# Lola.com Financial Model Corporate travel management SaaS for SMBs (20–500 employees), combining subscription software with transactional booking revenue - Canonical: https://finamodel.com/startups/lolacom - Excel download: https://finamodel.com/startup-models/lolacom.xlsx - Category: Enterprise/Security - Model type: SaaS ARR / Valuation - Funding round: Series C - Funding: $37M - Founded: 2019 - Geography: US primary; product used globally (business travel context) - Customer: B2C ## About the company Lola.com is corporate travel-management software for SMBs with roughly 20 to 500 employees. It combines travel workflow software with booking functionality, giving companies a simpler way to manage employee travel and related policy controls. The business has two revenue streams: subscription software and transactional booking revenue. Its American Express Global Business Travel partnership also creates a distinct channel-sourced customer cohort, while long-term margin ambition reflects a software-led travel platform rather than a pure agency. The model separates logo count times subscription ARPU from booking volume times take rate. It tracks direct and channel customers, retention, travel volume, supplier economics, gross margin, sales investment, and overhead to show the combined SaaS and transaction model. ## What's included - 5-year monthly revenue build with stage-appropriate growth assumptions - Full P&L, headcount plan, and operating-expense schedule - Cash-flow statement, runway, and burn-rate tracking - Valuation via exit multiple with a DCF cross-check - Returns analysis with MOIC and IRR - Unit economics including CAC, LTV, payback, and cohort retention ## Product & value proposition Three user personas served by one platform: - Finance & Operations: control + visibility over travel spend - Executive Assistants: save time, peace of mind on bookings - Business Travelers: 24/7 support, fast booking experience Core product principles: extreme ease of use, speed, gorgeous UI/brand, collaboration. Positioned as "easiest way to book and manage travel." Key differentiator: product-led growth - near-zero onboarding/account management team required, with customers self-activating and booking rapidly in Q4 2018. ## Market - $1.3 trillion spent on business travel worldwide - 600,000 US companies with 20–500 employees - the target customer segment - Primary channel used by these companies today: consumer travel sites (unmanaged travel) - $6B TAM in US alone, at $10,000 annual revenue per customer ## Revenue model - Dual revenue streams: SaaS subscription + transactional revenue - Subscription pricing implemented ~mid-2018 (listed as milestone 6 months prior to Q1 2019 deck) - Target long-term gross margin: 70%+ (described as "SaaS business" economics) - Go-to-market: inside sales + partner reselling - Key channel partner: American Express Global Business Travel (GBT) - 5-year mutually exclusive commercial partnership; GBT re-sells Lola.com to its customers and prospects - GBT context: largest B2B travel agency with $45B in annual travel bookings - Revenue per customer assumed ~$10,000/year ## Traction & metrics All specific traction numbers were redacted (shown as "X") in this version of the deck: - Number of companies closed on subscription: $X - Subscription ARR: $X - Total revenue run rate (subscription + transactional): $X - Dashboard slide (slide 16): showed Customers, Estimated Revenue (Subs + Trans), and # Travel Bookings (Hotels + Flights) charts - all chart bodies blank/redacted - Product-led success metrics (Q4 2018, all redacted): XX% of customers booked travel; XX% booked within 15 days; XX% within 60 days; XX% booked multiple times/week - G2Crowd ratings: Lola.com 4.8 stars vs TravelPerk 4.7, TripActions 4.6, Concur 4.0 ## Unit economics - Target long-term gross margin: 70%+ - Implied ARPU: ~$10,000/year (derived from TAM math) ## Competition / moat Competitive landscape: - Concur (SAP, enterprise-focused, 4.0 G2 rating - positioned as legacy/complex) - TripActions (raised $154M at $1B in 2018) - TravelPerk (raised $44M in 2018) Moat claims: - Founder pedigree: CEO built HubSpot (Mike Volpe); CTO built KAYAK (Paul English) - Product ratings: highest on G2Crowd at 4.8 stars - AMEX GBT exclusive partnership (5-year, mutually exclusive) - structural distribution advantage and barrier to competitor entry via that channel - Product-led growth driving adoption with minimal sales/CS overhead ## Team & funding ask / use of funds Team: - Mike Volpe, CEO (former HubSpot) - Paul English, CTO (founder of KAYAK) - Krista Pappas, SVP Business Development (Farecast → Microsoft) - Jeanne Hopkins, CMO (Ipswitch, HubSpot) - Ryan Ball, VP Sales (CarGurus, HubSpot) Round: Series C --- ## Recommended financial model - **Archetype + why:** SaaS ARR model with transactional revenue overlay. The business explicitly describes itself as a "SaaS business" with dual streams (subscription + transactional). The core drivers are logo count × ARPU (subscription) plus bookings volume × take rate (transactional). The 70%+ long-term GM target is a SaaS gross margin profile. The AMEX GBT partnership makes channel-sourced ARR a distinct cohort worth modelling separately. - **Forecast horizon & granularity:** Monthly for Year 1, quarterly for Years 2–3; 3-year forward from Q1 2019 base. Series C context means investors will want to see path to scale/profitability horizon. - **Key drivers & assumptions:** *Subscription revenue:* - Starting customer count: unknown - redacted; placeholder of ~100–300 customers given early-stage ARR and Q4 2018 momentum - Monthly new customer adds - direct sales: ramp from ~10–20/month in Q1 2019, scaling with sales headcount additions - Monthly new customer adds - GBT channel: separate cohort; ramp lags deal signing (partnership signed late 2018); assume 20–30% of new logos via channel by end of Year 1 - Average contract value (ACV): ~$10,000/year based on TAM math ($6B TAM ÷ 600K companies) - Logo churn rate: 5–10%/year (early-stage SaaS benchmark; no deck data) - Net revenue retention: 105–115% (seat expansion as companies grow travel) *Transactional revenue:* - Bookings per active customer per month: mix of hotel + flight bookings; customers booking multiple times/week in Q4 implies high frequency for active users - Average booking value: ~$500–800 per booking (blended hotel/flight) - Take rate / net revenue on transactional: 3–8% of booking value (typical GDS/agency economics) *Gross margin:* - Subscription gross margin: 75–80% (target 70%+ per deck; subscription software COGS = hosting + support) - Transactional gross margin: 20–35% (GDS fees, supplier costs, 24/7 support ops) - Blended GM ramps toward 70%+ as subscription mix grows *Operating expenses:* - S&M: 50–60% of revenue in early years; declining as GBT channel scales (leverage partner distribution) - R&D: 25–35% of revenue (product-led strategy requires continuous investment) - G&A: 10–15% of revenue - **Scenarios (Base / Bull / Bear - which variables flex):** - Base: steady inside sales ramp + GBT channel contributing ~25% of new logos by Year 2; churn ~8%; ACV $10K - Bull: GBT channel outperforms (40%+ of new logos), ACV expansion through upsell/seat growth, churn <5% - Bear: GBT ramp slower than expected, direct sales headcount lags, higher churn in early subscription cohorts (product-market fit still maturing) - **Required sheets / outputs:** 1. Assumptions dashboard (all drivers in one place, colour-coded) 2. ARR waterfall (direct vs. GBT channel cohorts; new / expansion / churn / net new ARR) 3. Transactional revenue schedule (bookings volume × take rate) 4. P&L (blended revenue, COGS by stream, gross profit, opex, EBITDA, net income) 5. Headcount plan (sales, CS, R&D, G&A - key cost driver) 6. Cash flow & runway (to size Series C proceed requirements) 7. KPI summary (ARR, logo count, NRR, CAC payback, LTV/CAC, burn rate) 8. Scenario toggle (Base / Bull / Bear) ## Frequently asked questions ### Is the Lola.com financial model free? Yes. The Lola.com model is a free Excel download with live formulas. ### Can I change the assumptions? Yes. The workbook is editable and its live formulas recalculate when assumptions change.