Fundraising Analytics: 20 Critical Metrics & How to Use Them

Chris Paver –

Analytics and the idea of “data-driven strategy” have reshaped how we think about fundraising and donor engagement over the past decade. Nonprofits of all sizes now understand the value of gathering data on their donors and campaigns.

The problem is that actually putting that data to work for their mission is something that many organizations aren’t equipped to tackle effectively. A lack of expertise and time can create a gap between gathering data and then using it to drive outreach and appeal strategies.

Artificial intelligence and machine learning tools for nonprofits have significantly shrunk that gap, making it easy for nonprofits to begin fully leveraging their data to boost fundraising results. But to use these new tools to their greatest effect, you’ll need a solid foundation of data.

In this crash course, we’ll cover all the essentials of fundraising analytics, including:

Charities should carefully plan which metrics they’d like to track and how they plan to use them to avoid overwhelming their teams or losing focus.

However, once you’ve got a firm grasp on what you’re looking for in your data and strategies for acting on those insights, the sky’s the limit. Let’s get started.

Take your fundraising analytics to the next level with Dataro's machine learning software.

What are the different types of fundraising analytics?

Types and Examples of Fundraising Analytics

Fundraising metrics can be broken down into three general categories based on what they describe and how they’re used:

Fundraising analytics fall into 3 core categories - descriptive, diagnostic, and predictive.

  • Descriptive fundraising analytics describes and summarizes what you already know about your strategies and donors. This helps you get a grasp on your current performance at the individual, campaign-specific, and organization-wide levels.
  • Diagnostic fundraising analytics involves studying the performance of past campaigns and appeals to diagnose their strengths and weaknesses. This helps you pinpoint where things went well or fell short so that you can adapt as needed.
  • Predictive fundraising analytics (also called propensity modeling) involves finding trends in your historical data to make predictions about future donor behavior. This can mean simply comparing performance metrics over time to make logical inferences about how trends are likely to continue. Nonprofits are also increasingly using AI software to generate deeper, individualized predictive insights—more on this below.

There are as many different fundraising metrics as there are ways to interact with your donors, so there can be some overlap between these categories. However, even if various metrics essentially measure the same thing, they can each provide a different descriptive, diagnostic, or predictive lens through which to study those donor interactions.

Examples of Fundraising Metrics

Let’s walk through some of the most common fundraising metrics that nonprofits regularly collect and study. As you explore this list, consider how each metric might be used on its own or in tandem with others to uncover descriptive, diagnostic, and predictive insights.

  • Donation volume
    • The number of individual donations you received during a specific campaign or timeframe
  • Gift recency
    • How recently an individual made a donation
  • Gift frequency
    • How frequently an individual makes donations
  • Average gift size
    • The average value of an individual donation from a particular donor or during a specific campaign or timeframe
  • Donor lifetime value (LTV)
    • The total amount of revenue that a single donor generates for your nonprofit from the start of your relationship to the time they lapse or churn
  • Likelihood to upgrade or reactive
    • The likelihood of a donor to upgrade their recurring donation or reactivate after lapsing, determined by studying previous engagement markers leading up to upgrades or reactivations.
  • Other individual predictive metrics
    • Machine learning software like Dataro can analyze your complete database to uncover a range of predictive metrics that indicate individuals’ likelihood to give to a particular appeal, churn, reactivate, or upgrade their regular giving.
  • Demographic metrics
    • Characteristics like age and location that you can learn about individual donors
  • Wealth and affinity markers
  • Preferred giving methods
    • The way that an individual or a group of donors prefers to give, like making online donations, sending checks, etc.
  • Preferred contact methods
    • The way that an individual or group of donors prefers to communicate with your nonprofit, like via email, phone call, text message, etc.
  • Conversion rate
    • The number of times donors completed a target action (like making a donation) versus the total number of donors who were asked to complete the target action. You can track and study conversion rates by appeal, donor segment, specific marketing outlet, and/or any other relevant factors.
  • Contacted conversion rate
    • The number of donors who both responded to your outreach and took the target action you asked them to. For example, you may call 100 donors and have 50 of them pick up the phone (contact rate). Of those 50 donors, 5 of them actually made a donation once you asked (contacted conversion rate).
  • Donor acquisition cost
    • The number of new donors you acquire during a specific timeframe over the total cost to make that appeal. You can calculate donor acquisition cost by campaign, event, appeal, or engagement channel.
  • Repeat giving rate
    • The number of individual donors who give more than one gift during a specific timeframe versus one-time donors during that same time
  • Retention rate
    • The number of donors who you retain from one specific date or campaign to another versus those who drop out during that same time
  • Churn rate
    • The number of donors who lapse out of your regular giving program versus the total number of donors currently enrolled over a specific timeframe
  • Donor acquisition or growth rate
    • The number of new donors you’ve acquired over the total number of active donors in your base during a specific timeframe. An unsustainable ratio of acquisition to retention can signal strategic problems that should be addressed.
  • Cost-per-dollar-raised
    • The total expenses of a campaign, event, or appeal over the revenue that it generated. This metric is useful for determining the overall effectiveness or impact of your strategies.
  • Return on investment (ROI)

These metrics can all be used in a variety of descriptive, diagnostic, and predictive contexts. And as you generate more engagement data, you’ll be able to make more comparisons and draw more insights at different scales.

For instance, descriptive metrics about an individual’s gift amounts, recency, and frequency are useful for understanding that donor’s engagement with your organization. As you collect this data for all of your donors, some bigger pictures can emerge across your entire donor base.

RFM (recency, frequency, monetary value) segmentation involves comparing these individual-level metrics to sort donors into discrete groups or segments—like donors who gave recently, tend to give twice a year, and give between $50 to $100 each time. This is a foundational analytics strategy for nonprofits because it helps you focus your appeals with data-backed assumptions for each segment. This will ideally save you time and money while boosting the overall effectiveness of your campaigns.

However, RFM segmentation does have its limitations, especially when it comes to predictive analytics. That’s where machine learning comes in. Jump ahead to learn about the future of fundraising analytics.

How can machine learning take your fundraising analytics to the next level?

How do you systematically collect fundraising metrics to analyze?

How to Collect Fundraising Metrics

You can collect fundraising metrics any time donors engage with your nonprofit’s fundraising strategies. Tracking these interactions lets you study how effectively your fundraising and outreach tactics perform, but you have to have a solid data infrastructure in place first.

Follow these best practices to build a reliable data pipeline for your nonprofit:

1. Use a dedicated CRM platform or database.

This solution will be the central depository for your fundraising data and should anchor your entire tech stack.

2. Choose fundraising software that provides performance data.

Your organization is likely already working with a range of online fundraising tools, but double-check that all of them are providing you with the data you need.
Reports on all donor interactions should be easily accessible, ideally flowing directly to your CRM. Of course, non-digital forms of engagement like direct mail can (and should) be tracked, too, but they’ll require a bit more manual input into your database.

3. Integrate your software with your CRM whenever possible.

Integrations between your CRM and your fundraising software are essential. These connections allow your tools to automatically report new gifts to your database—no manual entry required.

Customizable CRMs like Salesforce and Blackbaud platforms have become top choices for exactly this reason. With a full ecosystem of nonprofit Salesforce apps and Blackbaud partners, you can create a highly connected toolkit that actively contributes to your analytics efforts.

4. Follow standardized data entry protocols.

You’ll need to ensure that the data flowing into your CRM is being stored and reported exactly how you want it.

Start by configuring incoming data to be tagged with appropriate features, like donor name, date, amount, and the particular campaign or source of that donation. All data should also follow the same entry protocols to ensure nothing falls through the cracks.

5. Configure your CRM to provide customized data reports.

Next, set up custom reports to pull data that matches a set of specific features. For instance, you could pull reports of all donations made to a particular campaign or all donors who gave an average amount in the past year.

6. Use machine learning software to analyze your data and run predictive analytics.

To top off your data infrastructure and generate as much value as possible, use integrated AI and machine learning tools to handle the predictions.

Predictive analytics can be extremely complicated and is difficult to get just right. AI technology simplifies the process by generating lists of ideal donors for appeals or who may be at risk of lapsing, amongst other things.

Dataro’s solution uses AI to assign each of your donors a propensity score and rank (showing you how likely each individual is to take a particular action). With these insights reported straight to your CRM, you can then easily sort donors by their propensity markers to generate a targeted campaign list—no need for complex filters and confusing reports.

How do you use nonprofit analytics on an ongoing basis?

How to Use Fundraising Metrics

How do you use and maintain your analytics toolkit on an ongoing basis? We have a few tips:

Use these steps to build fundraising analytics into your everyday approach to fundraising.

  • Have a concrete process in place for collecting the metrics you need.
    • In order to continually learn from your fundraising metrics, you have to actively collect data. As mentioned above, software integrations and standardized data entry protocols are essential today. Always remember to keep data collection in mind for any new fundraising or outreach initiative you launch. You put in the hard work to plan the campaign, so don’t let your valuable insights fall by the wayside!
  • Regularly review your performance data.
    • Always take some time during and after your campaigns to study the results, but don’t stop there. Your data needs to be actively maintained. Data hygiene ensures the long-term value and usefulness of your nonprofit’s data, and it should be built into your recurring activities. Many organizations schedule regular data reviews to check for technical issues and data entry mistakes. If you use integrated AI tools like Dataro, you’ll receive new rounds of predictive insights on a weekly basis.
  • Actively use your analytics when planning campaigns and appeals.
    • Whenever you plan a new appeal or campaign, start by digging into your data. This will help guide your strategies based on what’s been proven to work in the past or what needs extra attention. For instance:
      • If you’re planning a new email appeal, start by studying how your last email appeals performed. If you notice low conversion rates, you can drill down to see where the issue may have occurred. You may be targeting the wrong audience or using donor segments that are too broad, in which case it’d be a good idea to review your segmentation strategy or use AI to generate a more targeted list of recipients.
      • If you’re looking to boost retention in your regular giving program, start by using your historical data to understand who is lapsing and when. Again, machine learning software can take the guesswork out of this process by studying all of the engagement patterns that lead to churn and proactively flagging individuals at risk.
  • In future campaigns and appeals, set goals tied to specific fundraising KPIs.
    • Bake analytics into your fundraising process by proactively tying future fundraising goals to specific metrics. This gives you a concrete frame of reference for judging your success and pinpointing issues.

Remember that the value of your data compounds over time as your database grows. As long as you’re shaping it and maintaining it in ways that are truly valuable for your nonprofit’s goals, you’re on the right track.

Let's start with the basics of analytics for nonprofits.

Fundraising Analytics FAQ

If your nonprofit is starting its data strategy from square one (or just looking to make a refresh), review the fundamentals. Here are a few frequently asked questions about fundraising analytics:

What is fundraising analytics for nonprofits?

Fundraising analytics for nonprofits is the process of studying your fundraising data to find trends and measure your performance.
How much did your last campaign raise? How often do donors give to your cause? How effective are your different types of fundraising appeals? Where are you doing well, and where are your strategies falling flat? Fundraising analytics helps you answer these questions in ways that can be concretely acted upon.

What are fundraising metrics or KPIs?

Fundraising metrics or key performance indicators (KPIs) are the discrete types or pieces of data that you use to measure your fundraising performance through analytics. There are tons of different metrics or KPIs that you can use to study your fundraising strategies, like the ones listed above.

Why is fundraising analytics valuable for nonprofits?

Good fundraising analytics will allow you to:

  • Measure the effectiveness of your fundraising tactics in quantifiable terms
  • Pinpoint the strengths and weaknesses of your strategies
  • Develop new strategies rooted in your actual performance, not just qualitative insights or vague hunches
  • Better communicate your effectiveness and impact to leadership, stakeholders, and donors
  • Continually refine your appeal and outreach strategies based on performance

Simply put, analytics is what enables nonprofits to create “data-driven strategies”. Machine learning tools can boost these benefits even further, giving you AI-driven analytic insights down to the individual level.

What tools can nonprofits use to generate fundraising analytics?

You can generate fundraising analytics and metrics any time donors engage with your nonprofit’s fundraising strategies. You’ll just need a way to measure that engagement and then store it in your constituent relationship management system (CRM).

Wrapping up our discussion on fundraising analytics for nonprofits.

Wrapping Up: The Future of Fundraising Analytics

The future of fundraising analytics will require tools that take the guesswork out of the process and replace outdated segmentation strategies.

Although it’s useful as a foundational tactic, traditional analytics strategies like RFM segmentation can be limiting, particularly when it comes to predictive analytics. The process of studying all of the possible data to make accurate predictions is extremely complex, if not impossible, to handle on your own. After all, so many factors go into a donor’s ultimate decision to give (or not give)—simply grouping donors based on top-level RFM markers will still leave you open to surprises.

The bottomline is that traditional segmentation and prediction strategies are inefficient, time-consuming, and often inaccurate.

That’s where machine learning comes in. AI and machine learning tools can make analytics predictions that would be incredibly complicated to generate otherwise. It studies your data to find trends and make predictions down to the individual level—no more clustering donors into vague RFM segments.

Here’s how the process works with Dataro’s machine learning tools:

  1. The machine learning software analyzes your fundraising data, splitting it into hundreds of discrete “features” like gift recency and donor age.
  2. The artificial intelligence then combs through these features to find trends, patterns, and relationships. It uses these insights to begin training algorithmic models that can make predictions for the specific propensity you’re looking for, like churn likelihood.
  3. The software evaluates its trained models to find the one that performs the most accurately based on all the complex relationships in your historical data.
  4. This model is then used to generate propensity scores and ranks for each individual donor, showing you how likely they are to behave a certain way and how they compare against the rest of the donors in your database.
  5. These scores and ranks are exported back to your CRM for easy use as analytic metrics in your next campaign or appeal, giving you a real-time, AI-driven list of the ideal donors who should be contacted.

Machine learning can make accurate predictions and completely replace the time-consuming process of studying your data to develop targeted campaign lists. This helps you raise more money for your mission without draining your team’s time and attention along the way.

To learn more about the world of fundraising analytics and machine learning, keep exploring with these additional resources:

AI can revolutionize your approach to nonprofit analytics. Learn more with Dataro.