Set suggested donation amounts from your own gift history

Suggested donation amounts should come from the gifts you are trying to repeat, not another nonprofit's form. Start with recent completed gifts from a comparable appeal, decide whether you want to protect participation or test a larger ask, then show three useful amounts plus an open amount. Change one choice at a time so the next result teaches you something.

That order matters because a higher default and a lower entry point can serve different goals. A 14-month PayPal Giving Fund experiment with more than 400,000 donors found that lowering the lowest default attracted more donors, while increasing the highest default raised more money (Stanford Graduate School of Business). There is no single number that wins both decisions for every appeal.

Choose the objective before the amounts

Write one sentence that defines what this form needs to learn. For example:

• Protect the number of first-time gifts while the campaign reaches a new audience.
• Test whether returning donors will choose a larger one-time gift.
• Make a monthly option understandable without making it look like a one-time amount.

Pick one. If the team wants more donors, a larger average gift, and more recurring gifts from the same edit, nobody will know what caused the result.

Keep one-time and recurring amounts separate. A $120 one-time gift and a $10 monthly gift create the same first-year total, but donors encounter different commitments on the form. Combining them into one distribution hides that difference and can produce a monthly array that feels implausibly high or a one-time array that starts too low.

Build a comparable gift set

Pull completed gifts from the closest available match to the new appeal. Use the same giving frequency, a similar audience, and the same campaign type when you can. Exclude failed, refunded, test, and offline-entered transactions if the question is about an online form.

Do not mix every gift your organization has received. A gala table, an emergency appeal, a peer-to-peer page, and a general donation form ask people to make different decisions. A large event payment can distort an annual-fund range even when both records are called donations.

For a lean team, the useful output is simple:

1. Count the comparable completed gifts.
2. Sort the amounts from low to high.
3. Note where gifts cluster and where a few large outliers begin.
4. Record the middle gift, not only the arithmetic average.
5. Save the date range and filters so the next review uses the same definition.

If changing one or two gifts would redraw the whole picture, the sample is not a precise model. Use it as a starting clue, keep the choices broad, and plan to review after the campaign. Do not turn a thin history into a claim about donor capacity.

Turn the range into three useful buttons

Choose clean values near the lower, middle, and upper parts of the comparable range. They do not need to reproduce exact past amounts. Their job is to make three plausible decisions easy to recognize.

Suppose a spring appeal has 42 comparable one-time gifts. Most sit between $25 and $100, the middle gift is $50, and three gifts above $500 are clear outliers. A reasonable starting array could be `$25`, `$50`, and `$100`, with `Other` available. The three large gifts stay in the history, but they do not force the public form to begin at $500.

Now connect the array to the objective:

• To protect participation with a new audience, keep the lower option visible and avoid preselecting an amount without a reason.
• To test gift size among returning donors, keep the lower and middle values stable and change only the upper option or selected default.
• To test monthly giving, build a separate monthly set from completed monthly gifts and label the frequency beside every amount.

Always keep an open amount. Suggested values are shortcuts, not a judgment about what a donor should give.

Test one decision, not the whole form

Do not change the amounts, default, copy, layout, and campaign page at once. Decide which element owns the test, record the starting version, and compare completed gifts on the same basis.

Suggested amounts are testable, but published results are not portable guarantees. Fundraise Up reports that a newer personalization model increased its average donation amount with lifetime value by 4.2% in a 55-day test across 600,000 Campaign Page visitors without reducing conversion (Fundraise Up). That result describes its model, pages, traffic, and measurement. It supports testing your choices; it does not supply your three numbers.

For a simple review, compare:

• completed gifts divided by form starts;
• the middle completed gift and total dollars;
• one-time and recurring results separately; and
• the share of donors who chose `Other`.

The `Other` share is useful evidence. If many donors leave the buttons, the range may not fit the audience. If almost nobody uses the lowest or highest value, that button may be taking space without clarifying a real choice. Treat either pattern as a question for the next controlled change, not an automatic verdict.

Keep the form decision connected to the campaign

Document the objective, source gift set, three values, default, open-amount option, and review date beside the campaign. That small record prevents a future teammate from copying an old array without knowing why it existed.

Settle where the donation form should live separately from the amounts inside it. When you evaluate fundraising forms, check that one-time and recurring giving levels can be configured clearly, then run a test gift at each value before launch.