One of WaterGrass’ goals has been to analyze our anonymized, aggregate data in order to learn about trends and best practices for individual fundraising.
From our analysis we’ve learned things like:
- Organizations with formal membership programs raise more per donor than organizations which don’t.
- The median donor gives to WaterGrass a little less frequently than once a year. (Good reason to ask for donations more often.)
But recently we’ve been too busy developing new features to really dig into the data, and so we’re delighted to announce that Dr. Phani Kidampi of the University of New Hampshire and his data science graduate students are interested. As their capstone program, the four students will analyze the anonymized, aggregated records of individual donations (not grants or fees) of WaterGrass groups for which we have 3+ years of records.
They’re particularly interested in seeing whether they can do “predictive modeling” – for instance, what can a volunteer’s past history tell you about the likelihood they’ll donate? Large marketers have used modeling like this for a while, and it will be fascinating to see what it yields for our nonprofits.
This is a wonderful chance for some rigorous analysis of granular data about donations and volunteering. Our data extends back an average of 7 years for our organizations, and the total data set comprises some quarter of a million donations and contributions of volunteer hours.
If you’ve got questions you think we should investigate, let us know! For instance, “How did COVID and the turmoil of 2020 affect fundraising?” That’s one we’re sure to look at.
The project will last through the second semester, and in May or June the masters students will present their results, which we’ll be sharing broadly, on this blog and elsewhere.