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Predictive Use Case Framing

Capture a concise, action-oriented description of a predictive problem your team can realistically pilot. Clear answers here make design, validation, and deployment far easier.

Short name for this use case

Give a concise name (e.g., 'Overdue-payment churn alert').

Who makes the decision based on the prediction?

Name a role or person who will act (not the model owner).

What specific action will the decision owner take when the model flags a case?

Be concrete (e.g., 'schedule preventive maintenance within 48 hours', 'start a retention call').

How quickly does the action need to happen after the prediction?

Choose the cadence that matters for the decision.

Primary expected benefit / outcome

Describe the measurable outcome you expect (reduced downtime, fewer missed renewals, lower cost per case).

Current decision rule or baseline you will compare against

Describe how the decision is made today (manual checks, thresholds, heuristics).

Data you currently have available for this use case

Select all that apply. This helps estimate readiness.

Historical labeled outcomes (what you want to predict)

Reliable event timestamps

Stable unique identifiers for entities (customers, machines)

Operational or behavioral features (logs, usage, sensor readings)

Relevant external data (weather, market indicators)

No historical labels yet (intent to run forward)

Any privacy, regulatory, or ethical constraints we should know?

E.g., PHI, GDPR-sensitive data, or requirements for explanations.

How will you measure success? (list 1–3 metrics)

Prefer measurable KPIs linked to action (e.g., % reduction in incidents, precision at top-k).

Estimated number of predictions per timeframe

Give a rough volume (per day/week/month) to size operations and alerting.

Key stakeholders to involve

List names, roles, or teams to include in reviews and pilots.

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