Pilot Strategy & Financial Model — Lantern San Diego
Overview
This document outlines the financial and strategic roadmap for the Lantern pilot in San Diego (60–90 days), including risk/reward analysis, revenue/cost projections, and implementation phases.
1. Risk & Reward Analysis 📊
Key Risks
Product & Market Risks
| Risk | Probability | Impact | Mitigation |
|---|---|---|---|
| Low merchant adoption / unwillingness to pay | Medium–High | High | Validate with 2–3 conversations before build; use partner's network for early access |
| Users don't check in or redeem offers | Medium | High | Start with high-value offers (BOGO, deep discounts); gamify check-ins with badges |
| Fraud & abuse dilute merchant ROI | Medium | High | Implement geofence + QR verification; monitor closely in pilot; set fraud thresholds |
| Competitive pressure from Yelp, Groupon, etc. | High | Medium | Differentiate via location-based discovery and social features (Wave); focus on merchant relationship |
Operational Risks
| Risk | Probability | Impact | Mitigation |
|---|---|---|---|
| Merchant support overhead | Medium | Medium | Keep onboarding simple; use templates; set SLA for responses (24–48h) |
| Payment processing failures | Low | Medium | Test Stripe integration thoroughly before pilot; have manual fallback |
| Privacy/data breach | Low | High | Encrypt location data at rest; use third-party identity services; audit before launch |
| Regulatory / local promo law friction | Low | Medium | Brief legal review of CA promotional rules; label sponsored content clearly |
Execution Risks
| Risk | Probability | Impact | Mitigation |
|---|---|---|---|
| Merchant dashboard delays | Medium | Medium | Use stubs & static mockups initially; MVP = CSV export + email reporting |
| Scale testing (10+ merchants) hits bugs | Medium | Medium | Heavy manual QA during weeks 4–6; daily standups with team |
| Geographic expansion pressure too early | Medium | Low | Document that pilot is San Diego-only; secure commitment to phased rollout |
Key Rewards
Short-term (Pilot: Weeks 0–12)
- Customer discovery: Direct merchant feedback on offer mechanics, pricing, and feature gaps
- Proof of concept: 50+ verified redemptions demonstrate product-market fit signal
- Revenue proof: 1–5K in pilot revenue validates monetization model
- Team learning: Operational playbook for merchant onboarding, support, fraud detection
- Network effect: Early merchant testimonials and word-of-mouth for phase 2 expansion
Medium-term (Phase 2: Post-pilot expansion, 3–6 months)
- Revenue scaling: 10–50K MRR from 50–100 merchants (San Diego + adjacent markets)
- Unit economics: Refined CAC, LTV, and merchant churn rates inform pricing strategy
- Product leverage: Merchant dashboard, analytics, and tiered pricing reduce support overhead
- Team hiring: Validate business case for merchant success manager and customer support hire
Long-term (Phase 3: 12+ months, multi-market)
- Network effects: Wave/social features increase user stickiness and check-in rates (flywheel)
- Recurring revenue: Subscription tiers unlock 50K–500K ARR from tiered merchants + frequency
- Acquisition advantage: Service industry partnerships / affiliate models reduce CAC to <5% of LTV
- Exit optionality: Acquired as merchant loyalty platform or standalone SaaS
2. Financial Model Overview 💰
TL;DR: Profitable from day 1. ~85/month fixed infrastructure cost. Merchants pay ~165/month avg. Every merchant grosses ~80/month profit to the platform.
Full economics details: See economics/ECONOMICS.md for comprehensive cost breakdown, pricing models, unit economics, and payment processor assumptions.
12-Month Revenue & Profit Projection
We'll track a cohort of merchants from recruitment through month 12, accounting for churn.
Base Assumptions:
- Merchant acquisition: 2–4 new merchants/month during pilot, 3–4/month during phase 2
- Merchant monthly retention: 95% (5% churn/month; implies 60% retention over 12 months)
- Average revenue per active merchant: $165/month
- Monthly operating cost (fixed): ~$85/month
Month-by-Month Breakdown (12-Month Projection):
| Mo | Phase | Merch | Revenue | Cost | Monthly Profit | Cumulative Profit |
|---|---|---|---|---|---|---|
| 0 | Pilot | 2 | 330 | 85 | 245 | 245 |
| 1 | Pilot | 5 | 825 | 85 | 740 | 985 |
| 2 | Pilot | 7 | 1155 | 85 | 1070 | 2055 |
| 3 | Pilot | 8 | 1320 | 85 | 1235 | 3290 |
| 4 | Phase2 | 11 | 1815 | 85 | 1730 | 5020 |
| 5 | Phase2 | 14 | 2310 | 85 | 2225 | 7245 |
| 6 | Phase2 | 17 | 2805 | 85 | 2720 | 9965 |
| 7 | Phase2 | 19 | 3135 | 85 | 3050 | 13015 |
| 8 | Phase2 | 21 | 3465 | 85 | 3380 | 16395 |
| 9 | Phase2 | 23 | 3795 | 85 | 3710 | 20105 |
| 10 | Phase3 | 29 | 4785 | 125 | 4660 | 24765 |
| 11 | Phase3 | 35 | 5775 | 125 | 5650 | 30415 |
| 12 | Phase3 | 40 | 6600 | 125 | 6475 | 36890 |
| YR1 | — | 40 | 38115 | 1225 | 36890 | 36890 |
(All values in USD)
Column Definitions:
- Mo = Month (0–12)
- Phase = Pilot / Phase 2 / Phase 3
- Merch = Active merchants at end of month
- Revenue = Active merchants × $165 ARPU
- Cost = $85/month (infrastructure) or $125/month (with contractor in Phase 3)
- Monthly Profit = Revenue − Cost for that month
- Cumulative Profit = Sum of all profits from Month 0 to that month
Key Milestones:
- End of Pilot (Month 3): 3,290 cumulative profit ✓
- End of Phase 2 (Month 9): 20,105 cumulative profit ✓
- End of Year 1 (Month 12): 36,890 cumulative profit ✓
Profit Breakdown by Phase:
| Phase | Months | Merchants | Revenue | Cost | Profit |
|---|---|---|---|---|---|
| Pilot | M0–3 | 10 acquired, 8 active | $3,630 | $340 | +$3,290 |
| Phase 2 | M4–9 | 21 acquired, 23 active | $17,325 | $510 | +$16,815 |
| Phase 3 | M10–12 | 24 acquired, 40 active | $17,160 | $375 | +$16,785 |
| TOTAL | M0–12 | 55 acquired, 40 active | $38,115 | $1,225 | +$36,890 |
User Adoption & Daily Active Users (DAU)
To understand the product impact alongside merchant revenue, let's model user growth:
Assumptions:
- Each merchant drives ~50–100 users (unique customers exposed to offers)
- Of exposed users, ~20–30% become active and check-in at least weekly
- Of active users, ~40% check in multiple times per week (engaged DAU)
| Month | Merchants | Potential Users | Active Users (20%) | Engaged DAU (40% of active) | Avg. Redemptions/User/Month |
|---|---|---|---|---|---|
| 0 | 2 | 150 | 30 | 12 | 1.2 |
| 1 | 5 | 375 | 75 | 30 | 1.5 |
| 2 | 7 | 525 | 105 | 42 | 1.8 |
| 3 | 8 | 600 | 120 | 48 | 2.0 |
| 4 | 11 | 825 | 165 | 66 | 2.1 |
| 5 | 14 | 1,050 | 210 | 84 | 2.2 |
| 6 | 17 | 1,275 | 255 | 102 | 2.3 |
| 7 | 19 | 1,425 | 285 | 114 | 2.4 |
| 8 | 21 | 1,575 | 315 | 126 | 2.5 |
| 9 | 23 | 1,725 | 345 | 138 | 2.6 |
| 10 | 29 | 2,175 | 435 | 174 | 2.7 |
| 11 | 35 | 2,625 | 525 | 210 | 2.8 |
| 12 | 40 | 3,000 | 600 | 240 | 2.9 |
Key User Metrics (Month 12):
- Potential Users Exposed: 3,000 (by month 12)
- Monthly Active Users (MAU): 600 (people who check in at least once/month)
- Daily Active Users (DAU): ~240 (estimated; ~40% of MAU checking in on any given day)
- Engagement: 2.9 redemptions per user per month (improving with product maturity)
Interpretation:
- By month 12, ~240 users are engaging daily with offers
- This validates product stickiness & merchant value (users returning for deals)
- DAU growth correlates directly to merchant retention (users = merchant ROI)
Key Financial Metrics (with Profit Breakdown)
| Metric | Value | Calculation / Note |
|---|---|---|
| Revenue (12 months) | $38,115 | $165 ARPU × active merchants across year |
| Operating Cost (12 months) | $1,225 | $85/month infrastructure + contractor ramp |
| Net Profit (12 months) | +$36,890 | $38,115 revenue − $1,225 costs |
| Net Profit Margin | 96.8% | $36,890 / $38,115 |
| Breakeven | Month 0, Day 1 | Profitable from go-live (every merchant adds $165 revenue vs $22 cost) |
| Average Revenue Per Merchant (Annual) | $1,980 | $165/month × 12 months |
| Average Cost Per Merchant (Annual) | $22 | $1,225 annual cost / 55 merchants |
| Merchant LTV | $1,980 | Annual average before extended periods |
| Profit Per Merchant | $1,958 | $1,980 revenue − $22 cost |
| Customer Acquisition Cost | $0 | Founder-driven during pilot & phase 2 |
Unit Economics Ratios
| Ratio | Value | Interpretation |
|---|---|---|
| LTV:CAC | Infinite (then 90:1) | Exceptional; CAC is near-zero until we hire |
| Gross Margin | ~96% | Platform business; minimal COGS |
| Net Margin | ~96% | Low fixed costs; high profitability |
| Payback Period (per merchant) | < 1 month | $165/month revenue vs $22 cost = 0.1-month payback |
What Drives Profitability?
| Factor | Impact | Mitigation |
|---|---|---|
| Merchant churn | High churn reduces LTV quickly | Focus on onboarding quality & early wins; aim for 95%+ retention |
| ARPU variation | Lower ARPU (e.g., PPC-only) reduces profit | Mixed pricing model balances risk; flat campaigns lock in revenue |
| Cost growth | If we hire too early, margin erodes | Hire contractor only when >20 active merchants justify support |
| API overage | Could add $200–500/month if scale explodes | Monitor usage; shard data or move to cached approach if needed |
Stress Test: When Does The Model Break? 🚨
What if acquisition costs or operating expenses spike? Let's test failure scenarios to understand breakeven points.
Stress Test Scenarios
Scenario 1: User-Merchant Ratio Imbalance (Too Many Users, Too Few Merchants)
Assumption Change: Viral user growth without merchant growth. 500+ users acquire organically but only 8 merchants by month 12. User engagement drives up API costs dramatically.
| Mo | Users | Merchants | Revenue | API Cost | Total Cost | Monthly Profit | Cumulative Profit | Status |
|---|---|---|---|---|---|---|---|---|
| 0 | 30 | 2 | 330 | 0 | 85 | 245 | 245 | 🟢 |
| 1 | 75 | 5 | 825 | 0 | 85 | 740 | 985 | 🟢 |
| 2 | 150 | 7 | 1155 | 10 | 95 | 1060 | 2045 | 🟢 |
| 3 | 250 | 8 | 1320 | 50 | 135 | 1185 | 3230 | 🟢 |
| 4 | 400 | 9 | 1485 | 150 | 235 | 1250 | 4480 | 🟢 |
| 5 | 600 | 10 | 1650 | 250 | 335 | 1315 | 5795 | 🟢 |
| 6 | 850 | 11 | 1815 | 400 | 485 | 1330 | 7125 | 🟢 |
| 7 | 1100 | 12 | 1980 | 550 | 635 | 1345 | 8470 | 🟡 |
| 8 | 1350 | 13 | 2145 | 700 | 785 | 1360 | 9830 | 🟡 |
| 9 | 1600 | 14 | 2310 | 850 | 935 | 1375 | 11205 | 🟡 |
| 10 | 1900 | 15 | 2475 | 1000 | 1085 | 1390 | 12595 | 🟡 |
| 11 | 2200 | 16 | 2640 | 1150 | 1235 | 1405 | 14000 | 🟡 |
| 12 | 2500 | 17 | 2805 | 1300 | 1385 | 1420 | 15420 | 🟡 |
Outcome: Model stays profitable but API costs erode margins as user base grows without matching merchant revenue. By month 12, API costs hit 1300/month (31% of revenue). Cumulative profit is still positive at 15,420 but growth trajectory is unsustainable. 🟡
Lesson: Without merchant monetization parity, you're running a user product with poor unit economics. You need users → merchants alignment. If users spike before merchants, either:
- Accelerate merchant onboarding to match user growth
- Implement user-side monetization (subscription, premium features)
- Optimize API usage (cache queries, reduce refresh frequency)
The Real Risk: A viral user loop that doesn't translate to merchant revenue is a growth trap, not a success.
Scenario 2: Heavy Support Overhead (Hire Early, Pay More)
Assumption Change: Hire a full-time support person from Month 1 at 4000/month (vs. waiting until Month 4 to hire a contractor)
| Mo | Merchants | Revenue | Cost (Full-time + Infra) | Monthly Profit | Cumulative Profit | Status |
|---|---|---|---|---|---|---|
| 0 | 2 | 330 | 4085 | -3755 | -3755 | 🔴 |
| 1 | 5 | 825 | 4085 | -3260 | -7015 | 🔴 |
| 2 | 7 | 1155 | 4085 | -2930 | -9945 | 🔴 |
| 3 | 8 | 1320 | 4085 | -2765 | -12710 | 🔴 |
| 4 | 11 | 1815 | 4085 | -2270 | -14980 | 🔴 |
| 5 | 14 | 2310 | 4085 | -1775 | -16755 | 🔴 |
| 6 | 17 | 2805 | 4085 | -1280 | -18035 | 🔴 |
| 7 | 19 | 3135 | 4085 | -950 | -18985 | 🔴 |
| 8 | 21 | 3465 | 4085 | -620 | -19605 | 🔴 |
| 9 | 23 | 3795 | 4085 | -290 | -19895 | 🔴 |
| 10 | 29 | 4785 | 4085 | 700 | -19195 | 🟡 |
| 11 | 35 | 5775 | 4085 | 1690 | -17505 | 🟡 |
| 12 | 40 | 6600 | 4085 | 2515 | -14990 | 🟡 |
Outcome: Model goes unprofitable until Month 10. Cumulative loss at Month 12: -14990. ❌
Lesson: Hiring full-time too early burns ~15K. You'd need to raise capital to cover this overhead.
Scenario 3: Aggressive User Acquisition Spend
Assumption Change: Spend 1000/month on paid acquisition (ads, partnerships) to accelerate merchant growth to 5/month starting Month 2
| Mo | Merchants | Revenue | Cost (Infra + Acq) | Monthly Profit | Cumulative Profit |
|---|---|---|---|---|---|
| 0 | 2 | 330 | 85 | 245 | 245 |
| 1 | 3 | 495 | 85 | 410 | 655 |
| 2 | 8 | 1320 | 1085 | 235 | 890 |
| 3 | 13 | 2145 | 1085 | 1060 | 1950 |
| 4 | 18 | 2970 | 1085 | 1885 | 3835 |
| 5 | 23 | 3795 | 1085 | 2710 | 6545 |
| 6 | 28 | 4620 | 1085 | 3535 | 10080 |
| 7 | 33 | 5445 | 1085 | 4360 | 14440 |
| 8 | 38 | 6270 | 1085 | 5185 | 19625 |
| 9 | 43 | 7095 | 1085 | 6010 | 25635 |
| 10 | 48 | 7920 | 1085 | 6835 | 32470 |
| 11 | 53 | 8745 | 1085 | 7660 | 40130 |
| 12 | 58 | 9570 | 1085 | 8485 | 48615 |
Outcome: Despite spending 1K/month on acquisition, still profitable (even more so due to faster scaling). Cumulative profit: +48615. ✓
Lesson: Paid acquisition pays for itself if ARPU is high enough (165 per merchant easily covers 1K/month spend across 6+ months of LTV).
Scenario 4: Low ARPU (PPC-Only Model)
Assumption Change: Only PPC pricing ($1.50/redemption, no flat campaigns). Average redemptions per merchant/month drops to ~8. New ARPU: ~12/month (not 165)
| Mo | Merchants | Revenue | Cost | Monthly Profit | Cumulative Profit | Status |
|---|---|---|---|---|---|---|
| 0 | 2 | 24 | 85 | -61 | -61 | 🔴 |
| 1 | 5 | 60 | 85 | -25 | -86 | 🔴 |
| 2 | 7 | 84 | 85 | -1 | -87 | 🔴 |
| 3 | 8 | 96 | 85 | 11 | -76 | 🟡 |
| 4 | 11 | 132 | 85 | 47 | -29 | 🟡 |
| 5 | 14 | 168 | 85 | 83 | 54 | 🟡 |
| 6 | 17 | 204 | 85 | 119 | 173 | 🟡 |
| 7 | 19 | 228 | 85 | 143 | 316 | 🟡 |
| 8 | 21 | 252 | 85 | 167 | 483 | 🟡 |
| 9 | 23 | 276 | 85 | 191 | 674 | 🟡 |
| 10 | 29 | 348 | 125 | 223 | 897 | 🟡 |
| 11 | 35 | 420 | 125 | 295 | 1192 | 🟡 |
| 12 | 40 | 480 | 125 | 355 | 1547 | 🟡 |
Outcome: Model barely profitable. Cumulative profit: +1547 (vs. +36890 baseline). Margins collapse to ~4%. ❌
Lesson: PPC-only model is not viable. You need flat campaigns (high ARPU) to sustain growth. Merchants won't renew on PPC alone.
Scenario 5: High Churn (10%/month instead of 5%)
Assumption Change: Merchants churn at 10%/month instead of 5% (maybe product quality issues or competing offer)
| Mo | Merchants | Revenue | Cost | Monthly Profit | Cumulative Profit |
|---|---|---|---|---|---|
| 0 | 2 | 330 | 85 | 245 | 245 |
| 1 | 4 | 660 | 85 | 575 | 820 |
| 2 | 6 | 990 | 85 | 905 | 1725 |
| 3 | 7 | 1155 | 85 | 1070 | 2795 |
| 4 | 9 | 1485 | 85 | 1400 | 4195 |
| 5 | 11 | 1815 | 85 | 1730 | 5925 |
| 6 | 13 | 2145 | 85 | 2060 | 7985 |
| 7 | 15 | 2475 | 85 | 2390 | 10375 |
| 8 | 17 | 2805 | 85 | 2720 | 13095 |
| 9 | 19 | 3135 | 85 | 3050 | 16145 |
| 10 | 23 | 3795 | 125 | 3670 | 19815 |
| 11 | 27 | 4455 | 125 | 4330 | 24145 |
| 12 | 31 | 5115 | 125 | 4990 | 29135 |
Outcome: Still profitable, but cumulative profit drops to +29135 (vs. +36890). Growth is slower due to retention loss. 🟡
Lesson: Churn is manageable if you acquire faster. Focus on retention to avoid this drag.
Scenario 6: The Perfect Storm (All Bad Things)
Assumption Change: Full-time hire (4000/month) + Low ARPU (50/month) + High churn (10%/month)
| Mo | Merchants | Revenue | Cost | Monthly Profit | Cumulative Profit | Status |
|---|---|---|---|---|---|---|
| 0 | 2 | 100 | 4085 | -3985 | -3985 | 🔴 |
| 1 | 4 | 200 | 4085 | -3885 | -7870 | 🔴 |
| 2 | 6 | 300 | 4085 | -3785 | -11655 | 🔴 |
| 3 | 7 | 350 | 4085 | -3735 | -15390 | 🔴 |
| 4 | 9 | 450 | 4085 | -3635 | -19025 | 🔴 |
| 5 | 11 | 550 | 4085 | -3535 | -22560 | 🔴 |
| 6 | 13 | 650 | 4085 | -3435 | -25995 | 🔴 |
| 7 | 15 | 750 | 4085 | -3335 | -29330 | 🔴 |
| 8 | 17 | 850 | 4085 | -3235 | -32565 | 🔴 |
| 9 | 19 | 950 | 4085 | -3135 | -35700 | 🔴 |
| 10 | 23 | 1150 | 4085 | -2935 | -38635 | 🔴 |
| 11 | 27 | 1350 | 4085 | -2735 | -41370 | 🔴 |
| 12 | 31 | 1550 | 4085 | -2535 | -43905 | 🔴 |
Outcome: Model goes deeply unprofitable. Cumulative loss: -43905. 🔴
Lesson: This scenario requires external funding or a pivot. You cannot survive on this path.
Stress Test Summary
| Scenario | Year 1 Profit | Verdict | Action |
|---|---|---|---|
| Baseline (Current Plan) | 36890 | ✓ Healthy | Execute plan |
| Scenario 1: User-Merchant Imbalance | 15420 | 🟡 Risky | Balance growth; need monetization parity |
| Scenario 2: Hire Full-Time Early | -14990 | ❌ Avoid | Wait until >25 merchants |
| Scenario 3: Paid Acquisition | 48615 | ✓ Better | Consider if you have capital |
| Scenario 4: PPC-Only | 1547 | ❌ Avoid | Require flat campaigns |
| Scenario 5: High Churn (10%) | 29135 | 🟡 Risky | Focus on retention |
| Scenario 6: Perfect Storm | -43905 | 🔴 Critical | Pivot or raise capital |
Key Takeaways
- Don't hire too early. Full-time hires before Month 4–5 kill profitability. Stick with founder-driven for pilot.
- ARPU is critical. Flat campaigns (150/month) are essential. PPC-only doesn't work.
- Paid acquisition can work if ARPU is high enough. Consider it in Phase 2 if growth is too slow.
- Churn management is important but secondary. Even 10% churn still yields profit.
- The real risk: Combining low ARPU + early high costs + high churn. Avoid this.
Bottom Line: Stay lean in Pilot & Phase 2. Let profitability flow before hiring. This keeps you independent and proves the model works before scaling costs.
Summary: Financial Health Check
| Dimension | Status | Implication |
|---|---|---|
| Cash Flow | 🟢 Positive from Month 0 | No funding needed; profitable immediately |
| Unit Economics | 🟢 Exceptional (1,958 profit per merchant) | Every merchant is high-margin |
| Scalability | 🟢 Fixed costs; scales linearly | Adding merchants = pure profit (until contractor hire) |
| Risk | 🟡 Merchant churn (5%/month is aggressive) | Focus on retention; offset with acquisition pipeline |
| User Traction | 🟢 240 DAU by month 12; improving engagement | Product stickiness signals product-market fit |
| Runway | 🟢 Self-sustaining; 36K+ profit | Can re-invest in hiring or geographic expansion |
Bottom Line: This is a lean, profitable, capital-efficient business model. No VC funding required for the pilot or Phase 2. By month 12, you have enough profit to hire a full-time merchant success manager and expand to a second market without external capital.
3. Implementation Phases 🚀
Phase 1: Pilot Setup & MVP (Weeks 0–3)
Goal: Onboard 5 merchants, launch with minimal merchant dashboard, establish operational rhythm
Objectives
- [ ] Close 5 initial merchants (use partner's network; target: 3 weeks)
- [ ] Deploy MVP merchant dashboard (OfferForm + CSV export; leverage existing code)
- [ ] Establish fraud detection rules (geofence + timestamp verification)
- [ ] Launch Stripe integration for flat campaigns and PPC billing
- [ ] Create operational playbook (onboarding script, support SLA, weekly reporting)
Deliverables
- Merchant onboarding email template & walkthrough video (5 min)
- Minimal dashboard: create offer, view check-ins/redemptions, export CSV
- QR code generation for redemption verification
- Weekly merchant report (email or CSV)
- Fraud rule thresholds and monitoring dashboard (internal)
Success Metrics
- 5 merchants signed and live
- 0 critical bugs in offer creation / redemption flow
- <5% false-fraud rate in first 100 check-ins
Phase 2: Scale Testing & Iteration (Weeks 4–9)
Goal: Onboard 5–10 merchants total, test both flat & PPC pricing models, collect performance data
Objectives
- [ ] Recruit 3–5 additional merchants (target: weeks 4–5)
- [ ] A/B test flat campaign ($150) vs PPC ($1 redemption) with merchants
- [ ] Collect 50+ verified redemptions across all merchants
- [ ] Weekly check-in calls with 3–5 key merchants (gather feedback)
- [ ] Monitor fraud, chargeback, and user behavior metrics
- [ ] Prepare merchant satisfaction survey (NPS) at week 8
Deliverables
- Merchant dashboard v1.1: analytics dashboard (impressions, CTR, check-in rate, redemption rate)
- Pricing comparison report (flat vs PPC performance) at week 8
- Merchant feedback summary (5–8 key themes) and product roadmap adjustments
- User behavior analytics (check-in patterns, offer preference, geographic hotspots)
- Fraud detection report (false positives, true abuse, rate adjustment recommendations)
Success Metrics
- 8–10 merchants live
- 50+ verified redemptions across merchants
- Merchant NPS >= 50 (passive or promoter territory)
- Redemption rate 20–35% (offer quality signal)
- Fraud rate < 5%
Phase 3: Wrap-up & Go/No-Go Decision (Weeks 10–12)
Goal: Consolidate learnings, document playbook, decide on phase 2 rollout (geographic expansion or pivot)
Objectives
- [ ] Complete merchant feedback cycles (resolve key issues)
- [ ] Finalize pricing model recommendation (flat, PPC, hybrid, or tiered)
- [ ] Produce go/no-go investment memo for phase 2 (team + advisors)
- [ ] Document operational procedures (merchant onboarding, support, billing, fraud)
- [ ] Plan phase 2: geographic targets (LA, SF, Austin?) or deepening San Diego
Deliverables
- Pilot final report: cohort performance, CAC/LTV, churn, NPS, merchant testimonials
- Pricing model recommendation with supporting data
- Operational playbook (30 pages: onboarding, support, billing, fraud, escalations)
- Tech debt & product roadmap for phase 2 (performance, analytics, integrations)
- Go/no-go memo + phase 2 plan (3–6 month timeline, geography, hiring needs)
Success Metrics
- Merchant satisfaction >= 70% (NPS or survey)
- Willingness-to-renew >= 70% (would you pay again?)
- Team alignment on model (flat, PPC, or hybrid)
- Clear phase 2 plan with resource requirements
4. Key Decision Points & Milestones 🎯
| Week(s) | Milestone | Decision | If No | If Yes |
|---|---|---|---|---|
| 0–1 | Recruit 1st 5 merchants | Proceed to MVP? | Reprioritize outreach; extend timeline | Build MVP |
| 3 | MVP live | Proceed to Phase 2? | Fix critical bugs; extend timeline | Begin scale testing |
| 6 | 50+ redemptions | Pricing model sufficiently validated? | Adjust offers/pricing; test longer | Commit to recommended model |
| 9 | Merchant NPS + willingness-to-renew survey | Strong product-market fit signal? | Iterate product; extend pilot | Greenlight phase 2 |
| 12 | Final report & go/no-go | Proceed with geographic expansion? | Pivot to B2B SaaS or feature; archive | Plan phase 2 (LA, SF, Austin, or deepen SD) |
5. Team & Resource Plan 💼
Pilot Team (3 months)
| Role | Owner | Hours/week | Cost |
|---|---|---|---|
| All operations, product, support, onboarding | You (founder) | 30–40 | $0 (bootstrapped) |
| Total | — | 30–40/week | $0 (bootstrapped) |
Note: You handle everything until merchant adoption / support volume triggers need for contractor help. At that point, you hire (ROI is clear).
Phase 2 Hiring (if greenlit, months 4–6)
- Merchant Success Manager (1 FTE): onboarding, support, retention, up-sells
- Data Analyst (0.5 FTE): metrics, reporting, fraud detection tuning
- Support / QA (1 part-time contractor, ongoing)
6. Key Assumptions & Sensitivities 📈
Core Assumptions
- Merchant acquisition: 5 merchants signed in weeks 0–3; 3–5 more by week 6 (use partner's network; assume 20% cold-approach conversion rate)
- Check-in rate: 5–10% of offer impressions → check-ins (low initially; improves with social features)
- Redemption rate: 25–40% of check-ins → redemptions (depends on offer quality and geofence accuracy)
- Merchant churn: 5%/month in pilot; 10%/month year 1 post-pilot (high because still validating ROI)
- Pricing: Start conservative ($150 flat, $1 PPC); adjust based on feedback
Sensitivity Analysis: What moves the needle?
If redemption rate drops to 10% (vs. 25–40% target)
- → Offers not compelling; refocus on geofence accuracy, offer timing, or copy
- → Consider gamification (badges, loyalty) to boost engagement
- → Cost to merchants rises; willingness-to-renew drops
If churn exceeds 10%/month in pilot
- → Product-market fit signal is weak; pivot model or pause expansion
- → Increase merchant support; check in weekly instead of bi-weekly
- → Re-evaluate offer terms and redemption UX
If fraud rate exceeds 10%
- → Implement stricter geofence validation, QR verification for high-value offers
- → Merchant trust erodes; implement manual review process
- → Increase support overhead
If partner cannot refer 3+ merchants in weeks 0–3
- → Use product demo + cold outreach to venue owners/operators
- → Extend recruitment timeline; parallelize with product build
- → Set realistic milestone: 3 merchants by week 4 instead
7. Competitive & Market Context 🏪
Comparable Products & Approach
- Yelp (Deals): Established, inventory-heavy, high friction for merchants
- Groupon: Focused on discounting, poor merchant perception of ROI
- Toast (POS integrations): Enterprise SaaS, high CAC, not merchant-acquisition focused
- Foursquare/Swarm: Legacy, declining merchant adoption
Lantern's Differentiation
- Location + social discovery: Wave/check-in creates serendipity and user engagement (Foursquare did this; Lantern evolves it)
- Merchant-first monetization: Lean toward flat campaigns and predictable pricing (less race-to-the-bottom than Groupon)
- PWA/mobile-first: Frictionless check-in and offer redemption (no app store friction)
- Service industry focus: Start with cafés, bars, retailers—venues with high foot traffic and check-in culture
Why San Diego is a good pilot market
- Market size: ~100K hospitality / retail venues within metro area; 5–10 in walkable neighborhoods
- Partner advantage: Service industry network accelerates merchant access
- Diversity: Mix of chains (Starbucks), independents (coffee shops), nightlife (bars), retail (boutiques)
- Competition: Moderate; Yelp and Groupon present, but many merchants underserved
- Tech adoption: High; California users familiar with PWA and location-based apps
8. Post-Pilot API Cost Projections (Phase 2 & Beyond) 📡
San Diego Deepening (Phase 2, Months 4–6)
If pilot succeeds and you expand within San Diego only:
| Scenario | Active Users | Monthly API Requests | Monthly Cost | 3-Month Cost |
|---|---|---|---|---|
| Moderate growth (150 users) | 150 | 4,500 | $0 | $0 |
| Strong growth (300 users) | 300 | 9,000 | $0 | $0 |
| Aggressive growth (500 users) | 500 | 15,000 | $160 | $480 |
Still free for all reasonable scenarios. Google's free tier covers up to 10K requests/month.
Multi-Market Expansion (Phase 3, Months 7–12)
Once you expand beyond San Diego (e.g., LA, SF, Austin, NYC):
| Market Count | Est. Active Users | Monthly API Requests | Monthly Cost | Annual Cost |
|---|---|---|---|---|
| 1 market (San Diego) | 500 | 15,000 | $160 | $1,920 |
| 3 markets (SD + LA + SF) | 1,500 | 45,000 | $1,120 | $13,440 |
| 5 markets (add Austin + NYC) | 2,500 | 75,000 | $2,080 | $24,960 |
| 10 markets (major metros) | 5,000 | 150,000 | $4,480 | $53,760 |
Observation: API costs remain < 5% of total infrastructure spend at scale. Negligible compared to merchant support, payment processing, and engineering.
Cost Optimization Strategies (Post-Pilot)
Caching: Cache venue search results for 24–48 hours (reduce API calls by 30–50%)
- Cost reduction: $480–800/month for 3-market setup
Offline fallback: Pre-load popular venues in San Diego, LA, SF; fallback to API only for edge cases
- Cost reduction: Further 10–20% savings
Batch requests: If building internal tools, batch multiple searches into single API call (free tier includes 20 results per call)
- Cost reduction: Natural optimization as product scales
User behavior optimization: Gamify/incentivize users to check in at venues they've already searched (reduce re-searches)
- Cost reduction: 5–10% via engagement increase
Bottom line: Even without optimization, Google Maps API is a rounding error in startup costs. Focus on customer acquisition and product quality first; optimize API spend later.
9. Go/No-Go Criteria for Phase 2 🚦
Must-have (all required to expand)
- [ ] 50+ verified redemptions across merchants (product demand signal)
- [ ] Merchant NPS >= 50 or "would pay again" >= 70% (customer satisfaction)
- [ ] Fraud rate < 5% (operational health)
- [ ] 1 repeating customer cohort (at least 2–3 merchants renew offer after 30 days)
Nice-to-have (improve confidence, not blockers)
- [ ] User retention >= 20% (30-day active check-in rate)
- [ ] Merchant churn <= 5%/month (post-pilot; understand retention drivers)
- [ ] Revenue >= $500 (validates pricing model)
- [ ] Team alignment on go/no-go decision by week 12
Pivot Triggers (consider alternative direction)
- Redemption rate < 10% consistently (offer/product mismatch)
- Merchant churn > 20%/month (not sustainable)
- Fraud > 15% of redemptions (operational risk)
- Partner unable to refer merchants; cold outreach <5% success rate (CAC too high)
10. Financial Summary & Next Steps 📋
Pilot Investment Summary
Weeks 0–12:
- Shadow cost (founder time): $8,000
- Contractor/support: $2,000
- Infrastructure + tools: $250
- Google Maps API: $0 (free tier covers entire pilot)
- TOTAL INVESTMENT: ~$10,250
Expected outcomes (conservative):
- Revenue: $350–950 (break-even not expected; is R&D spend)
- Customer base: 5–10 merchants
- Customer validation: NPS, churn, renewal signal
- Operational playbook: repeatable onboarding & support model
Expected outcomes (optimistic):
- Revenue: $1,500+ (if 20+ redemptions/month)
- Customer base: 10+ merchants
- Unit economics: LTV:CAC >= 1.2 (path to 3:1+ with scale)
- Team alignment on phase 2 (geographic expansion, hiring, funding)Immediate Next Steps (This Week)
- Confirm partner availability & outreach plan (3–5 merchant targets)
- Review OfferForm.jsx and Dashboard.jsx in codebase; plan MVP scope (which features, which stub)
- Set up Stripe test account and integrate into OfferForm
- Create merchant onboarding email & QR generation script
- Schedule week 1 kickoff & merchant recruitment (target: first merchant call by EOW 2)
This document is a living strategy. Update with actual pilot data (redemptions, NPS, churn) every 2 weeks; adjust phase timelines and metrics as you learn.