Written by Evan Aanerud
Nonprofit fundraisers are continually searching for efficient systems to discover, approach, solicit, and measure support from prospects. The market for fundraising software is over-saturated with business management technology that is constantly evolving. An increasingly prevalent trend in startup software is the integration of artificial intelligence technology, which has become more popular today thanks to greater access to data volumes, sophisticated algorithms, and improvements in computing power. If you work in fundraising, you have most likely read about the plethora of artificial intelligence (AI) applications and how it is set to simultaneously disrupt and transform our fundraising processes. It is a dizzying and somewhat overwhelming assortment of information. There is the automation of tasks such as producing written content and emails to donors, data entry and management, and chatbots that guide prospects through gift-giving. No matter what AI system is utilized, the organization’s data is the forefront asset.
Classifying AI-Empowered Software for Nonprofit Fundraising
The expansive library of software options makes it an exciting and difficult choice to choose the most feasible system for an organization’s needs and budget. There are distinctive and unique ways that each organization manipulates AI technology. Kanter and Allison Fine categorize these services in AI4Giving: Unlocking Generosity with Artificial Intelligence: The Future of Giving, a select few listed below:
Donor Prediction Models and Automated StewardshipWorkflow: Machine learning core methods to train algorithms on donor data to identify the most likely donor prospects.
Online Fundraising Campaigns: Machine learning analytics to analyze donor databases, sometimes appended to third-party data sets or social media data.
Donor Research/Data Collaboratives: Platforms that are sharing data for research purposes with privacy protocols and ethical standards.
Reporting and Workflow Tools: Helps platforms efficiently generate reports or automate administrative tasks like customer service for donor
At the forefront of the argument for integrating AI in fundraising is that machine learning technology stands to revolutionize workforce efficiency. Gravyty is a donor marketing and outreach tool that auto-generates emails based on repetitive learning the software has performed off of the organization’s donor data. Gravyty claims its tool empowers fundraisers to do four times more personal outreach and inspires first-time donors at a scale previously unthinkable. The software capabilities align with the “Online Fundraising Campaigns” model mentioned above; the donor relationship is the focus, instead of the actual identification of the donor.
Figure 1: An example of Gravty’s email generation layout. Source: AI4Giving.
Along the lines of workforce efficiency, AI-equipped fundraising software stands to eliminate mundane administrative tasks and help direct more attention on strengthening donor relationships and strategies. Boodle.AI’s platform trains donor prediction model algorithms using third-party data, a 500-something data points across a 220-miilion-record data set. This includes demographics, lifestyle, and behavioral analytics to help the fundraiser make decisions. Similar to Gravyty, Boodle.AI seems to prioritize marketing data and consumer behavior in its training model. Their platform even offers a Predictive Advertising module, which uses a combination of data enrichment, AI modeling, and segmentation to micro-target consumers.
Figure 2: Boodle.Ai’s prediction model algorithms in use. Source: AI4Giving.
Donor Prediction Models and Automated Stewardship
You may have heard of the “spray-and-pray” fundraising approach, a term coined by Gregory Warner. It’s a strategy where the fundraiser sends out a mass of donor appeals (“spray”) and proceeds to wait (“pray”) so that enough donors respond so they can make their goal. The spray and pray approach is transactional, expensive, and ineffective. Retention rates fall and donors lapse from burnout. Yet, moving to a targeted and focused fundraising approach can be time-consuming and tedious. Combing through prospect lists and current supporters can take hours and weeks of analysis.
“This task might take an experienced fundraiser a week of tedious desk work, but the algorithm can accomplish it in minutes. The fundraiser is freed to use that time for face-to-face meetings with donors, delving deeper into their interests and needs to personalize cultivation and an ask.”
This is where another facet of AI tech is altering the fundraising workflow: donor prediction models and automated stewardship. In this capacity, machine learning is used to train algorithms on donor data to identify prospects and predict upcoming donor lapses. These providers typically integrate with existing customer relationship management databases. The technology, if truly effective with the right organization’s data, harnesses the potential to shape how we prioritize our time with donors.
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