No, the machines are not taking over the world, but they are improving the online donation experience.
At the heart of the Fundraise Up platform are two core technologies that set us apart from other online donation platforms — artificial intelligence (AI) and machine learning.
What's machine learning? Put simply, it's a subset of artificial intelligence that enables a system (a machine) to learn from the information that's fed into it. Importantly, this learned information is used to improve the system without manual programming.
Above: Individualization is important. Ask for too little and you'll miss out on revenue; ask for too much and you'll lose donors.
Here's why machine learning matters in fundraising. Traditionally, fundraisers have been able to use their donor databases to craft individualized ask amounts for donors that they can then include in direct mail and email solicitations. This practice is smart because it recognizes that not all donors give in the same way. For some donors, $100 might feel like giving a major gift, while for others, $1,000 is a drop in the bucket. By individualizing the ask amounts in solicitations, you meet donors where they are.
The blank space in the art of individualizing donation asks is the nonprofit website. Think about your own website's donation form — what amounts are included in the suggested donation array? It's not uncommon to see amounts like $10, $25, $50, $100 and so forth. At first, it might feel like this approach makes sense. Bills come in $10, $50, and $100 denominations, after all.
Above: A typical suggestion donation array. The amounts here are static — they aren't individualized for each website visitor.
But remember that the magic in individualizing those direct mail and email solicitations is meeting donors where they are and removing friction from the opportunities for them to give. So what does that mean for your online form?
Without individualizing the donation experience, are you creating friction and potentially losing support? Unfortunately, the answer is yes, and until recently, there wasn't a solution.
When we built the Fundraise Up platform, we did it with the objective of removing friction from the donation experience. The best way to accomplish this, we found, was by taking advantage of technology that could be taught by donor behavior — machine learning.
When someone visits your website for the first time, we start learning about them. In fact, we look at over 100 data points to help us better understand who they are and what suggested donation amounts make the most sense for them. Location, device type, donation history, and even holidays influence our algorithm's donation suggestions.
Above: Over 200 data points influence Fundraise Up's machine learning model to create individualized donation asks.
Over time, as more people visit your website and become donors, our algorithm develops an understanding of who gives what, when they give, how they give, and what devices they use to give.
Here are a few examples of the questions our machine learning algorithm is able to answer:
- In Brooklyn, what suggested donation amounts make the most sense for an iPhone user?
- On Christmas day at 3 P.M., what suggested donation amounts work best for desktop users?
- What are the best-suggested donation amounts for someone whose cell carrier is AT&T?
Above: As our machine learning algorithm learns, it's able to to suggest donation amounts based on groupings of data.
The result of our machine learning individualization? Over 50% of your website visitors convert to donors and give using a suggested donation amount. This means that you get more donations from a greater number of donors and see an overall lift in donation revenue.
Machine learning is key to helping our customers grow and it's part of what makes our platform unique, powerful, and capable of bringing individualization to the online donation space.
Curious to hear about what else is possible? Connect with our Nonprofit Success team to get a free demo and stay tuned for the next #FeatureFriday post right here on The Standing Desk.