Methodology
The care behind every number.
Every name on your file is someone who chose to give to your work, and this page shows how we help you steward them well. Each method is named, each is stated plainly, and the statistics are linked so you can learn the terms and check the work. The through-line is simple: steward your donors well and their generosity grows.
Revision: July 2026
An average hides the donors quietly slipping away.
Every name on your file is a person who gave to your mission. Some give more each year, some quietly stop, and a small group carries most of the generosity. Read with an annual average, none of that is visible.
An average retention rate cannot say which month a donor drifted away. An average gift cannot say which donors give ten times what others do. An average year cannot separate a strong December from a base that is thinning underneath it.
The cost of not seeing this is real, and it is felt by people. It shows up as donors who stop giving without anyone noticing, and as money spent inviting people who were never going to stay. On a typical file, close to a quarter of active donors lapse in a year, most of them without a single sign in the reports your team actually reads.
22%of active donors lapse in a year
Lineage
Every number traces back to a gift.
Before the first model runs, three systems have to become one. Your CRM holds contacts and gifts. Google Analytics and your email platform hold how those donors arrived and what they opened. We land each source as it is, then reconcile all three into a single gift ledger without discarding an original field.
From that ledger, every named model reads the same rows, and every figure it returns can be followed back down the pipeline to the gift that produced it. This is the part a finance team asks about first. A number that reconciles to source is one your team can act on, and that counts for more than a number that only looks polished.
Schematic of the lineage every figure travels. Sample sources for a fictional organization.
1Survival analysisa,b
Why
An annual retention rate tells you how many donors stayed. It cannot tell you when the others drifted away, and the when is where you can still reach them. Kaplan-Meier estimation reads each donor as a tenure with a status, still giving or lapsed, and returns the month-by-month chance that a donor is still with you. Because it handles right-censoring, donors who are simply still active never drag the curve down.
How
Confidence intervals use Greenwood's formula, so the band widens honestly as later cohorts thin: a 36-month estimate built on fewer surviving donors carries more uncertainty than a 6-month one, and the chart shows it. A companion hazard function view estimates the risk of lapse at each month of tenure, usually sharpest in the window right after a first gift.
What it's worth
The months where the hazard spikes are the moments to reach out, with a warm thank-you, a note about the work their gift is doing, or an invitation to give again. Your care stops being spread evenly across the calendar and starts landing in the weeks that decide whether a donor stays with you.
Illustrative — sample data for a fictional organization.
2Lifetime valuec
Why
Every donor deserves to be stewarded according to how they give, and a flat lifetime value hides those differences. We fit a Weibull survival tail to each giving tier and discount the projected stream back to today, so a $25-a-month donor and a $1,000-a-year donor are cared for as the distinct relationships they are.
How
Each fit reports its shape and scale parameters and an R-squared against the observed curve. The discounted value carries a sensitivity grid across discount rate and horizon, so the figure is never a single point divorced from the assumptions behind it.
What it's worth
Seen clearly, every tier gets the welcome and the invitation that fits it. You stop over-investing in one group and overlooking the mid-tier donors who sit one thoughtful ask away from giving more.
| Horizon | 6% | 9% | 12% |
|---|---|---|---|
| 3-year | $182 | $174 | $167 |
| 5-year | $241 | $224 | $209 |
| 7-year | $278 | $252 | $231 |
Illustrative — sample data for a fictional organization.
3Seasonal-trend decompositiond
Why
A strong December can hide a base of donors that is thinning the other eleven months. seasonal-trend decomposition splits a monthly giving series into three parts, a repeating seasonal pattern, an underlying trend, and a remainder, so you can tell real growth in generosity from a good season.
How
Reading the trend on its own answers the question your team keeps asking: set aside the year-end lift and the summer lull, is generosity growing or slipping? A rising seasonal peak sitting on a falling trend is a common and costly pattern, and the decomposition makes it plain.
What it's worth
You learn whether last year's growth in giving was real or seasonal before you build next year's plan on it, and you keep the year's giving from being over-committed on a false read.
Illustrative — sample data for a fictional organization.
4Concentration risk
Why
When a small group of donors carries most of the giving, losing one of them is felt across the whole year, and each of them deserves close care. A Lorenz curve and its Gini coefficient measure exactly how much rides on those few, using the same math economists use for income inequality.
How
Donors are ordered smallest to largest and the curve plots the cumulative share of revenue against the cumulative share of donors. The further it bows from the diagonal, the more revenue rides on a few names, and the Gini puts that gap in one number between zero and one.
What it's worth
The review sizes what those few relationships mean in dollars, so care for your closest donors and a wider base both get planned against a number instead of a hunch. On a concentrated file, more than half of giving can rest on the top tenth of donors.
Illustrative — sample data for a fictional organization.
5Net dollar retentione
Why
Leaders fund growth they can trust. Net dollar retention is the operating metric a SaaS finance team lives by, applied to your donors: whether last year's supporters gave more or less this year, before a single new donor is counted.
How
It extends dollar retention to include upgrades and downgrades among the donors you kept, and excludes acquisition entirely. A base of donors can grow in headcount while its net dollar retention sits below 100%, which means giving among the people you kept is slipping under the growth.
What it's worth
One number tells you whether the generosity of the donors you already have is growing or fading, and where care for them returns more than chasing new names.
Illustrative — sample data for a fictional organization.
6Acquisition economics
Why
A channel that looks cheap on the first gift is often the costliest once you see who stays. Reading Google Analytics and the email platform next to the gift file shows what the donors from each channel, campaign, promotion, and appeal went on to give, not just what they gave first. The finance term is customer acquisition cost, read honestly against a cohort's two-year giving.
How
Each channel carries a true cost to bring a donor in and the discounted value of the donors it brought, tracked as a cohort across the following two years rather than judged on the first gift.
What it's worth
You invite more people through the channels whose donors stay and keep giving, and fewer through the ones that only looked cheap at the first gift. The best channel can return several times what the cheapest-looking one does.
| Channel | CAC | 24-mo value | Return |
|---|---|---|---|
| $18 | $142 | 7.9× | |
| Organic search | $24 | $118 | 4.9× |
| Referral | $31 | $126 | 4.1× |
| Paid social | $52 | $96 | 1.8× |
| Paid search | $68 | $88 | 1.3× |
Illustrative — sample data for a fictional organization.
7Revenue bridge
Why
An organization that can trace last year's giving to this year's, to the dollar, trusts the plan built on it. The revenue bridge is an accounting identity, not a model. It attributes the full year-over-year change in giving to named parts, new, reactivated, upgraded, downgraded, and lapsed, and the pieces sum exactly to the difference. It reads like the waterfall a CFO already knows.
How
Because it reconciles, it is auditable. If the components do not add up, the analysis has an error, and that constraint is the point. Every dollar of change has to be explained rather than waved at.
What it's worth
Your team sees exactly where giving grew and where it slipped last year, and next year's plan for stewarding those donors is built on a number that ties out.
Illustrative — sample data for a fictional organization.
8AI, on top of the analysis
The language model sits on top of the analysis, not inside it. Every figure, from the survival estimate to lifetime value to the revenue-bridge parts, is computed by the named methods above. The model calculates none of them. It reads the finished numbers and states them in plain language.
It is AI, so it can be wrong. It summarizes and drafts, and it does not decide what your organization should do. Treat its answers as a starting point, verify anything you act on, and let the people who know your donors make the calls.
The close
What you leave with.
Each method here ends in a stewardship move you can price and sequence, in order of what it returns against what it costs to run.
- 01Find the months your donors drift away, and reach them there with thanks and a fresh invitation to give.
- 02See every donor and every channel by two-year giving, not the first gift, and welcome each one accordingly.
- 03Tell real growth in generosity from a good December before you plan on it.
- 04Name in dollars how much rides on your closest donors, and widen the base of generosity that carries it.
- 05Hand your team a revenue bridge that traces last year's giving to this year's, to the dollar.
It adds up to one thing: donors stewarded well, and generosity that grows because of it. What a first read surfaces depends on the file. For some organizations it is hundreds of thousands in giving they were quietly losing, for others more, and the plan to keep it traces back to your own giving records.
Notes
- aKaplan, E. L. and Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282).
- bGreenwood, M. (1926). The natural duration of cancer. Reports on Public Health and Medical Subjects, 33. Basis for the survival-variance estimate.
- cWeibull, W. (1951). A statistical distribution function of wide applicability. Journal of Applied Mechanics, 18(3).
- dCleveland, R. B., Cleveland, W. S., McRae, J. E., and Terpenning, I. (1990). STL: A seasonal-trend decomposition procedure based on loess. Journal of Official Statistics, 6(1).
- eRetention definitions follow the Fundraising Effectiveness Project conventions, with lapse windows stated per file.
The same rigor, run for your donors.
Every method here runs on the contacts and gifts you already export, with its assumptions stated plainly and its numbers traced back to your own giving records.