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  • Ryan Brooks

Year-Over-Year Comparisons for Nonprofits

Updated: Jan 29

How to Use Year-Over-Year Comparisons to Track Nonprofit Performance

Our nonprofit can use year-over-year comparisons to gain insight into our programs, services, and participants. They allow us to see change over time. They also give us an opportunity to celebrate our successes and reflect on ways to improve.

In this post we discuss:

  1. Some benefits and challenges of year-over-year comparisons.

  2. Different types of year-over-year comparisons with examples.

The Benefits of Using Comparisons Over Time.

1. Shows change over time

The most obvious benefit of doing year-over-year comparisons is showing change over time. Ideally, our nonprofit is growing or improving, and the data can clearly demonstrate it. Perhaps we are serving more families or helping people get jobs at a higher rate.

Table 1: Pantry Meals Served 2018 - 2022


Number of Meals

Number Change

Percent Change





















Table 1 (above) makes it easy to see the change in pantry meals served over time. It presents total meals, the change in the number of meals, and the percent change. In general, we see that the total number of meals increased every year, but the number and percent of increase varied.

TIP: It's helpful to include the following in year-over-year tables: (1) total number of units (outputs or outcomes) each time period, (2) the number change (i.e. the difference between the current year and the prior year) , and (3) the percent change.

TIP: The headings in the 3rd and 4th columns say "Change" not "Increase". Data can go up and down, so the "Change" heading is more flexible than an "Increase" heading.

2. Provides context for your results

People need context, or a frame of reference, to help us make sense of the world. Year-over-year comparisons give us helpful context. This is certainly true when a nonprofit that people donate to once or twice a year starts throwing out numbers about how much good work it's doing.

We use comparisons to other things to create context to understand our data. Some examples of helpful context include:

  • We served 250,000 meals last year, making us the second largest food pantry in Central Ohio.

  • Today's unemployment rate of 3.2% is the lowest that we’ve experienced in 2 decades.

  • 1 out of 3 children live in poverty in our community compared to 1 out of 4 statewide.

In each of these examples, the statements include a piece of data (e.g. 3.2% unemployment rate) and a bit of context that helps us make sense of the data (e.g. the lowest we've experienced in 2 decades). Without that context, it would be hard to know if 3.2% unemployment is low, average, or high.

Looking back at Table 1(above), this nonprofit's annual report could simply say "Our program served 2,050 meals last year." It would be useful information, but it's missing context.

Or, the annual report could say: "Last year was our busiest year ever. We served 14% more meals than 2021, and more than double the number of meals we served in 2018." This additional context helps the reader know that 2,050 meals isn't just some number. It represents something significant.

TIP: When you present year-over-year data in a table or chart, you should explain what the data means. Provide 1 or 2 key points that you want the reader to take away from it. That helps your readers learn what you think is important, and it allows them to keep reading without getting bogged down in data.

3. Helps you plan for the future

Predicting the future is hard, but examining our data over time can help us make a more educated guess about what’s coming next.

If we examine the results in Table 1 (above), then we can see that the pantry program is growing annually, and we can reasonably expect to serve even more meals in 2023. The exact increase that we might project is based on a lots of factors, but the year-over-year comparisons give us a great starting place.

Challenges of Examining and Presenting Comparisons Over Time

1. Anomalies, Blips, Unusual Years

Sometimes we have an unusual year. COVID-19 was a huge anomaly that had major impacts on nonprofits and the communities they serve. Many nonprofits saw demand for services skyrocket. Some organizations received more funding from the government, while many volunteers were probably reluctant to offer in-person support.

At some point, things might return to normal - or something closer to what we remember anyway - and we have this unusual year sitting in the middle of our long term data.

Look at Table 1 (above). In 2020, there was a large increase in both the number of meals served and the percent increase in meals served. In 2021, the number and percent both increased, but the change was much smaller.

We have to make sense of that unusual year and incorporate it into their understanding of program demands and trends. Was that year, in fact, unusual, or does the organization face a new normal - with higher demand for services or different community needs?

2. Inconsistent Data

To do good year-over-year comparisons, our data must stay consistent. For example, we might change the Likert scale question about food in our conference feedback survey:

Conference Food Question (Old Version)

I liked the food at the conference :

1. Strongly Disagree

2. Disagree

3 Neither Agree Nor Disagree

4. Agree

5. Strongly Agree

Conference Food Question (New Version)

The food at the conference accommodated my dietary restrictions:

1. Strongly Agree

2. Agree

3. Disagree

4. Strongly Disagree

These are totally different questions. I could easily answer "Strongly Agree" to the New Version question and still dislike the food for other reasons. I wouldn't want to compare New Version results with data from the Old Version because I would have no way of interpreting them.

Also, the response options are different. The Old Version version of the question has five (5) choices ranging from Strongly Disagree=1 to Strongly Agree =5. The New Version only has four (4) choices ranging from Strongly Agree=1 to Strongly Disagree =4. Even if we pretended these versions of the question measure the same underlying concept, we'd have to figure out how to make the data consistent.

In sum, you need to make sure you have consistent data over time if you want to perform year-over year comparisons.

3. Cherry-Picking Favorable Data

Most of us want to make our organization or program look good. We want to feel like we are personally successful, and we want to attract future donors and grants. Year-over-year comparisons can help us demonstrate our success and increase transparency when used appropriately, or they can be used to hide trends that we don't think are favorable.

This challenge is particularly relevant when we are doing longer-term comparisons (i.e. comparing current year data with data from 2 or more years ago). Longer-term comparisons allow us to choose our starting year. For example, if the data from 2018 makes our program look stagnant, then maybe we could use 2019 or 2020 as our starting year. Our supervisor, board, or donors, probably wouldn't notice. But, the choice of starting year could be the difference between depicting our program as thriving vs. struggling. (more on this challenge below)

Approaches to Year-Over-Year Comparison

There are several ways your nonprofit can do basic year-over-year comparisons. Some of the most common are:

  1. Full Year Comparisons (2021 vs 2022)

  2. Same-time Last Year Comparisons (Jan - Mar 2022 vs Jan - Mar 2023)

  3. Multi-Year Comparisons (2019 vs 2020 vs 2021 vs 2022)

  4. Longer-Term Comparisons (2019 vs 2022)

A Note On Calculating Percent Change

Let's do a quick review on how to calculate the percent change. Skip to the next section if you already know it.

Percent Change = (Later Year - Earlier Year ) / Earlier Year X 100

Steps to Calculate Percent Change:

1. Subtract the earlier year from the later year.

2. Divide the result from step 1 by the earlier year.

3. Multiply by 100 to get a percentage.

We divide by the earlier year in step 2 because we want to know the percent change in relation to the earlier year.

2021 = 150 Meals Served

2022 = 175 Meals Served

Percent Change = (2022 Meals - 2021 Meals) / 2021 Meals X 100

(175 Meals - 150 Meals ) / 150 X 100

Percent Change = 16.7%

Full Year Comparisons (2021 vs 2022)

Table 2 (below) shows a simple comparison of the number of families obtaining affordable housing in calendar years 2021 and 2022. We can see that the organization placed 10 fewer families in 2022 (n=90) compared to 2021 (n=100). We can also see that this represented a 10% decrease in the number of families that obtained affordable housing.

Table 2: Number and Change of Families that Obtained Affordable Housing 2020 and 2021.


Number Obtaining Affordable Housing

Number Change

Percent Change









Same Time Last Year Comparisons (Jan - Mar 2022 vs Jan - Mar 2023)

The full-year comparisons in Table 2 are great, but maybe we don't want to wait that long to see how things are going. We can use a "same time last year" comparison to begin to understand trends without having to wait a full year.

Table 3: Number and Change of Families that Obtained Affordable Housing Jan - Mar 2022 vs Jan - Mar 2023


Number Obtaining Affordable Housing

Number Change

Percent Change

Jan - Mar 2022


Jan - Mar 2023




Looking at Table 3 (above) we can see that in the first 3 months of 2023, the organization helped 3 more families obtain affordable housing, which represents a 12% increase. This simple "same time last year" comparison allows us to understand our program and make any needed adjustments based on the most current data available.

TIP: Be explicit about the time periods you're comparing. In Table 3, we state the months and years we are comparing. This is also important when you are comparing data from fiscal years (or grant periods) that do not follow the calendar year. For example, you might note that that the 2022 data are from July 1, 2021 - June 30 2022. This information can go in a footnote below the table if needed.

3) Multi-Year Comparisons (2019 vs. 2020 vs. 2021 vs. 2022)

Multi-Year comparisons are a great way to get a longer term view of our work. We can detect longer term trends, if any exist, and this approach is highly transparent.

Table 4: Number and Change of Families that Obtained Affordable Housing 2019 - 2022


Number Obtaining Affordable Housing

Number Change

Percent Change

















Table 4 (above) presents four (4) years of data about the number of families that obtained affordable housing each year. This data shows that there was a large 31.3% increase in the number of families that obtained affordable housing. That is followed by two years of declines in 2021 (-4.8%) and 2022 (-10%).

There are no obvious trends in the number of families that obtained affordable housing in Table 4. We see both decreases and increases, and the percent changes jump around. For example, we don't see a steady 3% - 5% annual growth in successes.

4) Longer-Term Comparisons (2019 vs 2022)

Look at Table 4 (above) once again. The high number of families that obtained affordable housing in 2020 might be considered an unusual year. To even out the effect of that "unusual year", we can look at a longer time period. We might compare 2019 to 2022.

From Table 4, we see that 90 families obtained affordable housing in 2022 and 80 families did the same in 2019. That means ten (10 )more families obtained affordable housing in 2022 compared to 2019, which is a 12.5% increase between 2019 and 2022.

Over a longer period (2019 to 2022), the number of successes grew a total of 12.5 percent. We could divide 2.5% by the number of years after our starting year (12.5% / 3 years) and say that our program grew an average of 4.2% per year.

Overall, it's looking pretty good. Slow and steady wins the race, right?

Well, maybe???

Double-check your reasoning...and your motives

Remember what we said about cherry-picking. In this example, we must ask ourselves whether it's reasonable to use 2019 as a starting year. Were 2020 and 2021 really "unusual years", or was 2022 the anomaly?

What if the nonprofit served 105 families in 2018? Should our starting point be 2018? Should we always look back 3 years or 5 years and not worry about anything else? Four years seems like of an odd length of time.

This looks a little suspicious, like we are trying to spin a good story.

The data in Table 4 (above) is transparent about the nonprofit's performance over time. We can make the long-term comparison (2019 vs 2022), but we need to justify to ourselves and others why that's reasonable. If we have the data, let's include more years in Table 4, and then explain why we chose our starting year.


Year-over-year comparisons are a powerful and relatively easy way to use your data to learn more about your nonprofits' programs. Remember to keep your data consistent over time, or make that a priority right now, so that you get the most of the data you collect.

Learn More About Nonprofit Data Management

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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