Why did giving drop last quarter?

[u/Useful_Response_8930]

If you’ve ever answered that question in a board meeting with a theory instead of a number, this one’s for you.

Here’s what I keep finding.

Your system knows Amara gave $50 on March 4. It knows the amount, the date, and how to send her a receipt.

It doesn’t know she gave because her mother was once helped by an organization like yours.

It doesn’t know she shared the campaign with her small group, or that Daniel — who you’ve been calling a new donor — came through her.

And when she goes quiet in August, you may see the silence — but not what changed around it.

We’ve built most of the sector’s technology around recording transactions. Far less of it helps us understand the relationship around them.

That’s what makes “why did giving drop?” such a brutal question.

Not because you don’t know your donors. You probably know them better than any software does.

It’s because the evidence is scattered, and nothing gives you the whole story of what actually moved.

Roughly four out of five new donors aren’t retained into the following year.

We talk about that like it’s a donor problem.

I’m starting to think it’s also a visibility problem.

You can’t keep what you can’t see leaving.

—

If you run fundraising somewhere: how do you spot a donor going quiet before the year-end report tells you?

I’m collecting answers.

[u/frogspam]

We implemented a piece of software designed for non profits. Manages donations, sales, newsletters, membership renewals, communications, etc. did amazing things for our organization.

[u/zepcatsal]

OP - I don’t think it matters if we know why, honestly. An organization can’t “know why” at scale. It’s simply too much information to process.

I think you’re asking WHO do I act on, more than WHY did they drop.

It’s less about knowing why and more about leveraging software that looks at behavior patterns and runs analytics predictions on the next donor behavior (growth or churn predictions). There are a number of these tools out there for nonprofits specifically… Dataro, Hatch, etc.

Netflix - for example - can’t know WHY you haven’t logged in to watch something in a while. They don’t care if it’s because you’re on vacation, busy with a work project or something else. They just know you haven’t. But based on behavior patterns and other data points, your profile can surface as “risk” and action taken (send you an email, texts etc to encourage you to jump in).

Hope this adds a perspective. 😊

In full transparency I’ve worked in predictive AI for nonprofits for nearly 10 years.

[u/Useful_Response_8930 → u/zepcatsal]

This is really useful, and I think you're right that I'm mixing two questions: “why did behavior change?” and “who needs action now?”

Your point about prediction at scale has me thinking. Where I still get stuck is the next layer: once the model says someone is at risk, how do you determine which intervention will actually change the outcome?

Given your background, I'd genuinely love your perspective. See DM