How to Forecast Grant Revenue
Every grant writer eventually gets asked the question. Maybe it's a board member, maybe it's an executive director, maybe it's a client three weeks into a contract: "How much do you think we'll actually raise?"
And it's a fair question. The organization is trying to build a budget, and your list of grant prospects is a big part of that budget. But a list of prospects isn't a forecast. A forecast is a number — a defensible, "here's my reasoning" number — and getting from a list to a number is a skill.
The good news is that the method for doing it is refreshingly simple. I've been using it for more than 25 years, I teach it in my course as the final skill students walk away with, and I'm going to teach it to you right now.
Let's be honest about what happens without a forecast. The organization either treats every prospect on the list as guaranteed money (dangerously optimistic) or throws up its hands and plans nothing (dangerously vague). Neither one helps anybody.
A real forecast does three things.
First, it sets honest expectations. When you can tell a board "based on our current pipeline, I'd project somewhere around $85,000 in grant revenue this year," you've given them something to build a budget on — and you've protected yourself from the "but you said we'd get the big one!" conversation later.
Second, it tells you where to spend your time. A forecast forces you to look at each prospect and ask, "How likely is this, really?" That's an incredibly useful exercise. It surfaces the long shots you've been over-invested in and the strong fits you've been neglecting.
Third — and this is the one people forget — it makes you look like a professional. Anyone can hand over a list. A grant writer who hands over a reasoned revenue projection is operating at a different level. It signals that you understand the funding landscape well enough to put odds on it.
So how do you actually do it? Like this.
Here's the whole thing in one sentence: take your list of grant prospects, assign each one a percentage likelihood of getting funded, multiply that percentage by the requested amount, and add up the column.
That's it. That total is your forecast.
Let me show you with two prospects.
Say you're pursuing a $10,000 grant from a funder you're lukewarm about. You've done your research, and honestly, your project is only a so-so fit for their priorities. You assign it a 10% likelihood. Ten percent of $10,000 is $1,000.
Now say you're pursuing a $20,000 grant from a funder who has supported this organization twice before, whose priorities line up beautifully with the project, and who has practically told you to apply. You assign it a 90% likelihood. Ninety percent of $20,000 is $18,000.
Add those two together and your forecast for these two prospects is $19,000.
Now, here's the part that trips people up, so read it twice: that $19,000 is not what you expect to receive from those two specific funders. You're not going to get exactly $1,000 from the first funder — you'll either get the full $10,000 or nothing at all. The percentage isn't a prediction about any single grant. It's a prediction about the whole pile.
When you run this math down a list of fifteen, twenty, thirty prospects, the individual over- and under-estimates wash out against each other, and the total lands remarkably close to reality. Some grants you gave 90% will fall through. Some you gave 10% will surprise you. Across a whole portfolio, it evens out. (If you want the fancy name for why this works, it's the law of large numbers — the more prospects you're forecasting across, the more reliable that summed total becomes. Which also means: if your entire "pipeline" is three grants, treat the forecast as a very rough guess, not gospel.)
In the finance world, this method has a proper name — it's called a probability-weighted forecast, and the total you calculate is the expected value of your pipeline. It's the same technique sales teams use to forecast deals and the same one nonprofit finance experts recommend for building a realistic revenue budget rather than an optimistic one. You're in good company.
A quick visual of the math:
| Prospect | Request | Likelihood | Weighted Value |
|---|---|---|---|
| Funder A (so-so fit) | $10,000 | 10% | $1,000 |
| Funder B (strong fit, past funder) | $20,000 | 90% | $18,000 |
| Funder C (new, decent fit) | $15,000 | 40% | $6,000 |
| Funder D (invited to apply) | $25,000 | 80% | $20,000 |
| Pipeline Forecast | $45,000 |
Your whole forecast lives in one extra column. Multiply the request by the likelihood, then sum the column.
The multiplication is easy. Anyone can multiply. The skill — the thing that separates a meaningful forecast from a wild guess — is assigning that percentage honestly.
So where does the number come from? Not your gut, and definitely not your hopes. It comes from research. Here's what I'm weighing when I put a percentage next to a prospect:
How closely does the project fit the funder's priorities? This is the biggest factor. A funder that explicitly funds what you do, in the community you serve, is a fundamentally different bet than one where you're stretching to make the connection. Fit beats odds every time — a well-aligned funder with a low overall award rate is a better prospect than a poorly-aligned one with a generous one.
What does their giving history tell you? Who have they funded before? At what amounts? For what kinds of work? A funder's past grants are the single most honest statement they'll ever make about what they actually care about. If organizations like yours, doing work like yours, show up repeatedly in their giving history, your percentage goes up.
What's your relationship? Have they funded this organization before? Have you spoken with a program officer? Were you invited to apply? A warm relationship and a track record move the needle substantially. A cold prospect who's never heard your name is, statistically, a longer shot — foundation data on cold prospects generally puts win rates in the modest 10–25% range, which is a useful reality check when you're tempted to mark a stranger at 70%.
Where are you in the process? A prospect you've merely identified is less certain than one where you've submitted a letter of inquiry, which is less certain than one where a full proposal is already under review. The further along, the higher the confidence.
When you weigh all of that, the percentage stops being a feeling and starts being a judgment — an informed, defensible judgment you can explain to anyone who asks.
I want to zoom out for a second, because forecasting isn't a standalone trick. It's the last step in a process, and it only works if the earlier steps are solid.
This is exactly how I structure the Certificate in Grant Writing course, and it's deliberate. Students don't start by forecasting. They start by learning how to find and strategize approaches for every type of grantmaker there is — government, foundation, corporate, tribal, clubs, and associations. Each type funds differently, wants different things, and requires a different approach, and you can't put an honest percentage next to a prospect if you don't understand what kind of funder you're dealing with.
As students work through each funder type, they research real prospects for their own project — checking priorities, giving history, and fit — and document each one in a grant calendar. By the end of the course, they're not sitting on a vague list of names. They have a researched, strategized set of prospects with deadlines and fit notes attached.
Then, as the final capstone, they take that whole calendar of prospects and run a grant revenue forecast using the exact method I just walked you through. It's the moment where all the research pays off and turns into a number.
I mention this because it came up in a recent conversation about what organizations wish grant writing graduates walked in already knowing — and this was near the top of the list. Being able to look at a pipeline and produce a defensible revenue projection is a genuinely marketable skill. (I wrote more about the skills organizations want to see in [the article on what organizations wish grant writing graduates knew] — worth a read if you're building out your professional value.)
The point is: the forecast is only as good as the research underneath it. Garbage prospects in, garbage forecast out. Which brings me to a few rules.
The method is simple, which means it's also easy to misuse. Here are the mistakes I see most often — avoid these and your forecast will stay honest.
Don't count multi-year awards all at once. If you're forecasting a three-year, $90,000 grant, that's $30,000 a year, not $90,000 this year. Match the money to the year it actually arrives.
Watch your timeline. A grant with a decision date in next fiscal year doesn't belong in this year's revenue forecast, no matter how promising it is. Forecasting isn't just how much — it's when. Track expected decision and payment dates alongside your percentages.
Remember that a submitted proposal is not confirmed revenue. It's tempting to bump everything you've submitted up to 90%. Resist. Submitted means "in the running," not "in the bank."
Keep restricted and unrestricted funds in mind. A forecast total is one thing; whether the organization can actually use that money for the gap it's trying to fill is another. Restricted grants only count toward the programs they're restricted to.
Don't over-trust a single grant. As I said earlier, the math works across a portfolio, not on one line. A $50,000 prospect at 60% does not mean "$30,000 is basically guaranteed." It means "this one's a coin flip that leans yes, and I'm counting on the rest of the pipeline to balance it."
When I set out to write this article, I genuinely wanted to know: is my method the best one, or have I just been doing the same thing for 25 years out of habit? So I went digging. Here's what else is out there — and my honest take on each.
The cutoff method. Instead of weighting every prospect, you draw a line — say, 75% — and count only the prospects above that line, at their full requested amount. Everything below the line gets ignored. It's blunter than the weighted method, but it produces a "money I can basically bank on" number that conservative boards sometimes prefer. Some people run both and present the results as a range: the weighted total and the cutoff total. Not a bad move for a nervous finance committee.
Scenario forecasting. Rather than one number, you build three — conservative, expected, and optimistic. It's a nice way to communicate uncertainty, but the genuinely rigorous version takes a lot more information to assemble, which is why most people default back to a single weighted number. Useful when you specifically need to show a board a range of outcomes.
Historical/trend-based forecasting. You use your own track record — actual win rates, renewal rates on existing grants — to project forward. This is less a replacement for the weighted method and more a superpower for it: if you've tracked your real outcomes, your percentages stop being guesses and start being grounded in data. ("My cold prospects historically convert at about 15%, so that's what this one gets.") If you do nothing else differently this year, start tracking your actual results.
Stage-based pipeline forecasting. A structured cousin of my method: instead of assigning a percentage by feel, you assign it by pipeline stage — a "prospect" automatically gets a lower percentage than a "letter of inquiry submitted," which gets a lower one than "full proposal under review." It's basically the weighted method with guardrails, which makes it great for teams who need everyone assigning percentages consistently.
Monte Carlo simulation. The high end. Instead of one percentage per grant, you define a range of possible outcomes and let software run your pipeline thousands of times to produce a full distribution — not just an expected total, but the probability of hitting a specific target ("55% chance of clearing $100K"). It's genuinely powerful and genuinely overkill for a solo grant writer or a small shop. I mention it mostly so you know the sophisticated end of the spectrum exists — and so you can see that it's the same core idea as my method, just with a supercomputer bolted on.
Here's my honest verdict after all that research: use the probability-weighted method. It's the practical default for a reason.
It's simple enough to run in a spreadsheet in an afternoon. It scales from a solo freelancer's client to a good-sized development shop. It's transparent — anyone can see exactly how you got your number, which matters enormously when a board starts asking questions. And it's the foundation that every one of those "fancier" methods is built on top of anyway. The cutoff method is a variation of it. Stage-based is a structured version of it. Monte Carlo is it, industrialized.
The other methods are worth knowing about — they'll come up, and now you can speak to them intelligently. But you don't need them to produce an honest, defensible forecast. You need a list of well-researched prospects, an honest percentage next to each one, and the discipline to multiply and add.
That's the whole craft. Go forecast something.
What's the difference between a grant revenue forecast and a grant goal?
A forecast is a prediction of what you're likely to raise based on your current pipeline. A goal is what you want to raise. They're related but not the same — and a good goal respects the forecast, along with your staff capacity and what the organization can responsibly manage. Don't set a goal your pipeline can't support.
What percentage should I assign to a brand-new funder I've never worked with?
For a genuinely cold prospect — no relationship, no history with them — err on the low side. Foundation data generally puts cold-prospect win rates in the 10–25% range, so something in that neighborhood is realistic unless the fit is exceptional. Resist the urge to inflate it just because you're excited about the opportunity.
How many prospects do I need before the forecast is reliable?
The more the better, but the method starts producing trustworthy totals once you're forecasting across a real portfolio — think a dozen or more prospects. With just two or three, the total is a rough estimate at best, because there's nothing for the individual over- and under-estimates to balance against.
Do I include multi-year grants at their full value?
No. Split a multi-year award across the years it will actually be paid out, and only forecast the portion arriving in the year you're budgeting for.
Where do I learn to research prospects well enough to assign good percentages?
Honest percentages come from solid funder research across all grantmaker types. That research process — finding, strategizing, and documenting prospects for every kind of funder, then forecasting from them — is exactly what the Certificate in Grant Writing course walks you through, start to finish.
Forecasting grant revenue isn't about predicting the future perfectly — nobody can do that. It's about turning a list of maybes into a number you can defend, so the organization can plan, so you know where to spend your time, and so you never have to answer "how much will we raise?" with a shrug. The method is simple: research each prospect honestly, assign a percentage, multiply, and add. The discipline is in doing it well. Do it consistently, and you stop guessing about your revenue and start managing it.
Cheering you on,
Allison
Candid. (2024). Foundation grantseeking and win-rate research. Referenced in nonprofit grant pipeline forecasting guidance.
Nonprofit Finance Fund. (2026, February). The art of forecasting contributed revenue.https://nff.org/insights/the-art-of-forecasting-contributed-revenue/
If you want to learn the whole process from the ground up — how to find and strategize every type of grantmaker, research them for fit, build a grant calendar, and forecast revenue from it as your capstone — that's exactly what my Certificate in Grant Writing course teaches, start to finish.
