Finding Your Point Break in 2026 — Turning Crisis Donors into Loyal Supporters
If 2025 has felt like riding a wave of giving, 2026 is poised to be the undertow. Federal funding cuts for public media lit up the public square, and we saw a flood of new supporters as communities rallied to keep trusted news and culture close to home. That boom was real — and it was emotional. But waves recede. And when they do, retention — especially of crisis-acquired donors — should quickly become a top fundraising priority for local stations.
If you’re a fundraiser, you don’t even need to see the data to know that it’s cheaper to retain or reengage an existing donor than to find a new one, yet public media still loses the majority of first-time donors within a year, even in the best of times.
The good news: as far as waves of support go, public media caught a big one over the past year. Cowabunga! The strategic news: if you want 2026 to outperform 2025 on net, you cannot rely on “business as usual.” Capturing that second gift from those new donors and the smart reactivation of newly lapsing crisis donors must be the top priorities.
The Post-Crisis Giving Problem We Can Predict (and Fix)
Crisis donors behave differently. We’ve seen it in other nonprofit sectors time and time again: Donors give for reasons that are urgent, public and deeply personal. 12 months later — when the headline fades — so do many of those donors. The remedy isn’t more volume; it’s more precision. Predict who will make that second gift and who among those that lapse are most likely to return. Then deploy your dollars and attention accordingly.
That’s exactly the role for CDP’s Predictive Insights.
What Predictive Insights Does (That RFM Can’t)
CDP teamed up with our partners at ROI Solutions and members of their MiLo Intelligence unit to launch Predictive Insights for moments just like this. Our predictive modeling tool uses station data blended with the National Reference File (NRF) — the largest repository of transactional data in public media — to continuously score each constituent on the outcomes you care about most. It outperforms basic RFM (recency, frequency and monetary value) by identifying who is most likely to respond, even expanding into “marginal” segments (like deeper lapsed files) where it’s profitable. And it helps you suppress non-performers to maximize the value of your fundraising expense dollars.
Two use cases that will matter in 2026:
1) Additional Gift Acceleration (First 3–9 Months)
The moment: Crisis-acquired donors are most responsive early — don’t wait too long to ask again. The onboarding arc should steer them to a timely second gift, when the probability curve is highest, to help increase the odds of retaining them.
How Predictive Insights helps:
Use the model to pinpoint recent first-year donors who have the highest probability of an additional gift during the member year.
Score-driven selection lets you digitally message/direct mail the right subset while suppressing nonresponders, reducing your cost-per-dollar raised.
When it comes to additional gifts, net revenue is the name of the game. Because the model helps you add “marginal names” where it pays and reject nonperforming names where it doesn’t, your additional gift digital/direct mail program scales without eroding net.
During the surge in giving, stations saw big increases in new sustainers, setting the stage for additional gift asks for these new donors. Historically, that took nerves of steel, with low response rates plaguing efforts systemwide. With Predictive Insights, sustainers get modeled and scored as well, taking the guesswork out of additional gift segmentation for this important cohort of donors.
2) 12+ Month Lapsed Recapture
The moment: When crisis donors don’t retain, it’s important to recognize they’re not all created equal. Some have real win-back probability, while others are costly distractions.
How Predictive Insights helps:
Identify lapsed 12–24 month donors with the highest potential for recapture and win them back early. Enhance your results by leveraging the model’s ability to profitably open “marginal” segments (e.g., longer lapsed donors) selectively, while suppressing unlikely responders to protect your budget.
Beyond RFM: Instead of treating every 12–24 month lapsed donor the same, the model ranks which lapsed donors should get digital messaging/direct mail appeals and which should be held out entirely, allowing you to optimize your selection criteria and solicitation strategy.
Why this works in public media:
Built for our ecosystem: Predictive Insights utilizes machine learning that is trained on public media donor behavior at scale through the NRF, not a generic nonprofit model — so it reflects pledge, Passport, sustainer norms and our local/national content mix.
Always fresh, simple to run: Scores are refreshed from data you already send, and pricing is subscription based and tiered — no volume surprises — making it easy to operationalize across fiscal years.
Proven before deployment: Our NRF back-testing framework lets you see lift and ROI in historical data, so you can size budget and volume with confidence.
In 2026, success will belong to the local public media organizations that can read the current beneath the wave, move with intention to anticipate donor behavior and steer resources where they’ll have the most impact.
Find out more about CDP’s Predictive Insights, powered by MiLo Intelligence and ROI Solutions, here.