When Anthony Villescas became director of prospect development at the University of California at San Francisco in 2017, he found major-gift officers and analysts using many ways to assess a donor’s potential to give. “Everyone kind of has their own way of doing things, and it depends on the analyst you are sitting down with to evaluate [a donor] what the end result is going to be,” he says.
With no standardized approach to identifying donors with the greatest potential to give, UCSF major-gift fundraisers faced a bloated list of prospective donors. The team of about 50 frontline fundraisers and 10 data analysts managed a list of more than 8,000 people. Nearly half of them had not been contacted in a year. Data was available for each donor, but fundraisers couldn’t easily make use of it.
An Algorithm and a Score
Of course, we want fundraisers “to focus their energies on the ones most likely to give to us,” says Villescas. To help them do that, he created an algorithm that employs a specific set of data to calculate the potential of each donor or prospective donor. It’s an algorithm of sorts that spits out a single value, which he dubs the “Major Gift Inclination Score,” or MGI. It ranks potential donors on their propensity and capacity to make a gift of more than $100,000.
As he applied the algorithm to UCSF’s pool of prospective donors, Villescas culled low-scoring individuals from the list and divided those who remained into four tiers based on their likelihood to make a donation within the next year. The top tier includes those whose MGI score is in the top 30 percent of the pool.
By 2018, Villescas says, fundraisers’ portfolios had shrunk by 35 percent. The number of prospects in the most promising tier had increased by 20 percent, and the number of people deemed able to give $1 million or more had increased by 5 percent. Plus, fundraisers had been in touch with 89 percent of active prospects in the previous six months.
Villescas’s method also includes a fifth tier that evaluates companies and organizations, such as foundations.
How to Use This Approach
First of all, you need data. The university had been collecting data on prospects for years. UCSF tracks information such as frequency of donations, age, total lifetime contributions to other charitable causes, a WealthEngine “gift-capacity score,” institutional programs of interest, even the distance between the potential donor’s home and the UCSF campus, among other information.
Use the data you have, says Villescas, and use data that’s relevant to your organization’s goals when you create a standardized approach for your entire team.
Periodically review the list to prevent “portfolio bloat.” Within each tier are categories “keep,” “review,” and “remove.” For example, donors stay in tier one if they:
- have given at least $25,000
- have given within the previous three years
- have talked with a fundraiser in the previous six months
- have one of the following “philanthropic affinity” ratings: “most likely to give,” “likely to give,” or “inclined to give.”
All the conditions must be met for someone to stay in tier one. Prospects are moved to a lower category if they’ve not given to the university in the previous five years or if there’s been no contact with the prospect in the previous year.
Villescas also sets conditions for names to be removed from the lists altogether.
Download the methodology to see all of the conditions for each tier.
The overall approach helps major-gift fundraisers focus on individuals and organizations most likely to make a major gift.
Michael Theis writes about data and accountability for the Chronicle, conducting surveys and reporting on fundraising, giving, salaries, taxes, and more. He recently surveyed pay packages at charities and found wide disparities in base salaries and bonuses among nonprofit causes. Email Michael or follow him on Twitter .
