A Data-Driven Approach to Employee Onboarding and Training

Oliver Franz | 7/8/2024

Topics: Minitab Statistical Software, Minitab Workspace, Human Resources

When building an onboarding program, there are many factors to consider. 

Ease of use is important. Replicability is also a factor to consider. But the most important factor that any human resources manager will tell you is effectiveness.  

Unfortunately, though, it can be difficult to measure the effectiveness of an onboarding program. Research clearly shows a strong relationship between effective onboarding and employee retention, but only 12% of employees "strongly agree" that their company does an excellent job of onboarding new hires. With the average cost to replace a new hire at roughly 20% of their salary, the need for effective onboarding is crucial so employees feel empowered to excel in their role.  

The difficulty in measuring onboarding effectiveness can be negated by Minitab. Minitab makes it easy (even for non-statisticians) to not only measure the immediate impact of training associated with onboarding, but also easy to measure effectiveness over time. Let’s look at a hypothetical example to see how.  


HYPOTHETICAL EXAMPLE: Employee Onboarding Procedure 

Leaders at an enterprise company wanted to gauge the impact of their employee onboarding procedure for new entry-level specialists. To measure success, they developed four competency assessments, each with 25 randomized questions out of a pool of 100. Each assessment focused on competency skills and on-the-job skills necessary for success in the role. One assessment was given before onboarding began, and the second assessment was given six months after the beginning of their employment. 

The manager administered this assessment to the past group of hires at both intervals. The manager then used a paired t-test to test to see if the current onboarding and training process led to a statistically significant change in employee competency. A paired t-test is used to compare two related groups to determine if there is a statistically significant difference between them, in this instance scores before onboarding and after onboarding were compared. Here are the results: 

As is clear, the typical pre-onboarding score was just under roughly 62% while scores ranged a bit over that number 6 months after training. With a P-Value of 0.189 (well over .05), leadership quickly realized that some adjustments needed to be made in the training program to help bring employees up to speed as the difference between the numbers was not statistically significant. This indicated that action was necessary. 

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The manager hired an onboarding specialist and consulted with their current employees to learn what was beneficial and not helpful throughout the training process. The team sat down together and used a fishbone diagram in Minitab Workspace to brainstorm possible changes that could lead to a positive impact on employee training:  

In addition to revamping the training content, the team focused on improving their delivery and making their training materials available on mobile devices so it could easily be reviewed on long commutes home. They also created ongoing quizzes to be delivered on monthly increments to uncover possible training deficiencies and to see which concepts were resonating. The final idea and implemented change was to offer a bonus for successfully completing the onboarding and training program. 

After making these several key changes, the team rolled out the new onboarding program to the next set of new hires. The question of effectiveness still loomed—the team wanted data that proved the program was effective. With the cost of turnover, they couldn’t afford to leave it to chance.  

The data (again, a paired t-test) painted a much rosier picture after the changes were made:  

Right away, the initial scores were similar to the previous group of hires—right around 62%. 

But the scores after onboarding, at six months were closer to 71%. And with a P-Value of 0.000, we can say with data-driven confidence that the team’s results were statistically significant; the team’s new onboarding structure led to a significantly higher rate of success among team members.  

We can further visualize the differences in competency with a boxplot, also available in Minitab Statistical Software: 

With the data and visuals, the team was now prepared to demonstrate effectiveness and show their data to leadership. 



Often, business decisions are made based on gut feeling or intuition. Sometimes those decisions are the right ones, but often they are not, and when they’re not, the bottom line is almost always negatively impacted.  

Minitab can give your team the confidence to say whether something is effective or ineffective, correct or incorrect, or relevant or irrelevant. And with training options and a team of experts standing by, you will be surrounded by the support needed to achieve your biggest challenges. 

Most importantly, your decisions will be grounded in data, defendable and more accurate, whether you work in IT, HR, manufacturing, healthcare or even finance.

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