HumanForest Financial Model

• Dockless electric e-bikes available across London.
• Core hook: 20 free minutes per day for every user.
• After the free allowance: users pay for additional minutes, OR unlock continued free riding by engaging with a partner brand's offer (buy a product/service).
• Three platform pillars sold to corporate partners:

1. Digital Marketing & Loyalty - sponsored rides, geo-targeted push/in-app notifications, loyalty programmes, 14 communication channels.
2. ESG & Social Purpose - carbon offsetting, brand association, employee wellbeing.
3. Mobility Data - real-time partner dashboard, ML-based consumer segmentation and behavioural predictions.

• Current daily cycling trips in London: +700k.
• Potential daily cycling trips in London: +4.3m.
• 80% of transport air pollution caused by cars.
• Global capital investment in mobility (2015–2019) by broad category:
• Shared-Mobility Services: $211bn invested 2015–2019.
• Micro-mobility sub-sector: grew from $0.1bn (2010–14) to $14.1bn (2015–19), 5Y CAGR +176% - the fastest-growing segment in the chart.
• Ride-hailing: $81.3bn (2015–19).
• Last-mile logistics: $67.7bn (2015–19).
• No explicit TAM/SAM/SOM breakdown provided for e-bike sharing or the partner advertising market.

Two revenue streams described:

Stream 1 - Corporate partnership / sponsored rides (B2B)

• Partner companies pay HumanForest to "fund" the 20 free daily minutes for users.
• Partners receive access to the Digital Marketing, ESG, and Data platform in exchange.
• Pricing benchmark given: "HumanForest monthly investment [for a partner] is equal to 15 billboards (96 Sheets), or 1–3 newspaper full-page ads, or 3–5 × 30-second TV ads".
• No explicit £/month or CPM pricing disclosed.
• Sector exclusivity offered to partners.

Stream 2 - Pay-per-minute rides (B2C)

• Users who exhaust their 20 free minutes can buy additional time.
• No per-minute rate or pricing tier disclosed in deck.

• Dashboard screenshot shows a pilot/demo dataset dated 08/05/2012 → 12/31/2019 (likely sample data, not live operational figures):
• Daily Active Users: 2
• Weekly Active Users: 4
• Monthly Active Users: 23
• KG of CO2 Avoided (gauge): 43.2 kg
• No disclosed revenue, ride counts, fleet size, active partner count, or customer acquisition data.
• The low DAU/WAU/MAU numbers in the dashboard are almost certainly demo/placeholder data rather than real operational scale.

• Deck positions against traditional offline marketing (billboards, newspaper ads, TV) as the benchmark for partner spend - implying the moat is a lower-cost, more measurable, greener alternative channel for brands.
• No direct competitor e-bike operators (Lime, Santander Cycles, Dott, Tier) named or benchmarked.
• Implied moats: first-mover in ad-funded free-ride model in London; proprietary mobility data & ML platform; sector exclusivity for partners; ESG / brand-purpose positioning.
• No IP, regulatory exclusivity, or network effect data disclosed.

• Team: "Experienced management team" cited as investment rationale. No names, backgrounds, or org chart shown.
• Investment thesis bullet points:

1. High-growth sector positioning.
2. Quickly scalable (London → world).
3. Positive EBITDA & high returns (aspirational, not evidenced).
4. Multiple income streams.
5. Large addressable market.
6. Experienced management team.
7. "It is now the opportunity."

• Archetype + why: Marketplace / platform P&L with two revenue lines - (a) B2B partnership subscription/sponsorship revenue and (b) B2C pay-per-ride revenue - layered on top of a shared micro-mobility operations model (fleet capex, maintenance, charging, rebalancing). This is closest to a two-sided marketplace + ad-platform hybrid, not pure SaaS. An operating 3-statement model is appropriate because fleet capex and working capital are material.

• Forecast horizon & granularity: 5 years (Year 1–5); monthly for Year 1–2, quarterly thereafter. Monthly is needed to track fleet ramp, partner onboarding, and seasonality (London cycling peaks in spring/summer).

• Key drivers & assumptions:

*Demand / ride volume*

• Active fleet size (# e-bikes deployed): start at ~500 bikes, scale to 5,000+ by Year 3 based on London micro-mobility norms for a pilot operator.
• Average rides per bike per day: 3–5 rides/bike/day (industry benchmark for dockless e-bikes in European cities).
• Average ride duration: ~20 min (aligned with free tier; B2C top-up portion likely shorter).
• % rides that are free-only (no top-up): 70–80%; remainder generate B2C revenue.

*B2C revenue*

• Pay-per-minute rate: £0.12–£0.15/min (consistent with London dockless competitors at time of deck).
• Average top-up minutes per paying ride: 10 min → ~£1.20–£1.50 per paying ride.

*B2B partnership revenue*

• Number of active brand partners: 5 in Year 1, scaling to 20+ by Year 3.
• Monthly fee per partner: £15,000–£25,000/month, anchored to the billboard/newspaper/TV ad benchmarks shown in slide 11 (15 billboards ≈ typical £15k–£20k/month in London outdoor media).
• Sector exclusivity premium: small uplift for exclusive categories.

*Operating costs*

• Fleet capex per bike: £1,500–£2,000 (cargo e-bike with IoT).
• Maintenance/repair per bike per year: 15–20% of capex.
• Charging & logistics (rebalancing) per bike per day: £2–£4.
• Depreciation: 3-year straight-line on bikes.
• Staff (ops, tech, sales): ramp from ~10 FTE Year 1.
• Insurance per bike per year: £200–£300.
• Platform/tech hosting: modest SaaS-tier cloud costs.

*London market size ceiling (from deck)*

• Current daily trips: 700k - HumanForest addressable share 1–5% initially.
• Potential daily trips: 4.3m - long-run bull case ceiling.

• Scenarios (Base / Bull / Bear - which variables flex):
• Bear: Slow partner sign-up (3 partners Year 1), low rides/bike/day (2.5), high churn of partners, no geographic expansion beyond London in 5 years.
• Base: 5 partners Year 1 → 15 by Year 3; 4 rides/bike/day; 500 bikes Year 1 → 3,000 Year 3; one international city by Year 4.
• Bull: Rapid partner pipeline (10 partners Year 1 from strong ESG demand); 6+ rides/bike/day; 1,000 bikes Year 1; two additional cities by Year 3; premium data product upsell.

• Required sheets / outputs:

1. Assumptions - all drivers centralised, toggle for scenario.
2. Fleet model - bike count ramp, capex schedule, depreciation.
3. Revenue build - B2B partner revenue (partner count × monthly fee) and B2C ride revenue (rides × paying % × avg top-up value), monthly.
4. Opex build - staffing, charging, maintenance, insurance, platform costs.
5. P&L (Income Statement) - gross profit by revenue stream; EBITDA bridge.
6. Balance sheet - fleet assets, working capital (partner receivables).
7. Cash flow statement - capex timing critical (bike purchases front-loaded); operating cash flow.
8. KPI dashboard - rides/day, active partners, revenue per bike, EBITDA margin, CO2 offset (ESG reporting metric for partners).
9. Scenario toggle - Bear / Base / Bull switcher feeding all sheets.