Start by clarifying the business goal and framing the donation feature as a testable hypothesis about user behavior and social impact. Then outline a structured experiment plan covering metrics, design, risks, and decision criteria, emphasizing trade-offs between user experience and business outcomes.
Pro tip: Acknowledge that donation features can cannibalize core revenue or create user friction, and propose guardrail metrics to monitor these risks. Show maturity by discussing how to handle novelty effects and long-term impact.
Clarify the primary objective (e.g., social impact, brand perception, user engagement) and state testable hypotheses about how the donation feature will affect user behavior and business metrics.
Choose primary success metrics (e.g., donation rate, average donation amount) and guardrail metrics (e.g., order completion rate, average order value, customer satisfaction) to detect negative side effects.
Propose an A/B test with random assignment, define the control and treatment groups, determine sample size and duration, and consider segmentation (e.g., new vs. existing users, geography).
Anticipate risks such as user annoyance, donation fatigue, cannibalization of tips or revenue, and ethical concerns; suggest mitigation strategies like opt-out options or capping donation amounts.
Define decision criteria based on statistical significance, practical significance, and guardrail metrics; recommend a phased rollout or further iteration if results are mixed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.