What metrics would you use to evaluate a new Radar feature for enterprise merchants

Problem Statement Description

Product context: Stripe is financial infrastructure for internet businesses; its products include payments, Checkout, Billing, Connect, Radar, Issuing, Terminal, and tax tools.

Stripe Radar helps merchants detect and manage payment fraud while preserving legitimate conversion. In this interview, you are evaluating a newly launched or proposed Radar feature targeted at enterprise merchants, who typically operate at high transaction volume, across multiple markets, payment methods, customer segments, and risk policies.

Enterprise merchants have complex fraud operations: they may use custom rules, internal risk teams, third-party tooling, manual review queues, chargeback workflows, and region-specific compliance processes. A successful feature must create measurable value without introducing unnecessary friction for legitimate customers, operational burden for fraud teams, or reliability risk for critical payment flows.

Your task is to define how you would measure whether the new Radar feature is working. Focus on building a metrics framework that is useful for product, risk, engineering, sales, and merchant success teams to decide whether to launch, expand, iterate, or roll back the feature.

The experience should consider:

- Clear definition of the primary success metric and why it reflects enterprise merchant value.

- The denominator and unit of analysis, such as transaction, merchant, account, payment attempt, dispute, or reviewed order.

- How metrics would be instrumented across Radar decisions, merchant configurations, payment outcomes, disputes, and manual review workflows.

- Cohorts and segmentation, including merchant size, industry, geography, payment method, risk profile, integration type, and feature adoption depth.

- Guardrail metrics for false positives, authorization impact, conversion, latency, merchant operations, customer experience, and compliance risk.

- How to separate feature impact from seasonality, fraud attacks, merchant mix changes, policy changes, and broader payment-network effects.

- Decision usefulness: what metric movements would indicate product-market fit, enterprise readiness, rollout risk, or need for further iteration.

The goal is to present a rigorous measurement approach for a high-stakes Stripe product area where fraud reduction, conversion optimization, trust, and financial reliability must be balanced for large merchants.