Machine learning is changing how we build and manage major gifts portfolios, especially for bloated portfolios where gift officers struggle to build relationships with all of the prospects. Unlike traditional methods that rely on simple linear models, machine learning algorithms can identify clusters of donors who would not be identified by a simple scoring system.
Traditional Linear Models
In the early 2000s, fundraising organizations primarily used linear statistics to model donors, which assumed straightforward relationships – like donors becoming more likely to give as they age or advance in wealth ratings. While these models helped with low-hanging fruit, they often missed opportunities by overlooking less conventional donor profiles.
As one example, traditional linear models suggested that more donor contact always led to better results – sometimes recommending as many as 18 contacts per year. However, this approach often led to donor fatigue, with solicitation materials ending up in the recycling bin before they were even opened.
The Power of Machine Learning
Machine learning offers several key advantages:
- It can analyze large amounts of data to identify patterns humans might miss
- It adapts and learns from new information over time
- It can handle complex, non-linear relationships among donor characteristics
- It helps eliminate unconscious biases in prospect identification
Real-World Impact
Consider this example: In a typical constituency, about 2.5% of people might give at the major gift level ($100,000 or more). However, when we use machine learning to identify prospects based on specific characteristics – such as maintaining an average annual giving of $307 or having multiple relationship connections – the likelihood of major gift giving can increase to 27%. That’s a tenfold improvement in prospect identification!
Portfolio Management and Machine Learning
Machine learning is particularly valuable for portfolio management. With most gift officers managing between 75-150 prospects, it’s essential to focus on the most promising opportunities. Machine learning helps identify these prospects more accurately, allowing development teams to be more strategic in their outreach.
For example, machine learning can reveal that prospects who make first gifts of $25,000 or more typically do so within their first five years of engagement, particularly after two or more cultivation touches. Interestingly, the method of contact (phone, email, or in-person) may matter less than the fact of consistent engagement.
The Human Element
While machine learning provides powerful insights, it doesn’t replace the art of fundraising. Instead, it empowers gift officers to work smarter and more effectively by both pinpointing their best prospects and by suggesting good strategies. The best results come from combining data-driven insights with personal relationship building and professional judgment.
Success stories from the field show that gift officers who embrace these tools and insights often achieve better results. However, it’s important to remember that machine learning is just one tool in the fundraising toolkit – albeit a powerful one that’s helping to reshape how we approach major gifts development.
Ready to Learn More?
Be sure to watch our machine learning Water Cooler event replay below.
If your analytics team is curious about getting started in machine learning, check out this course: https://staupell-analytics-group-online-workshops.teachable.com/p/machine-learning-basics
Or write to me if you have thoughts or questions.