Machine learning is a term we’ve all seen splashed across headlines and built throughout the scripts of our favorite TV shows and movies. Across sectors, machine learning is expected to grow at an annual rate of 44% through 2022, so it’s a buzzword that won’t be disappearing anytime soon.
But what is machine learning, and why is it important?
You may know what it is in theory, but are not exactly sure what it can be used for. Or it may even be one of those concepts that you pretend you understand but still feel in the dark about. Even if you do consider yourself a machine learning expert, you likely know more about the for-profit applications of it than the impact it can make on the nonprofit sector.
However, nonprofit organizations can also leverage machine learning technology to make better decisions, streamline daily operations, and focus their fundraising efforts.
At DonorSearch, we know that a solid foundation of data is critical to fundraising success. Each day, thousands of nonprofit organizations rely on our informative database to make key decisions. Recently, we’ve seen more and more of those nonprofits turn to machine learning technology as a way to elevate their use of data and make an even bigger impact on their mission.
To help you unlock some of these benefits, we’ve created this guide that covers the ins and outs of machine learning for nonprofits. We’ll cover the following topics:
- Machine Learning: Overview and FAQ
- Benefits of Machine Learning for Nonprofits
- How to Fundraise With Machine Learning: 3 Use Cases
Machine learning is a powerful emerging trend for the nonprofit sector, but it’s still young. When surveyed, 89% of nonprofit professionals agreed that artificial intelligence and machine learning would improve their fundraising efforts, yet only 15% are using it. With this guide, you’ll be better prepared to implement a key tool at the forefront of fundraising technology and make a bigger impact on your mission. Let’s jump in.
Machine Learning: Overview and FAQ
When you hear the term “machine learning,” you may be picturing science fiction movies or a robot army set to rise up against mankind. Despite the images the term may conjure, machine learning is far from fiction. In fact, you likely rely on it far more often than you realize!
Machine learning refers to dynamic computer algorithms that are able to process and analyze large amounts of data to improve performance, accuracy, and insights over time. Machine learning algorithms work by discovering and making sense of patterns. The more data that is fed into a machine learning algorithm, the smarter it will become—hence why it’s called machine learning.
Every time you ask Siri a question or click play on a movie recommendation from Netflix, you’re reaping the rewards of machine learning. However, as we’ll explore in the next section, machine learning has applications beyond the needs of the big tech world.
Frequently Asked Questions About Machine Learning for Nonprofits
Benefits of Machine Learning for Nonprofits
While machine learning can personalize digital experiences and increase business revenue, it can also help nonprofits power social good.
Consider the following ways machine learning can benefit nonprofits and help them function more effectively:
More broadly, artificial intelligence can also help nonprofits sift and sort data much more quickly than staff members. With so many records on file (like personal information, individual donation amounts and patterns, event history, and more), it’s helpful to have a system that can process and make sense of this data quickly.
Intelligence University could use machine learning to plan for their annual Founder’s Day campaign.
Each year, Intelligence University plans an alumni giving day on the anniversary of the institution’s founding. Like similar campaigns for other higher education institutions, this giving day will help fund the general operating budget, campus renovations, unique extracurricular and educational programs, and scholarships to students in need.
As the Intelligence University development team starts preparing for this year’s event, they need to set a reasonable (but ambitious!) fundraising goal, start identifying candidates for major gifts, and draft campaign messaging.
While alumni are a valuable source of funding, it can be challenging to identify which former students have the highest propensity to make a significant contribution. In addition to major gifts, Intelligence University also wants to work towards a high participation rate in the campaign to get a more comprehensive view of engagement efforts.
With machine learning, Intelligence University can quickly process a huge volume of past student and donor data to determine which students are the most valuable prospects. When compared to typical major donor prospecting, machine learning can identify 4-5x as many qualified prospects in a much faster time frame.
With the algorithm handling this herculean task, Intelligence University’s development and alumni relations professionals will be able to focus their attention on making personal connections with individuals or creating emotionally compelling appeals.
The Intelligence County Animal Shelter could use machine learning to inform their #GivingTuesday marketing.
The Intelligence County Animal Shelter is hard at work planning their fundraising campaign for #GivingTuesday. This won’t be their first year participating in this worldwide day of giving, but they want to step it up a notch and achieve a higher fundraising goal than last year. While it’s possible for a team member to process and analyze historical campaign data on their own, the processing and reporting required would be challenging and time-consuming.
Machine learning can help the animal shelter efficiently learn from their past #GivingTuesday campaigns to increase its fundraising total.
A machine learning algorithm can help the animal shelter determine:
- Which send times led to the highest conversion rate
- Which platforms drove the most engagement (email, social media posts, direct mail etc)
- Which content elements were the most useful for encouraging clicks (images, videos, etc)
The marketing and communications team can take all of this information and use it to craft more compelling and effective appeals and posts. Additionally, machine learning can help the team create more accurate segments of donors and target each group with a personalized message.
Finally, machine learning can identify which of these segments (and which individual donors) are most likely to make a generous contribution, so the fundraising team can follow up and conduct outreach. Like in the other use cases, these predictions are based on a combination of existing donor information and prospect research data.
Intelligence Hospital could use machine learning to raise funds for a facility expansion.
Intelligence Hospital is one of many medical institutions that rely on fundraising initiatives to offer state-of-the-art equipment, improve their facilities, pay for supplies, and other important needs.
Right now, Intelligence Hospital is planning a capital campaign in order to build a new wing of the building that will be tailored to the needs of children with cancer. While there is a wide variety of potential donors that Intelligence Hospital could solicit for this healthcare fundraising campaign (such as doctors, corporate partners, or health-related foundations), one of the most common groups to pursue is grateful patients.
A former patient is likely to someday give back to a hospital if they are especially grateful for the care and treatment they received. By making a donation, a grateful patient can express their appreciation and provide support to the individual staff members and departments that were most meaningful during their care.
However, due to the intimate and delicate circumstances of medical treatment, it can be awkward to make fundraising asks to past patients. Development staff may worry about coming across as insensitive, or worse, violating HIPAA. Plus, timing is especially important, as patients should never be approached while still at the hospital but are most likely to donate soon after their discharge. While beneficial, grateful patient programs can be challenging and time-consuming. This is where machine learning can come in.
Intelligence Hospital can use a machine learning algorithm to identify potential donors from past patient records and automatically screen new patients for giving potential on an ongoing basis. This information can help gift officers conduct outreach to the most qualified prospects and secure the funding needed for the new children’s cancer wing.
As a real-life example of this in action, Futurus Group’s powerful Gratitude to Give model takes information from DonorSearch’s prospect data set and identifies potential donors based on gratitude. And because the model will continue to evolve and learn over time, the insights discovered will only become more and more accurate.
In the next few years, machine learning is likely to become more prevalent throughout fundraising and other nonprofit operations. With such clear benefits and use applications for this technology, it’s great news for the nonprofit world that machine learning is fact rather than fiction. To start making use of this forward-thinking innovation, look for a platform like DonorSearch that has the technology built in.
For more information about machine learning and other innovative fundraising technology, explore the following additional resources:
- Artificial Intelligence for Nonprofits: Fundraising’s Future. While machine learning and artificial intelligence aren’t interchangeable terms, the two technologies have similar functions and applications. Learn more about AI’s potential for nonprofits in this article.
- Major Donor Fundraising: 13 Effective Strategies for 2020. Machine learning can help your nonprofit identify and target potential major donors. This guide offers other useful strategies for major donor fundraising.
- Prospect Research: The Ultimate Guide. The benefits described in this article are only possible when machine learning algorithms can draw from a detailed data pool, like a prospect research database. Explore the basics of prospect research here.