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Training teaches a model the patterns in your history. Generating predictions (also called scoring) applies that trained model to a population today, producing a score for every contact in it.

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What a prediction is

Each contact gets a score whose meaning depends on the model type:

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Prediction configurations

A prediction configuration is a saved recipe that you define once and re-run whenever you need fresh scores. It captures:
  • Which models to score with — one or more trained models (e.g. DM propensity, expected amount).
  • Which population to score — the contacts you want ranked: a specific list, a contact segment, filtered by response type, or the full active audience.
  • Response type filter — optionally restrict scoring to contacts with a particular history (e.g. Payment, Pledge, or telemarketing respondents).
Running the configuration is called a prediction execution. Each execution is timestamped and stored so you can see exactly which run fed a given decision or export.

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Running a prediction

1

Open your prediction configuration

Choose an existing configuration or create a new one. Give it a clear name that identifies the model and the campaign or audience it targets.
2

Choose the population to score

Point the model at the contacts you want scored — typically a list (e.g. everyone eligible for an upcoming appeal). You can combine multiple lists or filter by segment. Only contacts present in your mapped data can be scored.
3

Select which models to include

A single configuration can run multiple models at once — for example, DM propensity and expected amount together — so all the scores you need for a decision are produced in one go.
4

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 and compare against what actually happened.
5

Run

Allyy scores every contact in the population and stores the results. The execution status moves from Running to Completed (or Failed with a log if something goes wrong). Once complete the scores are ready to view, turn into a decision, or export.
Screenshot to add — a completed prediction run showing status, population size, and example scores.

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Reading the results

After a run completes you can inspect the scores in the platform. Each record shows:
  • The contact (contact ID and any display fields you’ve mapped).
  • The score — a probability between 0 and 1 for classification models, a currency amount for regression models.
  • The execution timestamp — which run produced this score.
A score ranks contacts — it does not tell you how many to contact or with what. That is the job of a decision.

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Keeping predictions fresh

Scores reflect the data at the moment they were generated. Contact behaviour changes — recent donors, lapsed contacts, and new subscribers all shift the rankings. Re-run scoring before each campaign — or schedule it with a workflow — so you’re always acting on current behaviour.

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Next

Turn scores into action

Combine and optimise scores into a ranked, segmented contact list.

Check the model first

Use the evaluation dashboards to confirm the model ranks well before you rely on its scores.