Lantern: Founder Context & Positioning β
Overview β
This memo establishes founder context, competitive positioning, and the strategic narrative for Lantern's San Diego pilot and beyond.
Founder Context π€ β
Lead (You) β
- Current role: Web/app analyst at YouTube
- Background: Web design and product experience
- Why this matters: Deep understanding of how users discover and engage with locations; experience building scalable platforms; design sensibility ensures frictionless UX
- Unique advantage: YouTube network and cross-functional insights into monetization and creator partnerships
Co-founder (Partner) β
- Current role: Service industry professional (operations, hospitality, or venue management)
- Background: On-the-ground relationships in hospitality/retail
- Why this matters: Direct access to merchant pain points, operational workflows, and venue networks; credibility in customer conversations; low CAC through referrals
- Unique advantage: Insider knowledge of what venues want (foot traffic, customer loyalty, data) and what they'll never pay for
Complementary Skills β
- Technical: You (product, design, analytics)
- Commercial: Partner (merchant relationships, operations, retention)
- Team: Contractor/support to handle onboarding and QA during pilot
Result: Ideal founding team for a merchant-first, location-based product. Avoids the trap of pure engineers building for users they don't understand.
Market Problem & Why Now π― β
The Problem β
Merchants (especially independent cafΓ©s, bars, small retail) struggle to:
- Drive foot traffic and repeat customers economically
- Compete with chains that have massive marketing budgets
- Understand who their customers are and what they want
- Offer experiences (not just discounts) to build loyalty
Users struggle to:
- Discover quality independent venues (Yelp is cluttered; Groupon is discount-heavy)
- Seamlessly check in and redeem offers without friction (apps, QR codes, paper coupons)
- Find serendipity and social connection at places (Foursquare is dead; Instagram is passive)
Why Now? β
- PWA maturity: Frictionless mobile experiences without app store overhead
- Geofencing accuracy: GPS and location APIs now enable accurate proximity-based features
- Post-COVID venue recovery: Hospitality venues are aggressive about acquiring customers; willingness to experiment with new tools
- Merchant tech adoption: Small businesses increasingly use Stripe, Toast, Squareβopenness to integration
- Social rediscovery: Gen Z and millennials return to venues for in-person experiences; location-based discovery matters again
Competitive Landscape π β
Established Players (Why They're Vulnerable) β
| Player | Strength | Weakness | Opportunity |
|---|---|---|---|
| Yelp (Deals) | Huge user base; reviews integrate with offers | High merchant friction; poor ROI perception; complex interface | Lantern: simpler offer creation, lower fees, merchant-first UX |
| Groupon | Proven unit economics; brand recognition | Merchant resentment (low margins); perception as discount-only | Lantern: focus on repeat customers, not one-time deals |
| Toast / Square | POS integrations; rich analytics | Targeted at enterprise; high CAC; not acquisition-focused | Lantern: pure acquisition and loyalty, cheaper to integrate |
| Foursquare / Swarm | Pioneered location + social | Declining daily users; merchant adoption fell off; no monetization for merchants | Lantern: clean monetization, user engagement via Wave, modern tech |
Emerging Competitors β
- TikTok Shop / Instagram Shops: Growing; could add location features; but not specialized for venues
- Local niche platforms: Micro-communities (e.g., neighborhood apps); small scale, low threat in 2026
Why Lantern Wins β
- Purpose-built for venues: Every feature (Wave, check-in, offers) is designed for in-person discovery
- Merchant-first monetization: Offers revenue models (flat, PPC, subscription) with healthy margins for merchants
- Frictionless UX: PWA, no app install, one-tap check-in and redemption
- Network effects: Wave (social discovery) + check-ins create user stickiness; loyalty features reduce merchant churn
- Founder advantage: Service industry co-founder means merchant relationships and real feedback loops
Strategic Positioning π β
Positioning Statement β
For independent hospitality and retail venues, Lantern is a customer discovery and loyalty platform that drives foot traffic and repeat customers with minimal friction, without requiring complex integrations or high fees.
Unlike Yelp (cluttered, user-focused) and Groupon (discount-focused, merchant resentment), Lantern prioritizes merchant success and sustainable unit economics.
Narrative Arc (Investor / Strategic Pitch) β
- Problem: Merchants want customers; users want discovery. Traditional tools (Yelp, Groupon) serve one or the other, not both.
- Solution: Lantern is a purpose-built venue discovery + loyalty platform for independent venues and their communities.
- Proof: San Diego pilot (5β10 merchants, 50+ redemptions, 70% NPS) validates product-market fit.
- Expansion: Phase 2 expands to LA, SF, Austin; phase 3 adds subscription analytics and payment services (becoming SaaS/marketplace hybrid).
- Exit: Acquired as merchant loyalty platform, or standalone at $50β500M valuation (similar to Toast, Square, Foursquare at peak).
Why San Diego? π΄ β
Geographic Rationale β
- Market size: ~100K hospitality / retail venues; 5β10 walkable, high-traffic neighborhoods
- Partner advantage: Direct network in San Diego service industry = low CAC, high trust
- Diversity: Mix of coffee shops, bars, upscale dining, casual retail
- Tech adoption: California users familiar with PWA, location apps, digital loyalty
- Competitive: Yelp and Groupon present but underserving independent venues
- Replicable: Success playbook transfers to LA, SF, Austin, NYC, Chicago
Why NOT a larger market first? β
- Execution risk: Distributing across cold markets requires expensive merchant sales team
- Founder leverage: Partner's network accelerates first 5 merchants; scales faster than cold outreach
- Data quality: Concentrated market allows daily feedback loops; easier to detect fraud and optimize
Narrative for Merchants πΌ β
When Recruiting Your First 5 Merchants β
Script:
"We're building a free, frictionless way to drive foot traffic and loyalty. Check out how it works:
- Create an offer (e.g., 'Buy 1 drink, get 50% off when you check in on Lantern')
- Users who are nearby see your offer, check in with one tap, and redeem
- You get a redemption token; user gets their discount
- You see metrics: how many saw the offer, how many checked in, how many redeemed
Cost: We're testing two modelsβpay $150 for a weekend campaign, or $1 per verified redemption. You pick.
Risk: If it doesn't work, you've only spent the cost of the coupons you already give out. If it works, you have proof for future campaigns.
Upside: Even 10 repeat customers this month is worth it. And you get data on who they are and when they come."
Why This Works β
- Free to try: No upfront cost if you go PPC; low cost if flat campaign
- Proof of value: Real redemptions = real data
- Competitive advantage: Your competitors aren't on Lantern yet
- Timeline: "Help us test this for 90 days" feels like collaboration, not a long-term commitment
Narrative for Users π₯ β
When Growing User Base (Post-Pilot) β
Hook: "Discover independent venues near you and get exclusive offers when you check in. See where your friends are hanging out (Wave). Earn rewards."
Core features:
- Discovery: Browse nearby venues (sorted by relevance, check-ins, your friends)
- Wave: See your friends at venues; join them or be spotted later
- Check-in: One-tap check-in for offers and loyalty
- Rewards: Badges, loyalty streaks, redeemable offers
Why this appeals:
- Social rediscovery: Serendipity element (Wave) missing from passive apps like Instagram
- FOMO: Friends at venues = reason to visit
- Loyalty without friction: No loyalty card; one app for all venues in your city
Success Metrics & Key Questions π β
Pilot Success Criteria (Weeks 0β12) β
β Must achieve:
- 5β10 merchants signed and live
- 50+ verified redemptions across merchants
- Merchant NPS >= 50 or "would pay again" >= 70%
- Fraud rate < 5%
- 1+ merchant cohort renewing offer after 30 days
β οΈ If we miss these: Product-market fit signal is weak; pivot to B2B SaaS or feature licensing.
Unit Economics Targets (Phase 2, post-pilot) β
- CAC: < $1,000/merchant (via referrals + partnership)
- LTV: > $1,200 (ARPU $150/mo, 8-month average retention)
- LTV:CAC >= 1.2 (path to 3:1 with scale)
- Redemption rate: 25β40% of check-ins
User Growth Targets (Phase 2, post-pilot) β
- Monthly active users: 5β10K (San Diego, months 4β6)
- Check-in rate: 5β10% of venue impressions
- Wave adoption: 30%+ of users have friends (indicate stickiness)
Potential Objections & Responses π¬ β
| Objection | Response |
|---|---|
| "Yelp and Groupon already do this" | Yes, but neither prioritizes merchants or has frictionless UX. Yelp clutters users; Groupon breeds resentment. We're merchant-first and app-free (PWA). |
| "Why should merchants pay when Yelp is free?" | Because Yelp is user-focused; Lantern drives action (check-ins, redemptions). You pay for results, not exposure. |
| "What if users don't check in?" | Fair risk. That's why we test with Wave (social discovery) and gamification (badges, streaks) to drive engagement. Pilot will validate. |
| "Fraud risk is high" | Agreed. We combine geofence verification, QR codes, and behavioral heuristics. Pilot will show if fraud exceeds 5% threshold. |
| "How do you compete with TikTok / Instagram?" | We're not; they're passive. We're venue-native: check-in, real-time, social context. Complementary, not competitive. |
| "Isn't this just a loyalty app?" | No. Loyalty is one layer. We're a discovery platform (like Yelp) + social network (like Swarm) + payment/redemption (like Groupon), optimized for venues. |
Investment Narrative (If Fundraising) π― β
Market Size β
- TAM: 1M+ independent hospitality / retail venues globally
- SAM (serviceable): 100K venues in US major metros (10β15 cities)
- SOM (achievable by year 3): 5β10K merchants, $10β50M ARR
Business Model β
- Merchant monetization: Flat campaigns ($150), PPC ($1 per redemption), subscriptions ($50β500/mo)
- Margins: ~90% gross (software), 60%+ operating (post-scale)
- Path to profitability: Positive unit economics by month 18 with 500+ merchants
Funding Ask (Example) β
- Seed (Year 1): $500Kβ$1M for pilot + phase 2 (San Diego, LA, SF)
- Team: Hire 1 merchant success manager, 1 engineer
- Marketing: Targeted merchant acquisition, partner outreach
- Tech debt: Analytics, fraud detection, integrations
- Series A (Year 2): $3β5M for geographic expansion + product features
- 5β10 cities, recurring revenue baseline
- Team: Sales, marketing, customer success
- Product: Merchant SaaS dashboard, payment integration
Why Now? (Timing) β
- Hospitality recovering post-COVID; venues desperate to acquire customers
- PWA technology mature; no app install friction
- Geolocation APIs reliable; hardware ready
- Founder team in market; relationships + credibility
- Competitor weakness: Foursquare declining, Yelp stagnant, Groupon merchant-unfriendly
Next Steps & Immediate Priorities π β
This Week β
- [ ] Confirm partner's top 3β5 merchant targets (personal relationships)
- [ ] Plan outreach: calls, demos, terms discussion
- [ ] Finalize MVP scope: what's built, what's stubbed (see BUSINESS.md, PILOT_STRATEGY.md)
- [ ] Set up Stripe test account; integrate into offer form
Next 2 Weeks β
- [ ] Close 1st merchant; launch offer
- [ ] MVP dashboard live (create offer, view check-ins, CSV export)
- [ ] QR code generation + redemption UX tested
- [ ] Onboarding email template & support runbook
Weeks 3β6 β
- [ ] Close 4+ additional merchants
- [ ] Collect 50+ redemptions; monitor fraud
- [ ] Weekly merchant check-ins; gather feedback
- [ ] Prepare A/B test: flat vs. PPC pricing
Week 12 β
- [ ] Final report: NPS, renewal intent, revenue, churn, fraud
- [ ] Go / no-go decision on phase 2
- [ ] Pitch deck update (if fundraising)
This memo is your founder story. Share it with advisors, potential merchants, and investors. Update it quarterly with pilot data.