What a prediction is
Each supporter gets a score whose meaning depends on the model type:| Model type | Score meaning |
|---|---|
| Propensity / churn / engagement (classification) | A probability from 0 to 1 — e.g. 0.82 = “82% likely to respond”. |
| Expected amount / lifetime value (regression) | An amount — e.g. an expected gift of £45. |
| Ask amount | A suggested set of ask values for that supporter. |
Running a prediction
Choose the model
Pick a trained model. Allyy uses the model exactly as it was trained — the same features and logic — so predictions are consistent with the reported performance.
Choose the population to score
Point the model at the supporters you want scored — typically a list (e.g. everyone eligible for an upcoming appeal). Only supporters present in your mapped data can be scored.
Set the scoring date (optional)
By default Allyy scores as of now. You can also score as of a past date to back-test how the model would have ranked a previous campaign.
Predictions are descriptive of likelihood, not instructions. A score ranks supporters; deciding how many to contact and with what is the job of a decision.
Keeping predictions fresh
Scores reflect the data at the moment they were generated. Re-run scoring before each campaign — or schedule it with a workflow — so you’re always acting on current behaviour.Next
Turn scores into action
Combine and optimise scores into a ranked contact list.
Check the model first
Use the evaluation dashboards to confirm the model ranks well before you rely on its scores.