There’s a veritable yottabyte (the largest data storage unit, if you didn’t know) of information about AI out there these days. On any given day, we’re exchanging DMs with 17 different AI chatbots and wading through 2,156 different AI-related headlines. In all the hubbub, it’s hard to keep track of what’s accurate and what’s relevant and what’s, generally speaking, what. And as a nonprofit, you need to stay on top of all these tech trends, using them for the betterment of humanity (and the betterment of your mental health)! So, today, we want to zero in on a subset of AI that’s useful, accessible, and not at all scary: machine learning, also known as ML.
What is machine learning?
Machine learning is a type of computer programming that teaches computers to learn and improve from experience (just like robot puppies!). In the days of yore, you had to tell a computer exactly what to do in any and every situation. With machine learning, you can give the computer a bunch of examples and let it find patterns and determine trends on its own.
What is the difference between machine learning and artificial intelligence?
Machine learning is actually a type of artificial intelligence. AI is all about teaching machines to mimic human thoughts and behavior. That’s a big field, so there are many AI subsets, and machine learning is just one of them.
How does machine learning work?
We love nonprofiteers—always so eager to learn. Like most things AI-related, machine learning is complex, and most of the information out there is a big ol’ pile of techy jargon. So, we asked Chat GPT to explain how machine learning works … as if we were a small child. Take it away, friend!
Okay, imagine you have a little brother or sister who is learning to read. At first, they don't know any words and have to sound out each letter one by one. But the more they practice, the better they get at recognizing whole words without having to sound them out.
Machine learning is kind of like that. Instead of words, we give the computer lots of examples of something we want it to learn, like pictures of dogs and cats. The computer looks at all the examples and tries to figure out what makes a dog a dog and a cat a cat.
Just like your little brother or sister, the more examples the computer sees, the better it gets at recognizing patterns and making predictions. And just like your sibling, sometimes the computer might make mistakes, but we can help it learn from those mistakes so it gets better over time.
Types of machine learning
Machine learning may feel futuristic and cutting-edge, but it began way back in the 1940s, when Walter Pitts and Warren McCulloch presented a paper with the first attempt at mathematically modeling neural networks. We’ve come a long way since then, and today, there are three main types of machine learning:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
1. Supervised learning
Supervised learning is the most commonly used type of ML. You enter clearly-labeled historical data into the algorithm, and the ML model learns from these examples, getting increasingly accurate over time. For example, someone shows the computer pictures of goats and llamas, and each picture is labeled “goat” or “llama.” When it sees a new picture, it can determine whether it’s a goat or a llama.
2. Unsupervised learning
With unsupervised learning, the data isn’t labeled, forcing the model to look for patterns in the data on its own. For example, the computer is given a bunch of pictures of goats, llamas, sheep, and lizards, and it tries to figure out which animal doesn’t belong. (There, there, lizards, we still love you.)
3. Reinforcement learning
Finally, there’s reinforcement learning, in which the machine learning model learns by receiving positive or negative feedback. The model takes actions, then gets a yay or boo, and learns to maximize the yays over time. For example, the computer gets a bunch of photos of llamas, goats, capybaras, and pygmy marmosets, and it gets a reward every time it correctly identifies a llama. It keeps learning and getting better and better at llama recognition.
How nonprofits can use machine learning to improve fundraising
So, what are the applications of machine learning for nonprofits looking to up their fundraising game? Here are just a few.
- Finding donors
- Eliminating program failure
- Retaining donors and volunteers
- Forecasting fundraising
- Translating language
- Understanding the donor journey
- Matching volunteers with opportunities
- Learning from past campaigns
- Building Spotify playlists
Finding new donors
Machine learning can help you analyze past donor data to identify patterns in who gave, when, where, and even why. You can go broad, looking for which demographic group gave the most, or you can narrow the search, looking into behavioral data to figure out your target audience for specific campaigns.
Preventing programs from failing
With predictive analytics, machine learning can help identify red flags for your programs before they become real problems.
Retaining current donors and volunteers
In addition to identifying new donors, machine learning can help you identify donors who are likely to lapse or volunteers who have gradually decreased their involvement over time. Then, you can swoop in from above to ensure they stick around.
You can use machine learning to predict fundraising outcomes based on historical data. This enables you to set aspirational but realistic goals and allot resources accordingly.
If you have clients, volunteers, and/or donors from different backgrounds, machine learning can help you translate materials into different languages, increasing accessibility and expanding your reach.
Understanding the donor journey
Google Analytics 4 uses machine learning to help organizations understand the end-to-end journey for each web visitor. As a result, you can easily identify key touchpoints and build targeted fundraising strategies to engage and retain donors.
Matching volunteers with opportunities
ML can help you match volunteers with opportunities that align with their interests, making it more likely that they’ll keep coming back for more of that warm-and-fuzzy feeling.
Learning from past campaigns
After a big campaign, it’s tempting to take a nap before moving on to the next big campaign. Instead, use machine learning to improve campaigns in the future. Computers can analyze past campaign data and identify what worked and what didn’t, from highlighting the most effective social media channels to finding which messaging resonated with different donor groups.
Building Spotify playlists
You need a good playlist to keep up that productivity, but who wants to listen to the same tunes day in and day out? Thanks to ML, Spotify can recommend songs based on your and other users’ musical preferences.
How nonprofits can use machine learning to help clients
As you can see, machine learning is incredibly useful when it comes to optimizing fundraising efforts. At the same time, it can help you increase your impact for your clients. Here are a few ways ML can change lives for the better.
- Identifying potential clients
- Personalizing learning
- Assisting via chatbot
- Improving medical diagnoses
- Allocating resources
- Reducing waste
Identifying potential clients
No one knows what the future holds, but machine learning can help us make an educated guess. Based on historical data trends, you can identify which populations may be at an increased risk of homelessness or where a natural disaster is likely to occur.
For education nonprofits, ML can help create personalized learning experiences. For example, if you provide tutoring for at-risk teens, machine learning algorithms can identify areas where a student is struggling and provide targeted support.
Chatbots and virtual assistants
A basic chatbot is an easy way to provide around-the-clock support to your clients. You can use ML to train it to answer basic questions, schedule appointments, as well as provide resources and guidance. Furthermore, it can connect clients to humans when they need it.
Improving medical diagnoses
If you’re a healthcare nonprofit, machine learning can analyze patient data and predict potential health issues so you can intervene early and improve outcomes.
Appropriately allocating resources
As your nonprofit grows, it can be hard to know how many staff members and volunteers you need for various programs—not to mention how much funding is required. ML can analyze operations and client data to identify where you most need resources and where you can be a bit more hands-off.
Reducing food waste
Whether you run a food pantry or provide meals for those in need, machine learning can help minimize waste and improve distribution. ML can analyze food preferences, dietary restrictions, allergies, and more to ensure clean plates and happy customers.
What does machine learning look like in real life?
Well, good news, it's more seamless than you probably expect! Funraise has been implementing machine learning into our tools for years now, which should be no surprise. We are the most innovative fundraising platform in the nonprofit world, after all. Here are some of the ways that Funraise impressively infuses machine learning into your fundraising front-end and CRM.
Increase donation form conversion rates
Personalize the giving experience with ask amounts based on signals like time of day, device type, giving history, and more. Present them with a donation form speaking directly to the amounts and methods that make them feel comfortable giving. And BTW, Funraise has the best donation form conversion rate in the industry: 50% of visitors to Funraise donation forms make a donation.
Detect and reduce fraud
Funraise uses bots for good that predict potentially fraudulent charges and identify fraud bots, so your donation forms automatically block fraudsters and keep your donors safe.
Maximize fundraising efficiency
Clear the path for your biggest supporters, and remove obstacles that will lead your future major supporters right to your door. Use your donor data to in smart ways, like evaluating fundraising campaign efficacy, spreading the word about your nonprofit, and creating audiences for social and search ads.
Innovate new ways to implement AI
With machine learning advancing rapidly, Funraise is excited to implement new opportunities to improve nonprofit fundraising efficiency and performance inside our platform for you, our nonprofit friends.
How to implement machine learning
“Wow!” you exclaim. “Machine learning can really streamline our operations, optimize our fundraising, and help even more people! But … how do we do that.” Great question/statement!
Start with planning
Before you start googling YouTube machine learning courses, lay out your vision for how this type of AI can help further your mission. In a best-case scenario, what do your nonprofit, your clients, and your staff gain, precisely? Once you think through your very specific use case, then let’s talk about making it happen.
Make use of accessible programs
Machine learning used to require a data scientist or three, but these days, it’s becoming increasingly accessible—even to those with zero programming experience. By taking advantage of no-code, you can gain all the insights above while still associating Python with a big snake.
Wait, who has no-code? Funraise! Gorgeous fundraising campaign websites, amazingly effective donation forms, and easy-build reports and dashboards, and more to come.
No-code machine learning tools often use a drag-and-drop visual interface or walk users through various options and menus. If you’re ready to dive into some basic machine-learning models, try Amazon SageMaker, Apple CreateML, or DataRobot.
Bring in the human element
Of course, even if you rely on a code-free solution, you’ll need a human expert to ensure data accuracy and build strategies around the outcomes. Additionally, a little bit of coding experience can go a long way toward optimizing your algorithms and creating streamlined operations. So, if you’re totally new to the world of ML, hiring a consultant or training a staff member to get you started on the right foot … er, keyboard? … is a great way to ensure you’re getting the most out of your machine learning tools.
And there you have it: your complete introduction to the exciting world of machine learning! ML is like having a super helpful crystal ball that makes sense of the complexities around you. It’s impressive, it’s helpful, and it can improve things for the better—just like you, nonprofiteer! So, rather than being scared, buy it a (digital) latte and toast to a data-driven future!
Machine learning for nonprofits: Key takeaways
- Machine learning teaches computers to learn and improve from experience by finding patterns and trends.
- Machine learning is a subset of artificial intelligence, which teaches machines to mimic human thoughts and behavior.
- There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.
- Nonprofits can use machine learning to improve fundraising efficiency and effectiveness and improve client services and satisfaction.