Most nonprofits seems to associate a look at Return on Investment with measuring the success of a project after the fact–sort of like outcomes measurement, but with money involved. But in my experience in the corporate sphere–where I spent about five years doing technology consulting with Accenture and other firms prior to moving to the nonprofit realm–ROI is more often used as a predictive tool to look at the possible returns prior to starting a project.
Personally, I think ROI is far easier to approach in this context, and very useful for thinking through all sorts of projects. As every number (both investment and returns) is an estimate, it’s not a precise measurement of return, but rather a thought experiment to see the plausibility of actually seeing returns.
Example? Let’s say Idealware is evaluating whether it should invest in a document management system, which, in fact, it is–we’re looking at PowerPoint slide management systems, a specialized market if there ever was one. We want to think through whether it makes sense to spend $2,000 a year on the system, and an ROI analysis is really helpful in this situation.
First, we brainstorm all the possible costs associated with the system: money, staff time, hardware, etc. Then, we estimate some plausible numbers–for instance, it seems reasonable that it would take about 24 person-hours to set up the system, four hours to train the two people using it, etc.–and assign a dollar value to these hours. None of these numbers is super precise, but they’re all in the right ballpark.
Then we brainstorm all the possible ways the system might save us money. How much time would we save in creating new presentations that reuse some slides? How often do we do that? Would it save printing or other costs? Is there a quality increase, and if so, how could we plausibly quantify that–maybe by attracting two more training clients based on the quality increase?
And then we compare. It’s useful to put this all in an Excel spreadsheet so you can play with your estimates. You may find that some of the estimates you feel least confident about don’t matter much to your final outcome anyway, so you don’t need to worry about them.
On the other hand, you’ll often find that the entire ROI hings on one or two things. In our case, for example, it mostly comes down to the the number of new trainings we think we’re going to create per year. That’s not an easy number to estimate, but we can see where we would break even and decide if it’s plausible. If we start to make money back after we create five new trainings, that’s an easy decision — we would definely do that. But if the break even is at 30 new trainings, that’s a harder decision, and we need to think carefully through what to expect. If it’s at 200 trainings, forget it, there’s no way that’s a plausible investment.
There’s a lot of power in the thought process. Just taking an hour to think though a software decision in a quantitative way like this can be hugely useful, regardless of the actual outcome of your numbers.