AI in fundraising: every question about your data is just one answer away.
Daisy is our agentic AI for fundraising data. She connects all your sources in one data lake (a shared data foundation) and answers questions about donations, audiences, mailings and reports directly in your data. No Excel reports. On request she also trains models for a goal.
- All sources connected
- Questions, not reports
- Models on request
Watch Daisy at work
An example from the product, shortened. The numbers are made up, not a real run.
Your AI agent for fundraising data, audiences, mailings, reports and donor questions. Let's work through it together.
I've run the analysis. To compare channels fairly, each cohort gets the same amount of time: donors who gave recently haven't had the chance to make a second gift yet.
Cohort: donors whose very first gift was between 24 and 6 months ago. Observation window: exactly 6 months after the first gift, the same for everyone.
| Channel | Newly acquired | Median first gift | Median total value, 6 mo. |
|---|---|---|---|
| direct mail | approx. 11,600 | €28 | €29.40 |
| other | approx. 8,100 | €45 | €46.70 |
| donation form | 14 | €55 | €57.50 |
Is the difference random? No. The chi-squared test for repeat rate and the Mann-Whitney tests for value and timing each return p < 0.0001. Because donation amounts are heavily skewed, I use non-parametric tests rather than means.
Acquisition costs unified: I allocated production, postage, media and agency costs per channel to the donors actually acquired. Year 1 is observed (cohort with first gift 24 to 12 months ago). Years 3 and 5 I project from older cohorts (first gift 2018 to 2021) using a survival curve. "Net" means cumulative donation value per new donor minus acquisition cost.
| Channel | Cost per new donor | Net after 1 yr | Net after 3 yrs | Net after 5 yrs |
|---|---|---|---|---|
| direct mail | €38 | −€4 | +€23 | +€46 |
| other | €52 | +€6 | +€52 | +€89 |
What this means for the CAC question: Direct mail is cheaper per head but only pays back after about 14 months. "Other" costs more, but converts to repeat giving more often and faster, and is in the black after about 11 months. After 5 years, each euro invested returns roughly €1.20 net (direct mail) versus €1.70 (other). Year 5 is a projection with an uncertainty band of about ±15%. Anyone who only looks at first-gift acquisition cost would favour direct mail and leave money on the table over the longer horizon. That requires exactly this unified cost per channel.
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Where data analysis in fundraising often gets stuck
Every question becomes a ticket
The answer comes weeks later from the data team or the agency. By then the print run is printed.
The numbers live in many systems
Database, mailing tool, online numbers: nobody combines them, because it is manual work every time.
A model looks convincing but only guesses
With too few cases, any model returns a list that looks good and costs money.
How Daisy works
Connect sources
All your data sources come together in one data lake.
Ask questions
You ask in your own words. Daisy calculates in your data and shows steps and result.
Give a goal
When needed, Daisy spins up her own server, trains a model for your goal and tests it on data she set aside beforehand.
Put it in context
Daisy says what the result does not prove, and declines when the data is not enough.
Give her a goal
Pick a goal, and Daisy trains and tests a model. Worked example with made-up numbers.
What you get: answers you can check
- How do newly won donors develop per channel, and what are they worth after 1, 3 and 5 years?
- How big should our house mailings be in 2027? Where does the print run no longer pay off?
- Which one-time donors are most likely to become monthly donors, and which channel reaches them best?
These are example questions from the product. Daisy shows every step and tests models on held-out data before giving you a list. A simple rule of thumb serves as the comparison.
An example of an honest no: with 9 bequests in 6 years, a model would only guess. Daisy then gives you no list and suggests asking about interest in bequests in the next mailings and retraining in 12 months. This example uses invented numbers and is not a real run.
Daisy works with anonymous donor IDs. Personal data stays in your house.
Frequently asked questions
What does AI in fundraising mean?
AI evaluates your donor data and answers questions that otherwise need reports or weeks of waiting for the data team. Typical tasks are planning print runs, finding monthly donors and comparing channels.
What is an AI agent?
An agent does not just answer one question. It plans steps, calculates and carries them out. Daisy spins up her own server when needed and trains a model for your goal.
What is a data lake?
A shared data foundation where all your sources come together: donations, audiences, mailings and reports.
Does my IT have to approve anything?
No. Daisy works with anonymous donor IDs. Personal data stays in your house.
Can the AI guarantee donations?
No. Daisy shows you probabilities and limits. Whether a letter or call really triggers something is only shown by a small test with a control group.
How do I get started?
With the Tailwind Check: 2 exports, 15 minutes, result in 3 days. Then you decide whether to continue.
Which goal should Daisy tackle for you?
Pick a time. Bring a goal, 15 minutes are enough.
Discuss your goal with Daisy →