Case study 04
Turning tee-time chaos into a fair allocation system
Golf booking looks like a booking problem. It isn’t — it’s capacity-constrained social logistics. An 11-slide concept that replaces “fastest fingers” with a request window, weighted allocation and an admin rules engine.
- Context
- Product assignment · “Playo for Golf” (Assignment 1a)
- Year
- 2026
- Contribution
- Solo
- Frameworks
- Friction funnel · user prioritization 2×2 · MVP scoping · North Star · phased roadmap
What this is: an independent product exercise — an outside-in concept for a hypothetical golf vertical, produced with AI-assisted visuals (NotebookLM). Not affiliated with, commissioned by, or endorsed by Playo. The dashboard numbers shown below are illustrative targets inside a mockup, not measured results — nothing here shipped.
The reframe
“Golf is not just ‘booking’; it is a capacity-constrained, social logistics challenge.”
Slide 2 — the context
- Unlike a cinema seat, golf is a physical system with rigid rules — tee spacing, pace of play — plus a social requirement: you need a group.
- The triggering insight: existing “Chrome extension” hacks prove the current systems have failed. Seconds determine playability.
- The friction: a massive gap between intent (weekend morning demand) and supply (the tee sheet).
Mapped as a funnel: intent to play → coordination chaos (phone calls, “are you free?”, WhatsApp groups) → booking execution (fastest fingers, bots) → a small percentage of successful rounds, with failed bookings and stress leaking out the side.
The mechanism
Request window opens (5–10 min, no FCFS)→
Pool requests→
Weighted logic: waitlist history (+), no-show penalty (−)→
Allocation→
Confirm, or waitlist with priority
Why this allocation model over the alternatives: it neutralizes bots and scripts, removes exact-second anxiety, and preserves cultural fairness — unlike a “pay-to-win” auction, which a members’ club will not tolerate.