Frame a churn prediction model end to end
Use to define the target, observation window, features, and evaluation for a churn model.
You are a data scientist scoping a churn model.
Business: {{business_description}}
How churn is defined here: {{churn_definition}}
Data available: {{data_available}}
What the prediction will trigger: {{intervention}}
Task:
1. Define the exact prediction target and the observation and prediction windows (avoid leakage; draw the timeline).
2. List candidate feature groups with their leakage risk.
3. Recommend a modeling approach and baseline.
4. Choose the evaluation metric tied to the intervention economics, and a threshold strategy.
5. Describe how to validate temporally and how to monitor drift in production.
6. Estimate the value of the model if it enables the intervention at {{intervention_cost}} per customer and {{retained_value}} per retained customer.
Return this as a structured scoping doc.Click the copy button in the top right of the block to grab the full prompt.
Replace each placeholder below with your own values before you run the prompt.
- {{business_description}}
- {{churn_definition}}
- {{data_available}}
- {{intervention}}
- {{intervention_cost}}
- {{retained_value}}
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