Using Machine Learning and AI to Enhance Donor Engagement and Maximize Donor Value

Machine learning and first-party data. One’s a tool, and the other is an ingredient to really drive improved return on investment (ROI). Knowing how machine learning can be applied to drive some pretty amazing results empowers your nonprofit to improve its reach and generate more donations. It’s even more important now, during a time when many traditional targeting methods that have worked great for decades are getting a little dated.  

Understanding Your Donor’s Day 

Each day, the experiences your donors have, the decisions they face, and the opportunities that arise for them are shaped by their past behaviors. The for-profit world monitors these decisions and turns that knowledge into a curated experience.

For example, when a donor logs in to Netflix, wanting to watch a show or movie, they’re presented with choices based on what they’ve liked, finished, and stopped watching in the past. On Facebook, the targeted ads are (some would say eerily) more and more targeted to extremely nuanced interests, from the style of clothes the users wear to what they tend to buy. 

At the grocery store, the prices and what’s available in stock at your store depend on your ZIP code and buying trends. This is captured using your loyalty discount card. Retailers don’t want to give you $10 cash back on every $1,000 you spend just because they want to — they do it because they want to know what you’re buying.  

Even life-or-death decisions are now informed by very advanced mathematical algorithms. This includes serious decisions about the choice of medicine that has the best chance of curing a disease or determining the best course of treatment. The nation’s top hospitals are all data-science–oriented now.  

Bringing AI and Machine Learning into the Nonprofit Sector 

We’ve been surrounded by AI (artificial intelligence) and machine learning in the corporate world for many years. Unfortunately, most nonprofits still use segmentation strategies that date back to when donor records on cards were physically shuffled in a backroom to decide who should receive marketing or mail. 

None of the elements of AI or machine learning are cutting edge anymore for most industries, and it’s time for the nonprofit sector to catch up to gain all of the benefits of using this technology in donor engagement, outreach, and marketing efforts.  

Making Sense of Messy Data 

One of the most common concerns nonprofits bring up when considering how to implement machine learning, AI, and other data-science opportunities is the cleanliness of their data. They believe their data is a mess, and many fear that data hygiene might be a barrier to using these tools.  

The good news is that most organizations already have the data necessary to capitalize on machine learning, AI, and some other tools. Often, you don’t have to overstretch or be too intrusive. In most cases, you’re already collecting the right data in terms of donor histories and who your donors are. It’s the math and how this data is used that can be improved. 

How to Get Started with Machine Learning and Artificial Intelligence 

For many nonprofits, the idea of implementing machine learning and AI can feel overwhelming. However, getting started doesn’t have to be complicated or expensive if you consider four critical focuses. 

1. Assess Your Data Readiness

Before diving into AI, take an inventory of the data you already collect. Most nonprofits already gather valuable first-party data, including: 

• Donation history (frequency, amount, timing) 

• Event attendance and volunteer engagement 

• Communication preferences and response rates 

• Website interactions and social media engagement 

2. Define Your Goals

Identify what you want to achieve with AI and machine learning. Some common nonprofit goals include: 

• Predicting donor churn: Identifying donors who are at risk of disengaging so you can take action

• Optimizing donation requests: Determining the right amount to ask from each donor based on their giving patterns 

• Personalizing outreach: Sending tailored messages to donors based on their interests and past interactions 

• Improving donor segmentation: Automatically categorizing donors into meaningful groups for targeted campaigns 

3. Start with Simple AI Tools

You don’t need a dedicated data-science team to begin leveraging AI. Many user-friendly, no-code tools exist that integrate with common nonprofit software. A few examples include: 

• AI-powered CRM platforms such as Salesforce Nonprofit Cloud and Blackbaud’s AI-enabled solutions

• Marketing automation tools such as HubSpot and Mailchimp, which use AI to optimize email campaigns 

• Predictive analytics platforms such as Google’s AI tools and DonorSearch, which help identify high-value donors 

Even free or low-cost solutions such as Google Analytics can provide AI-driven insights into donor behavior. 

4. Test and Learn with a Pilot Project

Rather than overhauling your entire approach, start with a small-scale AI project. For example: 

• Use AI-powered email automation to personalize donor outreach. 

• Implement predictive modeling to identify lapsed donors and reengage them. 

• Leverage chatbots to handle common donor inquiries to free up staff time. 

Track performance metrics to see what’s working and iterate based on results. 

This blog is only is a start. Learn more about machine learning and AI for nonprofits in our Quick Byte webinar Your Donor Data + Machine Learning = Your Most Powerful Levers for Improved ROI.