> ## 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.

# DM propensity

> Will this supporter respond to a mailing?

Predicts, for each supporter on a mailing list, how likely they are to respond to a direct mail appeal — so you can mail fewer packs without losing income.

**What it predicts** — The probability that a mailing leads to a donation within a set number of days of the mail drop.

**Who it scores** — Everyone on the mailing universe: the full list you could mail.

**The decision it drives** — Rank the mailing list and cut low-probability names, so you post fewer packs without losing meaningful income.

**Worked example** — You plan a 100,000-piece appeal. The model ranks the list; the bottom 30,000 contribute almost no expected response. You mail the top 70,000, save the print and postage on the rest, and hold response roughly flat.

**What good looks like** — Cost per response falls and net income holds or rises.

> [!TIP]
>
> **Pairs well with [Expected amount](https://docs.allyy.io/models/dm-expected-amount.md)** — rank on expected revenue (likelihood × value), not likelihood alone.

> [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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