Editor’s note: Due to the inclusion of actual salary data in this case study, the company involved has requested its name be kept confidential.
Considering the formula presented here on aggregating ROI and payback data, you can probably see this is good information at an individual level. But how do you generalize these kinds of data to a larger population to use it to defend your budget?
This “In Practice” case study presents an example to answer that question. The information is taken from an aerospace company and was used to defend their Information Technology Group’s training budget for FY 2000-2001 by using aggregated data:
- An authentic assessment on Microsoft Office was administered to a random group of 44 employees. Because the training in the Office curriculum was based on a similar instructional methodology used in other courses, it was possible to generalize the results to these similarly designed training programs. This technique was used to hold down the labor cost of the authentic assessments without sacrificing accuracy, as the random population was representative of the total population (n=606) trained by the company, and the Office training used a representative methodology.
- This group received an average authentic assessment score on MS Office of 90 percent and completed the assessment in 13 minutes. This yielded a real-world task-performance factor of .07 or (.90/13). We get a forecasted weekly use of 15.6 hours after training against a pre-training use of 12.8 hours: a gain of 2.8 virtual hours per week.
- For the random group of 44, an average salary was calculated to be $68,750, or an hourly rate of $35.26 ($68,750/1,950). The weekly economic benefit is $98.73 (2.8 x $35.26). To get a three-month economic benefit we simply multiply the weekly benefit by 13 weeks, or $1,284 ($98.73 x 13).
- The average cost for a course was $354, and it took the learner an average of five hours to complete, which cost the company an additional $176.30 (5 x $35.26); consequently, the total cost of the training was $530. We are now ready to aggregate our data to the total population of 606 trained in 2000-2001.
- The total economic benefit for three months for the total population of 606 is $778,104 (606 x $1,284) against a total cost of $321,180. (606 x $530.).
- We can now calculate the aggregate ROI for the total population of 606.
- Using this same data, we can also calculate the aggregate payback period.
In defense of our aerospace company’s $321,180 investment in training for 2000-2001 for 606 IT employees, its director of training was able to demonstrate an ROI of 142 percent, an economic benefit of $456,924 ($778,104 – $321,180) and a payback on the initial investment in 2.6 months.
James L’Allier, Ph.D. is chief learning officer and vice president, research and development, for NETg Thomson Learning. Jim has 29 years of product development experience in the training industry and is a pioneer in research, design, development and management of computer-based training, interactive video and multimedia projects.