Skip to main content
A prediction tells you how likely or how much for each supporter. A decision turns that into action: who to contact, in what order, and how far down the list to go — given your budget and goals.
“Decision” is the combination and optimisation of one or more model scores into a contact list. (Not to be confused with content recommendations, which is a separate recommender feature for your CMS.)

​
Why scores aren’t enough on their own

Ranking by a single score is a good start, but the best campaigns combine signals and account for cost:

Combine signals

Multiply propensity × expected amount to rank by expected revenue, not just likelihood — a supporter who’ll probably give a little may be worth less than one who’ll possibly give a lot.

Account for cost

Every contact costs money. Past a point, mailing more people lowers profit. The decision finds where to stop.

​
What a decision does

1

Brings scores together

It pulls in the relevant model scores for your population — for example DM propensity plus expected amount.
2

Computes a decision value

It combines them (e.g. expected revenue) and ranks every supporter.
3

Optimises the volume

Using a profit curve — expected income minus cost as you contact more people — it identifies the volume that maximises return, and where you break even.
4

Segments and labels

It can split the list into segments (e.g. high-potential vs standard), reserve a slice for testing under-explored supporters, and hold out a control group to measure impact.
5

Produces the final list

The output is a ranked, labelled contact list ready to export.

​
The profit curve, in plain terms

Imagine ordering every supporter best-first. As you add more, total income keeps rising but each extra contact adds cost and brings a less-likely supporter. Plot profit against volume and you get a hump: profit climbs, peaks, then falls.

Optimal volume

The peak — contact this many for maximum profit.

Break-even

The point past which extra contacts cost more than they bring in.

Confidence band

A range around the peak — the curve is often flat on top, so nearby volumes are about as good.
The model-evaluation dashboards let you explore exactly this with a cost-per-letter input and an as-is vs optimised comparison — see Model evaluation dashboards.

​
Testing and fairness

Good programmes keep learning. A decision can:
  • Hold out a control group so you can prove the uplift versus not contacting.
  • Reserve an exploration quota — deliberately include some lower-scored or under-contacted supporters so the model keeps learning about them rather than only ever mailing the obvious choices.
  • Run experiments — split supporters into arms (e.g. standard vs higher ask) to measure what works.

​
Next

→ Exporting decisions