Counts and Percentages in Nonprofit Data
Updated: Nov 9
In another post we talked about how nonprofits can use duplicated and unduplicated counts to understand their data and present it to their supporters. Nonprofits can use those different approaches to counting their work (people served, services delivered, achievements...err achieved) to get a clearer picture of what they are doing, how much they are doing, and possibly gain insight into how they can do a little better.
Now, let’s focus on counts and percentages to help us learn even more from our data.
In this post, we will:
Explain the basics of counts and percentages
Talk about the pros and cons of counts and percentages for nonprofit reporting
Give a few practical, common uses of percentages in nonprofits
Walk through an example to explain how a nonprofit can use percentages and counts to understand its programs
I admit that talking about counting (again) feels really basic, but stick with me.
Counts, or the numbers you gather, are where everyone starts. You might have an elaborate data collection process and specialized data tracking software to count your services, successes, and people served, or you might use paper files or spreadsheets to manage your data. Regardless of your data collection processes and technology, it will be built on counts.
You might need to count the number of meals, people, volunteer hours, jobs obtained, and etc to understand how much you're doing. Even if you offer ‘no-questions-asked’ services, you probably still want to count something. For example, you might count the number of meals you served in your soup kitchen, the number of units of naloxone you distributed, or the number of times people used your computer lab to access the internet in a week, month, or year.
Value of Counts
Counts are easy for you and your audience to grasp quickly, especially when they are put in context.
Last year we served 75 people, and this year we served 100 people.
Counts clearly show how much an organization provides (75 meals provided) or achieves (25 adults obtained living wage jobs).
Counts of outputs and outcomes are quite common, so are counts of the total number of people served.
If you want to compare similar organizations, then counts are an easy way to see which one is providing more of something you care about.
If your nonprofit wants to partner with a counseling organization for referrals, then you might like the fact that Partner A serves hundreds of clients per year, whereas Partner B only serves fifty.
Limitations of Counts
Counts can hide inefficiency, ineffectiveness, or unmet needs.
A program might help 25 adults avoid evictions. Nice job! But, if 75 people who asked for help were evicted, then we are missing some important data if we only look at the count of successes.
It’s easy to become too focused on producing big numbers (i.e. quantity) rather than focusing on the quality of your programming.
The vast majority of nonprofits will count outputs, and most will also count outcomes. Human service nonprofits will count individual participants with a few exceptions (see anonymous services above), while some organizations - such as environmental organizations or nonprofits that focus on community-wide issues - might not count people if that’s not critical for understanding their work.
Nonprofits can convert their count data to percentages to learn more about their work. Below are 3 examples of how your nonprofit can use percentages.
We almost always use percentages when we examine demographic data to learn about the the people served by a nonprofit or its community. You and your supporters might want to know what percentage of your participants are:
Asian, White, Black, Two or More Races, etc
College graduates, HS graduates, HS dropouts
Live in Zip Code 11111, 11112, 11113, 11114, etc
We present these values with percentages, rather than raw counts alone, because percentages are much easier to compare.
What percent of participants are female?
Number of females / Number of Participants X 100 = Percent Female
50 Females / 125 Participants X 100 = 40% of the participants are Female
2. Success Rates
Success rates are another common way to examine a program or service. A foundation funder with lots of grants can use success rates to understand the various programs in their portfolio. Or, they might compare success rates submitted on multiple grant proposals to identify programs that look more effective and programs that look less effective.
Success Rate Example
We might want to know our success rate helping the participants in our Basic Skills Training obtain jobs.
Success Rate Data
Number of Successes: 25
Number of People Who Participated in Jobs program: 100
Number of People who Completed Jobs Program: 35
Number of Successes / Number of Participants X 100 = Success Rate
In this case, we have two ways of looking at the data. We can calculate the success rate among everyone who participated in the program, and we can calculate the success rate using only those participants who completed the program.
Everyone Who Participated in Program
25 Successes / 100 Participants X 100 = 25% Success Rate
Participants Who Completed Program
25 Successes / 35 People Who Completed Program X 100 = 71% Success Rate
It might seem confusing to have two success rates for the same program, but it this approach is really useful. The different success rate calculations help us examine our programs more deeply and ask better questions, including:
How can we prevent people from dropping out so that our overall success rates improve?
How can we screen and recruit differently to find participants who are a better fit for our program?
Among those who complete the program, how can we help more participants get jobs?
This data tells me that:
Most of the people who complete the program get jobs. There's probably room to improve, but the program seems pretty successful as long as people complete the program.
More than half of the participants drop out before the program is completed, and that’s significantly reducing the overall success rate.
Knowing this, I can decide where to focus my limited resources to improve the program.
Nonprofits also need to decide which data to share externally with your supporters. Maybe you decide to share everything, or maybe you decide to share only one of the success rates. There's no right answer, but make sure that you present the success rate that you think best reflects your work. And, it's best to stick with the same metric over time so that you don't confuse your supporters.
3. Change or Growth:
Nonprofits often want to demonstrate change over time. This is particularly useful when nonprofits have lengthy programs that help participants make difficult changes. A nonprofit might offer a series focused on saving for their children’s education. Some people would start with $0 in savings and some would start with something more - maybe $100 or $500. By measuring change, you present a fair picture of how much your program helps people save, rather than focusing on their total savings at the end of the program.
Show the change in average income between before and after participants complete the Advanced Skills Training program.
Original Average Income: $25,000 (before)
New Average Income: $30,000 (after)
(New Income - Original Income ) / Original Income X 100 = Percent Change in Income
($30,000 - $25,000) / $25,000 X 100 = 20% Change in Income
Important tip on calculating percent change
Always put the starting value (aka original value) in the denominator if you want to calculate change over time.
We do this because we are calculating the percent change from the starting value, so we put the starting value in the denominator.
(New Value - Starting Value) / Starting Value X 100 = Percent Change
Value of Percentages
Great for year-to-year comparisons when the size of the program varies (e.g. compare the success rate of a program when it serves 75 people in one year and 60 people the next year)
Helpful for comparing different organizations with similar outcomes but different program designs (e.g. two eviction programs) to determine if one approach is more efficient.
Limitations of Percentages
Percentages alone don’t give you a sense of scale.
Does your employment program with a 75% success rate serve 100 people or 1,000 people? Did you help 75 people get jobs or 750 people?
Sometimes counts can be more informative to your audience.
It might be less impactful to tell your donors that your participants increased their incomes by an average 11% instead of saying participants increased their incomes by an average of $2,500.
Using Counts and Percentages With Summary Data
To consider how we might use counts and percentages in practice, Table 1 (below) presents an organization with 4 services. Table 1 summarizes the yearly results of these services.
Each service includes an appropriate outcome that it tracks. For example, the Basic Skills Training helps people get entry-level full-time jobs, while the Advanced Skills Training helps participants who are already employed get promoted. These services work with different participants with different needs.
Table 1 includes counts of successes, total participants served for each service, and success rates for each service. These values are presented together, rather than presenting only counts or only success rates, and that gives us a much better understanding of our work.
For example, the Advanced Skills Training has a very high success rate for helping participants obtain a promotion (96%), but that outcome only applies to a small number of participants (n=25) who used that service. Whereas the Basic Skills Training helped more than twice as many people gain full-time jobs (n=55), but the success rate of that program is much lower (55%).
If we only look at counts of successes or success rates, we’d form different impressions of which service is “better”. However, when we look at counts and success rates, it forces us to think more deeply about why one program serves more people but has a lower success rate. We’re also faced with the question of whether we are allocating our limited resources in the ideal manner or if we need to change how we run one or both of these services.
Table 1: Outcome Successes and Success Rates for Employment and Housing Services
Count of Successes*
Basic Skills Training
Obtain Full Time Job
Advanced Skills Training
Housing Locator Service
Obtain Stable Housing
Table 1 Notes:
*Unduplicated Count of successes
**Unduplicated Total Count of participants served
***Count of Successes / Total Participants Served X 100 = Success Rate
Use Caution When Interpreting Data
In Table 1, we can also see that our Eviction Prevention Service serves a large number of participants and has an 80% success rate. Our Housing Locator Service serves far fewer people and has a 40% success rate.
We might be tempted to say “Clearly the Eviction Prevention Service is doing better and should be where we focus our resources.”
Hold that thought. Don’t say it to your boss or to your board. Don’t write it in a report.
Glancing at these numbers cannot give us the full story about those counts and percentages. It’s possible that your community has very few units that are affordable to the people your program serves. Maybe you have very ample flexible funding to prevent evictions, but your resources for finding affordable housing is much more constrained.
Also, what are similar programs in your community or elsewhere able to achieve? Is an 80% success rate with Preventing Evictions low compared to other organizations doing the same thing (or compared to yourself in prior years). Are you helping participants find affordable housing at much higher rates than other organizations (or compared to yourself in prior years)?
Even with counts and percentages side by side, you still need to think about their context. This is especially true when you are making funding allocation decisions.
How to choose between percentages and counts
Don’t. If there is a logical percentage to be calculated, then include the percentage and the count. Providing the count and percent together is the best way to understand your services and it’s also gives your audience more useful, complete information.
Each value alone is not enough to understand their programs. Using them together gives them far more power.
This post is part of our nonprofit data bootcamp series. Check out the complete list of nonprofit data bootcamp topics with links to other published posts.
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Founder, CountBubble, LLC