Design Parking for Google Maps

Design a parking-finding experience for **Google Maps**. Assume the product goal is to help drivers reduce uncertainty and wasted time when parking near a destination, especially in dense urban areas. Walk through how you would define the problem, choose a target user segment, design the product, prioritize an MVP, define success metrics, and address monetization and counter-metrics. ### Constraints & Assumptions - Keep the experience inside Google Maps rather than proposing a separate parking app unless you justify why. - Consider both garages/lots and street-parking uncertainty, but do not assume perfect real-time street availability. - Balance user trust, local regulation, partner data quality, and business incentives. - State assumptions for geography, launch scope, and data sources. - Prompt variant: the interviewer may not name the app and instead ask you to pick a navigation app you know well and design a parking solution for it. That framing is broader than parking-finding alone, so choose the app first and justify the choice (who uses it, what data it already has, and whether it is open at the moment parking hurts) before you define the problem. - Prompt variant: the interviewer may name Google Maps and ask how you, as its product manager, would improve the parking experience it already offers. Treat the scope as the whole parking journey (planning the trip, approaching the destination, choosing a spot, paying, walking to the destination, and getting back to the car), not only finding a space, and check what Maps already provides before you present any feature as new. ### Clarifying Questions to Ask - Is the primary goal user satisfaction, navigation completion, revenue, or marketplace expansion? - Are we launching globally or piloting in a few dense cities? - Can Google partner with parking operators, cities, or event venues? - Should the product support reservation/payment at launch, or only discovery and guidance? ### Part 1 - Define The User Problem And Segment Who are the target users, what pain are they experiencing, and why is this problem worth solving? #### What This Part Should Cover - A specific high-pain segment such as drivers going to dense urban destinations, airports, events, offices, or restaurants. - The core job to be done: arrive with confidence, avoid circling, understand price/rules, and complete the trip. - A reason not to solve for every driver and every city on day one. ### Part 2 - Design The Product What should the parking experience include, and how should it fit into the Google Maps journey? #### What This Part Should Cover - Pre-trip parking suggestions, availability confidence, price and walking-distance filters, and navigation handoff. - A clear distinction between reliable partner inventory and lower-confidence predictions. - User-facing trust mechanisms such as confidence labels, rule clarity, reports, and fallback options. ### Part 3 - Prioritize The MVP Which features would you launch first, and which would you defer? #### What This Part Should Cover - A focused MVP with structured garage/lot data, eligibility triggers, filters, and last-mile walking directions. - Deferred ideas such as guaranteed street parking, peer-to-peer spaces, or broad real-time coverage if they add too much risk. - A prioritization rationale based on reach, impact, confidence, and effort. ### Part 4 - Define Success, Business Model, And Counter-Metrics How would you measure success, monetize the product, and guard against harmful outcomes? #### What This Part Should Cover - A north-star or primary outcome tied to successful parking-assisted trips. - Metrics for adoption, time-to-park, prediction accuracy, reservation/payment conversion, repeat usage, and trust. - Revenue options such as referrals, bookings, transactions, or sponsored placements, with trust guardrails. - Counter-metrics for wrong recommendations, increased circling, citations, unsafe routing, cancellations, user complaints, and Maps performance impact. ### What a Strong Answer Covers - Starts with the user problem rather than a feature list. - Makes realistic assumptions about data quality and city-by-city launch constraints. - Shows good product judgment about trust, accuracy, and monetization tradeoffs. - Connects metrics to actual user value, not just clicks. ### Follow-up Questions - How would your answer change for street parking versus paid garages? - What would you do if availability predictions were only 60% accurate? - How would you rank paid placements without hurting user trust? - What local regulation or safety issues would you investigate before launch? - How would you test this in one city before scaling?