Set launch metrics for an experiment in subscription billing with high trust risk

Problem Statement Description

You are evaluating an experiment in a subscription billing flow for a student-focused product where the business goal is revenue recovery, but the trust risk is high. The experiment may affect how students are notified, retried, prompted to update payment details, moved through grace periods, or handled when billing fails or eligibility changes.

The user journey is sensitive because students are often price-conscious, may rely on discounted access, and may have limited payment methods or irregular cash flow. A billing change that recovers revenue but creates surprise charges, confusing renewal behavior, accidental loss of access, or support escalations could damage long-term trust.

Your task is to define the launch metrics framework for this experiment. Focus on what should be measured before launch, during rollout, and after launch to determine whether the experiment is safe, useful, and decision-ready.

The experience should consider:

- Clear metric definitions, including numerator, denominator, time window, and event trigger

- Revenue recovery measurement without overstating gains from delayed churn, failed retries, or double-counted payments

- Student-specific cohorts, such as new subscribers, renewing students, discounted-plan users, failed-payment users, and users near eligibility expiration

- Instrumentation needed across billing events, notifications, payment attempts, subscription state changes, access changes, refunds, and support contacts

- Guardrails for customer trust, including unexpected charges, refund requests, complaints, cancellation spikes, payment disputes, and access disruption

- Experiment readout needs, including baseline comparison, holdout/control design, statistical confidence, and cohort-level interpretation

- Operational signals such as payment processor errors, notification delivery failures, support volume, and manual intervention rate

- Decision usefulness: what results would justify ramping, pausing, iterating, or rolling back the experiment

The goal is to propose a metrics plan that helps a product team launch responsibly in a high-trust billing environment while balancing revenue recovery with student experience, fairness, and long-term subscription health.