Connect your data
You connect one or more data sources (your CRM, warehouse, files, or a streaming feed) inside a dataset, and map them to Allyy’s data model. See Connect a data source.
Build a model
Allyy trains a model on your history to answer a specific question — will this person donate?, will they lapse?, how much should we ask for? See What Allyy can predict.
Generate predictions
The trained model scores your supporters, producing a prediction (a probability or an amount) for each one. See Generating predictions.
Make a decision
Allyy combines those scores — for example propensity × expected amount — and optimises them into a ranked, budget-aware contact list. See From predictions to decisions.
The pieces, and how they fit
Everything in Allyy is one of a small number of objects. If you understand these, you understand the platform:| Object | What it is |
|---|---|
| Dataset | A project-level container holding the data sources for one body of work. |
| Data source | A connection to your data (BigQuery, SQL Server, CSV, SFTP, Salesforce, …), batch or streaming. |
| Model | A trained predictor for one question (propensity, churn, expected amount, lifetime value, …). |
| Prediction | The scores a model produces for a population at a point in time. |
| Decision | Scores combined and optimised into a ranked action list for a campaign. |
| Export | A decision pushed out to your tools (or pulled via API). |
| Workflow | Automation that runs syncs, training, scoring and exports on a schedule. |
The Monitoring page tracks all of these in one timeline — Datasets, Models, Predictions, Exports and Workflows — so you can see at a glance what ran, when, and whether it succeeded. See Monitoring & logs.
Descriptive vs predictive
Allyy gives you two complementary lenses:Predictive
Models, predictions and decisions look forward — who to contact next and how.
Descriptive
The Analytics dashboards look backward — what your supporter base looks like today and how it’s trending, no modelling required.