“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 contact 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.
Decision configurations
A decision configuration is a reusable recipe — a named pipeline of steps that you define once and re-run each time you need a fresh contact list. Because the same configuration runs before every edition of a campaign, results are consistent and comparable across mailings. Each time you execute a configuration it produces a decision run: a timestamped, labelled contact list ready to export or review.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 from a recent prediction execution.
2
Computes a decision value
It combines them into a single ranking metric. The most common combination is expected revenue:
Expected revenue = Propensity score × Expected donation amountThis ranks each contact by what they’re likely to generate, not just how willing they are.
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 ranked list into named segments (e.g. high-potential, standard), reserve a slice for testing under-explored contacts, 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 to your CRM, mailing house, or other tool.
Segments
A decision can split the contact list into named groups based on score thresholds or rules:
Segment names and thresholds are configurable per decision.
The profit curve, in plain terms
Imagine ordering every contact best-first. As you add more, total income keeps rising but each extra contact adds cost and brings a less-likely contact. 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.
Control group
A control group is a randomly sampled slice of the eligible population that is withheld from contact. After the campaign you can compare their actual behaviour against the contacted group to isolate the uplift — the effect of mailing itself, not just the natural behaviour of likely donors. You can size the control group in several ways:
Experiment arms are useful when you want consistent group membership — the same contact will always land in the same arm across re-runs, making longitudinal measurement reliable.
Exploration quota
Over time, a model that only ever contacts the top-scored contacts stops learning about the rest of the audience. The exploration quota (also called reserve) deliberately includes a small number of lower-scored or under-contacted contacts in each contact list. This serves two purposes:- Cold-start fairness — new or lapsed contacts get occasional exposure rather than being permanently excluded.
- Model improvement — their responses (positive or negative) feed back into future training, keeping the model calibrated across the full contact base.