You’ll need an Allyy account (follow the invitation email) and access to a data source — your CRM, a warehouse like BigQuery, or even a CSV export.
Create a dataset
A dataset is the container for everything that follows. Create one for the body of work you have in mind (e.g. “Direct mail appeals”).
Connect a data source
Inside the dataset, add a data source and enter its credentials. Preview the data to confirm it loaded correctly.→ Connect a data source
Map your data
Map your columns onto the Allyy data model — Contacts, Responses, Offers, Subscriptions. This is what lets Allyy understand gifts, campaigns and supporters generically.→ Data mapping · The Allyy data model
Train a model
Create a model, choose the question you want to answer (e.g. DM propensity — who is likely to respond to a mailing), and start training. Allyy builds the features and trains on your history.→ Create & train a model · What Allyy can predict
Generate predictions
Point the trained model at the population you want to score. Allyy produces a score for each supporter.→ Generating predictions
Make a decision and export it
Turn the scores into an optimised, ranked contact list, then export it to your campaign tools.→ From predictions to decisions · Exporting decisions
Measure the result
After the campaign runs, use the model-evaluation dashboards to compare predicted vs actual, and the analytics dashboards to see the effect on your supporter base.→ Model evaluation · Analytics dashboards
What next?
Understand your file first
No model needed — the analytics dashboards describe your supporter base as it is today.
Automate the loop
Once it works manually, schedule it with a workflow so it runs on its own.