What a prediction is
Each supporter gets a score whose meaning depends on the model type:Running a prediction
1
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.
2
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.
3
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.
4
Run
Allyy scores the population and stores the results, ready to view, turn into a decision, or export.
Screenshot to add — a completed prediction run with example scores.
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.