You are the Product Manager for the **Profiles** area of a dating app such as Tinder. Design, validate, and launch a new profile customization feature that appeals to different user needs while still helping users form meaningful connections. ### Constraints & Assumptions - Consider Millennial and Gen Z users as hypothesis sources, not rigid product modes. - Avoid over-generalizing by age; segment by needs, behaviors, relationship intent, comfort with self-expression, and privacy preferences. - Balance richer expression with simplicity, safety, authenticity, and moderation cost. - Optimize for better connections, not only profile editing activity. ### Clarifying Questions to Ask - What is the business goal: onboarding completion, profile quality, match quality, conversations, retention, or safety? - Which market, age range, and relationship intents are in scope? - Are video, audio, prompts, badges, privacy controls, or identity fields allowed? - What moderation and trust-and-safety capacity exists? ### Part 1 - Define Users And Pain Points How would you think about different demographic groups without over-generalizing by age? #### What This Part Should Cover - Age-based hypotheses for Millennials and Gen Z, validated with research and data. - Need-based segments such as users needing guidance, users wanting rich expression, privacy-conscious users, and intent-driven users. - Core profile pain points: generic profiles, low trust, too much effort, privacy concerns, and weak conversation signals. ### Part 2 - Propose And Prioritize A Feature What profile customization feature would you prioritize first? #### What This Part Should Cover - A focused MVP such as guided prompts plus interest/intent badges and privacy controls. - Comparison against richer options like video intros, themes, or advanced customization. - Prioritization based on reach, impact, confidence, effort, safety, and moderation cost. ### Part 3 - Validate And Launch How would you validate and launch the feature? #### What This Part Should Cover - Qualitative research, prototype testing, beta rollout, A/B experiment, and phased launch. - Instrumentation before launch and trust-and-safety review. - Localization and demographic fairness checks. ### Part 4 - Measure Success And Guardrails How would you know whether the feature worked? #### What This Part Should Cover - Metrics for profile completion, prompt/badge adoption, match quality, match-to-message conversion, reply rate, retention, and user satisfaction. - A north-star-style outcome tied to meaningful connection. - Guardrails for onboarding drop-off, report/block rate, moderation load, misleading profiles, privacy complaints, and demographic disparities. ### What a Strong Answer Covers - Uses demographics thoughtfully without stereotyping. - Prioritizes a realistic MVP and explains tradeoffs. - Measures downstream connection quality, not just feature usage. - Addresses privacy, trust, and safety as first-class product constraints. ### Follow-up Questions - What would make you reject video intros for the MVP? - How would you define a meaningful connection? - What if Gen Z adoption rises but report rate also rises? - How would you localize profile prompts? - How would you avoid making users feel overexposed?