Opportunity Solution Tree
Product Vision: Enable seamless restaurant reservations while eliminating no-shows
Last Updated: January 2026
How to Read This Tree
An Opportunity Solution Tree connects business outcomes → customer opportunities → solutions. It helps ensure every feature solves a real problem.
Desired Outcome (North Star)
├── Opportunity 1 (Customer Pain Point)
│ ├── Solution A (Feature/Experiment)
│ └── Solution B (Alternative Feature)
└── Opportunity 2 (Another Pain Point)
└── Solution C2
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ReserveEasy Opportunity Solution Tree
Detailed Breakdown
Desired Outcome (North Star Metric)
KPI: Monthly Completed Bookings
Target: 15,000 MAB (Monthly Active Bookers) by Month 12
Why: Completed bookings = revenue for us AND restaurants
Opportunity 1: Diners Abandon Booking Flow
Evidence:
- Funnel analysis shows 20% drop-off between "Check Availability" and "Confirm Booking"
- User interviews: "I started booking but got interrupted at work, never finished"
- Benchmark: Industry average is 15-18% drop-off
Impact: 1,000 lost bookings/month = $10k lost revenue
Solution 1A: 60-Second Booking Flow
Hypothesis: If we reduce form fields from 8 → 4, drop-off will decrease 20% → 16%
Experiment:
- A/B test: Control (8 fields) vs Treatment (4 fields: name, email, phone, special requests)
- Success Metric: Conversion rate improves by 4 percentage points
- Timeline: 2-week test with 5,000 users
Status: ✅ Shipped - Improved conversion by 5pp (20% → 15%)
Solution 1B: Guest Checkout
Hypothesis: Forcing account creation creates friction → allow guest booking
Experiment:
- Launch guest checkout in Week 1
- Track: % of bookings from guests vs registered users
- Success Metric: 30% of bookings are guests (proves demand)
Status: ✅ Shipped - 45% of bookings are guests (exceeded target)
Solution 1C: Apple Pay / Google Pay
Hypothesis: One-tap payment is faster than entering credit card details
Experiment:
- Integrate Apple Pay for iOS users
- Measure: Time from "Check Availability" → "Confirmed" decreases by 15 seconds
Status: 📅 Planned for Q2 2026
Opportunity 2: Restaurants Lose Revenue to No-Shows
Evidence:
- Industry data: 30% no-show rate across casual dining
- Restaurant interviews: "We lose $60k/year to no-shows" (Raj persona)
- Pain point mentioned by 18/20 restaurants in discovery
Impact: $60k lost per restaurant = biggest pain point
Solution 2A: Deposit System
Hypothesis: Requiring $10/person deposit (refundable on show-up) reduces no-shows from 30% → <5%
Experiment:
- Pilot with 10 restaurants for 1 month
- Compare no-show rate for bookings WITH deposit vs WITHOUT
- Success Metric: <10% no-show rate for deposit bookings
Status: ✅ Shipped - Achieved 4% no-show rate (beat target!)
Solution 2B: 24-Hour SMS Reminder
Hypothesis: People forget → reminder reduces forgetfulness-driven no-shows
Experiment:
- Send SMS 24 hours before booking: "Reminder: Table at Bella Italia tomorrow at 7pm"
- Track no-show rate: Control (no reminder) vs Treatment (reminder)
- Success Metric: 10% reduction in no-shows
Status: ✅ Shipped - Reduced no-shows by 12%
Solution 2C: Easy Cancellation
Hypothesis: If users can't cancel easily, they just don't show up
Experiment:
- Add "Cancel" button to SMS/email with one-click cancellation
- Track: Cancellation rate increases, but no-show rate decreases
Status: 📅 Planned for Sprint 16
Opportunity 3: Booking Modifications Are Difficult
Evidence:
- 15% of bookings are cancelled (then often re-booked at different time)
- User feedback: "I had to call the restaurant to change my time"
- Cancellation = lost opportunity (table sits empty until re-booked)
Impact: 750 cancellations/month, ~30% never re-book = 225 lost bookings
Solution 3A: Self-Serve Modification
Hypothesis: Let users change time/party size in-app → reduces cancellations
Experiment:
- Build "Modify Reservation" flow (check real-time availability, update booking)
- Track: Cancellation rate decreases by 5pp (15% → 10%)
Status: 🚀 In Progress - Sprint 15 (current sprint)
Solution 3B: Flexible Cancellation Policy
Hypothesis: Full refund if >24 hours notice → users cancel responsibly vs no-showing
Experiment:
- Communicate policy clearly: "Cancel anytime for full refund (24 hour notice)"
- Track: Cancellation rate increases, but no-show rate plummets
Status: ✅ Shipped - Policy live
How We Prioritized Solutions
We used RICE scoring to rank solutions:
| Solution | Reach | Impact | Confidence | Effort | RICE Score | Priority |
|---|---|---|---|---|---|---|
| Guest Checkout (1B) | 5000 | 3 | 100% | 2 weeks | 3750 | P0 |
| Deposit System (2A) | 5000 | 3 | 80% | 3 weeks | 2667 | P0 |
| 60-sec Flow (1A) | 5000 | 2 | 90% | 1 week | 4500 | P0 |
| SMS Reminder (2B) | 5000 | 2 | 100% | 1 week | 5000 | P0 |
| Self-Serve Modify (3A) | 3000 | 2 | 70% | 2 weeks | 1400 | P1 |
| Apple Pay (1C) | 2000 | 1 | 60% | 3 weeks | 400 | P2 |
Top Priority: SMS Reminder (RICE = 5000) → Shipped first
Lessons Learned
What Worked
✅ Deposit system exceeded expectations - Reduced no-shows to 4% (vs 5% target)
✅ Guest checkout was a must-have - 45% of bookings are guests
✅ Data-driven prioritization - RICE scores aligned with actual impact
What Surprised Us
- Apple Pay ranked low (RICE = 400), but user feedback says "payment is painless already"
- SMS reminders had biggest bang-for-buck (RICE = 5000, only 1 week to build)
What's Next
- Q2 Focus: Retention features (modify reservation, loyalty program)
- Deferred: Native mobile apps (web is sufficient for now)