> ## Documentation Index
> Fetch the complete documentation index at: https://docs.allyy.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Subscription churn

> Will a regular giver cancel their direct debit soon?

Predicts which regular givers are at risk of cancelling — so a small save programme can protect a large share of recurring income.

**What it predicts** — For supporters on an active recurring mandate, the probability their subscription ends within the next window (e.g. 3 or 6 months).

**Who it scores** — Only currently-active subscribers as of the scoring date. One-off donors and already-cancelled givers are excluded automatically.

**The decision it drives** — Who to put into a *save* or thank-and-reassure programme before they lapse: a call, a stewardship email, an impact update.

**Worked example** — You score 12,000 active regular givers each month and flag the top 800 by churn risk. The retention team calls the highest-value 200 and emails the rest; cancellations in that group fall against a holdout.

**What good looks like** — A small, cheap intervention protects a disproportionate share of recurring income.

> [!TIP]
>
> **Pairs well with lifetime value** — prioritise saves by what's at stake.

> [card] [All models](https://docs.allyy.io/models/what-allyy-can-predict.md)
>
> Back to the full catalogue.

> [card] [Create & train a model](https://docs.allyy.io/processes/model.md)
>
> Set one up on your own data.


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