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Once your data is connected and mapped, you can train a model. A model learns from your history — past offers and responses — to predict what will happen next. You configure it once; you can then retrain it whenever your data refreshes.
Not sure which model to build? See What Allyy can predict for the catalogue and how to choose.

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Before you start

1

Connected, mapped data

The model trains on mapped data inside a dataset. In particular, gift amounts, timestamps and response types must be mapped correctly — they define what “success” means. See Data mapping.
2

Enough history

Models learn patterns from the past, so they need a reasonable history of offers and responses. The more representative the history, the better the model.

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Training a model

1

Create a model in your dataset

Open the dataset and create a new model.
Screenshot to add — the model creation screen (creating a new model inside a dataset).
2

Choose the question (model type)

Pick what the model should predict — for example DM propensity (who will respond to a mailing) or expected amount (how much they’ll give). This choice sets how Allyy defines the target and which signals it builds.
3

Define the scope

Tell Allyy which records the model should learn from and predict on — for example a particular campaign type, channel, or supporter segment.
4

Let Allyy build the features

Allyy assembles the model’s inputs from your data using built-in feature recipes — recency and frequency of giving, gift-size trends, channel history, engagement, and more. You don’t compute these by hand.
5

Train

Start training. Allyy fits the model on your history, tunes it, and validates it on held-out data so the reported performance reflects unseen supporters.
Screenshot to add — training in progress and a completed training run.
6

Review performance

When training finishes, review the model’s quality before using it (see below).

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Is the model any good?

After training, Allyy reports how well the model separates likely responders from unlikely ones, and how its predictions break down. The clearest read is on the model dashboards, which compare the model’s scores against what actually happened in past campaigns. → See Model evaluation dashboards for how to read model performance, score groups, and the profit curve.
A model doesn’t have to be perfect to be useful — it has to be better than your current selection. The dashboards show exactly how much cost you can save (or income you can gain) versus contacting everyone.

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Retraining

As new data arrives, retrain the model so it stays current. You can do this manually, or schedule it with a workflow so it always trains on fresh data before each campaign.

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Next

Generate predictions

Apply your trained model to a population to produce scores.

Model evaluation

Evaluate the model and see the impact of optimising your selection.