Guest Poster Bonnie Meyers explains predictive modeling and how a data analytics strategy can allow nonprofits to predict donor behavior and campaign outcomes. Learn how your nonprofit can benefit.

To be successful, nonprofits need to collect a lot of data. You need to know who your donors are, how much they donate, and what kind of messages lead them to support your nonprofit. By analyzing your data, you can find these answers and reach important conclusions about what motivates your donors to give.

There are multiple types of data analysis that can help you better understand your donors, but one of the most useful types for nonprofits is predictive analytics, or predictive modeling. In this quick guide, we’ll cover the basics of this impactful strategy, including:

  • What is Predictive Modeling?
  • 3 Benefits of Predictive Modeling for Nonprofits
  • How to Get Started with Predictive Modeling

You don’t have to be a large organization or a data expert to leverage predictive modeling. With the right resources and support, any size nonprofit can benefit. Let’s explore the essentials so you can get started.

What is Predictive Modeling?

Meyer Partners’ nonprofit analytics guide defines predictive modeling as “a broad mathematical strategy that uses current and past data to predict future behavior, actions, or outcomes.” In a nonprofit context, this means analyzing your donor data to identify trends that help you predict how donors will act in the future. 

Predictive modeling works by creating a statistical model based on your organization’s data that helps you forecast future data. Typically, nonprofits do this using either modeling software or professional data services.  

With the information you learn from predictive analytics, your organization can segment supporters more effectively, choose the best types of outreach for your donor base, and ultimately increase your fundraising success. 

3 Benefits of Predictive Modeling for Nonprofits

Let’s take a closer look at the practical applications of predictive modeling for nonprofits like yours. With this analytical strategy, you can access these key benefits:

1. Predict giving patterns

By finding trends in your data, predictive modeling breaks down your donors’ giving behavior and gives you an idea of how they’ll act in the future. Knowing when, how often, and how much your donors are likely to give helps you tailor your fundraising appeals and marketing strategies to align with donor habits. 

For example, if you know that your donors are more likely to give larger donations in the months of November and December, you can focus on planning your Giving Tuesday and year-end giving campaigns instead of a spring fundraiser. 

Predictive modeling can also help you segment individual donors accurately based on their current giving level and future giving potential. Consider pairing predictive modeling with additional tools like a donor pyramid to visualize where individual donors stand. The more a donor gives, the higher they should be placed on the pyramid. Use the example below as a guide for creating your own:

This infographic is an example donor pyramid broken down by giving level, which your nonprofit can use in conjunction with predictive modeling to segment donors.

Once you’ve distributed donors throughout the pyramid, you can focus on moving donors up to higher levels of giving based on their likelihood to do so. Double the Donation suggests creating stewardship strategies that target specific segments of the pyramid to focus your efforts.

2. Identify potential donors to upgrade

By zeroing in on individual donors, predictive modeling can help you identify specific supporters who have the potential to give at higher levels. For instance, you can use predictive analytics to find prospective:

  • Major donors. Soliciting major gifts is vital to reaching your fundraising goals, but it can be a challenge to find donors interested in making a major donation for the first time. Based on their habits, predictive modeling can show you potential major donors among your existing support base to jumpstart your major donor prospecting efforts.
  • Legacy donors. Identify donors who may consider making a planned gift, bequest, or endowment donation in the future. Along with the size and frequency of their previous donations, factors like age and the length of time they’ve been involved with your organization can impact this decision.
  • Recurring donors. Predictive modeling may show you individual giving patterns you wouldn’t otherwise notice, like a donor who makes small or mid-level donations often but not at a regular cadence. These supporters would be great contenders for your monthly giving program.

Once you identify prospective major or legacy donors with the help of predictive modeling, don’t forget to conduct prospect research to learn more about them. Predictive analytics is helpful for pointing you in the right direction, but you need more information about a prospect’s capacity and willingness to give a major gift before you reach out to them with an appeal. In-depth research will help you confirm your decisions and develop personalized cultivation plans for each donor.

Along with showing you donors who may be ready to upgrade their support, predictive modeling can also help you find donors who are at risk of lapsing. Catching these at-risk donors early gives you time to re-engage them before they stop giving altogether.

3. Develop more effective fundraising plans

Ultimately, all of the information you glean from predictive modeling helps you create more tailored, more effective fundraising plans

When you understand your donors’ habits and giving behaviors, you can more easily determine the best fundraising strategies and communication tactics to inspire your donors to give. In addition to helping you choose which donors to try to upgrade, predictive modeling can point you toward the best timing for your fundraising appeals, ways to effectively allocate your marketing resources, and which donors to target for specific campaigns.

How to Get Started with Predictive Modeling

Before you dive into predictive modeling for your own organization, it’s essential to have organized, accurate data. Take time to audit or clean the data in your donor database first, and make sure your staff members are using good data hygiene practices across your organization. Otherwise, the information you learn from predictive modeling may not be accurate.

Once you feel confident in your data management practices, decide how you want to perform the analysis. You have two main options:

  1. Perform in-house data analysis. To handle everything in-house, you’ll need to invest in predictive modeling software and training for your team on how to use it. It’s a complex process, so you’ll likely need a full team of staff members with technical expertise to perform the analysis successfully. 
  1. Partner with a nonprofit data service. Consulting firms and marketing agencies that specialize in nonprofit data analysis can give you better results without sacrificing your team’s valuable time. That said, your success will depend on the qualifications and reliability of the agency you choose, so be sure to research your options thoroughly.

Depending on which option is a better fit for your organization, start by either forming a predictive modeling team and investing in their training or by researching potential agencies to partner with. From there, you can let the data experts lead the way.

Predictive modeling can seem complex and out of reach, but with the right tools or professionals by your side, it doesn’t have to be. When it’s done right, predictive analytics can take your understanding of your donors to new heights—paving the way for long-term fundraising success.


Bonnie Meyer

Bonnie brings to her role at Meyer Partners more than 30 years of fundraising experience, with a special emphasis in multimedia approaches to new donor acquisition and development. Her expertise encompasses several facets of direct response fundraising, including copy writing and creative direction, market research, strategic planning, and comprehensive results analysis.

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