover has gone down to 13%, and poor management skills are no longer cited as a top reason. Assuming there weren’t multiple programs targeting this one issue over the same period, they can attribute the decrease to the training.
Thus, the ROI calculation would look at the value of retained employees (which could include the cost of backfilling, lost production time, time to onboard a new employee, etc.) minus the cost of developing and delivering the training program.
Training ROI = ((value derived from training – cost of delivering training) / cost of delivering training)) x 100
Extra Challenges When Training Is Delivered at Scale
ROI calculation is not as complex for small, localized training programs because the variables, influential factors and data are generally contained and known. The same can’t be said for scaled training programs that may be offered to thousands of customers, partners and employees over a rolling period. So how is this methodology used to determine training ROI at this scale?
The process is similar with additional considerations. Like local employee training, large-scale initiatives should also target a specific business problem or opportunity. Just as one wouldn’t invest in a business that had no clear plans or goals, one wouldn’t want to invest in a training program that wasn’t developed for a purpose. So, the program should target larger business issues such as customer churn, product adoption, time-to-value, number of qualified leads and so on.
In small and large programs, there is both a cost to developing, delivering and maintaining these training programs as well as a benefit to addressing the business problem. So far, this is all the same steps discussed above, but here’s where it starts to get complicated. One challenge to measuring ROI for large-scale programs is that training teams lack visibility and control over other influential factors. Take the example of customer onboarding that is targeting the business problem of low product adoption. The team may think to show ROI by drawing a correlation between the customers that participated in the training and the final metric of product adoption. While this is a step in the right direction, it fails to consider that other measures are likely in place for improving product adoption, and it doesn’t really paint the full picture.
How can this be addressed? Performing a regression analysis to identify the influence of multiple factors is certainly one way, but many teams don’t have data analysts. Another option involves adding a layer to our measurement (which isn’t a bad idea for smaller programs either). This added layer shows that the outcomes of the training were indeed met and that those outcomes led to the expected behaviors that ultimately impacted that final business metric.
The measurement path can look something like this, for example:
Customer participated in training (engagement) > Customer met objectives of training (assessment) > Customer took action as a result of training (behavior) > Product adoption metric increases (results).
Why More Metrics Matter
How does this extra measurement help define ROI? The purpose for measuring ROI is to make data-driven decisions around future investments. Businesses want to know what value a program can drive and then consider whether that value is worth the investment. This is why it’s important to get an accurate understanding of the training’s actual and possible impact, rather than simply correlating participation to a broad, lagging metric.
Consider this example:
Say that 50 out of 100 customers meet objectives of a training, and those 50 are 35% more likely to take an action than those that didn’t meet the objectives. That 35% increase is the actual benefit of the training. This measurement also shows a clear opportunity to enable more customers to meet the objectives.
Another challenge when measuring training ROI at scale is the availability of product and customer data. Training teams typically have access to all metrics related to the engagement and efficacy of their learning material, but there needs to be close collaboration and partnership with both product and commercial teams to connect the data points between learning and the business. Having a measurement framework in place, where it is clear what data points are needed and why, can help teams unite around a common vision.
Of course, this entire process requires a data-driven culture that is open to capturing, analyzing and learning from data. Importantly, it’s quite possible that a training program won’t immediately yield a positive ROI. But measuring all levels from engagement through change in behavior can provide the foundation to target training programs toward real business results.
