# Kero Sports Financial Model B2B2C whitelabel SDK that delivers algorithmically curated in-game micro-bets (every 30–60 seconds) plus social features, deployed inside sportsbook and media partner apps. - Canonical: https://finamodel.com/startups/kero-sports - Excel download: https://finamodel.com/startup-models/kero-sports.xlsx - Category: Media/Gaming - Model type: SaaS ARR / Valuation - Funding round: Seed - Funding: $1M - Founded: 2022 - Geography: North American focus implied (NHL/NBA/MLB game examples, Canadian teams) [DECK slide 3]. - Customer: B2C ## About the company Kero Sports offers a whitelabel SDK that sportsbooks, teams, leagues, and media networks can embed in their own apps. Proprietary machine-learning models present curated in-game micro-bets roughly every 30–60 seconds, with social chat designed to extend engagement between betting moments. The product serves both real-money betting and a free-to-play leaderboard mode. The free layer brings casual fans into a funnel that can convert them to sportsbook customers, while the SDK gives partners first-party behavioural data and a way to activate viewers already watching sport. The deck does not state commercial terms or operating traction; its visible bet-pool figures are interface examples, not performance data. A defensible model should therefore separate partner licences or revenue shares, free-to-play conversion or referral economics, and potential data upsells, with client launches and event coverage as core drivers. ## 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 - Whitelabel SDK embedded into clients' (sportsbooks, teams, leagues, networks) existing apps. - Proprietary ML models generate curated in-game bets on ~30–60 second intervals. - Two modes: real-money sportsbook betting and free-to-play (FTP) leaderboard/points mode to reach casual fans. - Social chat layer between bets to drive engagement and retention. - Funnel logic: All Sports Fans → Those Watching → FTP Users → Sportsbook Clients - Kero claims to serve the entire funnel. ## Revenue model Not explicitly stated in deck. Inferred from context: - B2B licensing / revenue-share with sportsbook, team, league, and media network clients - standard for whitelabel igaming SDK vendors. Rationale: product is described as a "whitelabel SDK deployed in the apps of our clients" with no direct-to-consumer monetization cited. - Possible data/analytics upsell on first-party fan behavior data generated through the platform. Rationale: slide 5 references "first party data" as a conversion driver for sportsbook clients. - FTP mode likely monetized via referral/affiliate fees to sportsbooks when FTP users convert to real-money accounts; consistent with industry norm for FTP-to-real-money funnel products. ## Traction & metrics Sample in-game numbers visible on slide 3 are illustrative UI mockups (e.g., "YES $27,790 / NO $19,270" bet pool sizes; "$9.47" win amount) - not traction figures. ## Competition / moat - Moat claim: proprietary ML models for real-time in-game bet curation; first-party behavioral data flywheel. - Differentiation framing: incumbents (sportsbooks) are positioned as failing to engage the next generation; Kero positions itself as the engagement layer they lack. - Client-side pain points addressed: sportsbooks need differentiation beyond price; teams/leagues/networks need fan monetization and engagement strategies. ## Team & funding ask / use of funds Team: - Tomash Devenishek - Founder & CEO; 15+ years senior tech exec; previously built Coachella app gamification engine; bootstrapped Kero for 2 years; formerly founded a blockchain P2P sports betting exchange. - Tom Gray - VP of Revenue; 10+ years sports & media sales; joined from OpenBet; formerly Senior Manager of Sports Partnerships at Sportradar. - Rustin Domingos - Chief Data Scientist; UC Berkeley EPS PhD Candidate; MIT Physics PhD Candidate; 2 peer-reviewed publications; built predictive algorithms for NBA, MLB, NHL. --- ## Recommended financial model - **Archetype + why:** B2B SaaS / revenue-share SDK model. Kero's economics are driven by enterprise client contracts (sportsbooks, leagues, networks) - not direct consumer volumes - making a contract-count × revenue-per-client ARR model the right spine. A secondary FTP-to-real-money conversion affiliate layer can bolt on as a separate revenue line. - **Forecast horizon & granularity:** 3 years monthly (Year 1–2 monthly detail; Year 3 quarterly roll-up). Pre-revenue / pre-launch stage warrants monthly granularity to track client onboarding pipeline and burn clearly. - **Key drivers & assumptions:** *Client acquisition & contract* - Number of signed B2B clients (sportsbooks, teams/leagues, media networks) by quarter - zero disclosed; start from 0 with ramp assumption. - Average annual contract value (ACV) per client type - sportsbooks likely higher ($200K–$500K ACV range) vs. teams/media ($50K–$150K); no deck data. - Revenue-share % of GGR (gross gaming revenue) generated through SDK - industry range 10–25% of incremental GGR; alternative to flat ACV. - Sales cycle length ~6–12 months for regulated gaming operators; affects revenue recognition timing. - Client churn rate ~10–15% annually; typical early-stage B2B SaaS. *FTP / affiliate layer* - FTP monthly active users (MAU) - not in deck; modeled as a function of client installs × engagement rate. - FTP-to-real-money conversion rate ~5–10% industry benchmark. - Affiliate CPA per converted bettor ~$100–$200 per acquired depositing player; standard igaming affiliate rate. *Cost structure* - Headcount: engineering (ML/data science heavy), sales, client success - 3 named team members currently; model headcount ramp as clients sign. - Cloud / infrastructure costs for real-time ML inference at 30–60 second intervals - significant; scale with active users per game event. - R&D / model training costs. - No COGS for physical inventory (pure software). - **Scenarios (Base / Bull / Bear - which variables flex):** - Bear: Slow client onboarding (1–2 signed in Year 1); lower ACV; FTP conversion below 5%. - Base: 3–5 clients in Year 1 growing to 10–15 by Year 3; ACV mid-range; FTP layer contributing ~20% of revenue by Year 3. - Bull: Major sportsbook anchor client signed early (DraftKings / FanDuel tier); rapid distribution via league deals; FTP flywheel accelerates conversions. - **Required sheets / outputs:** 1. Assumptions dashboard (all drivers clearly flagged, easily toggled per scenario) 2. Client pipeline & ARR build (client count × ACV, monthly new / churned / expansion ARR) 3. FTP/affiliate revenue model (MAU funnel → conversion → CPA revenue) 4. P&L (Revenue, Gross Profit, OpEx by category, EBITDA, Net Income) 5. Headcount plan (by function, with salary + benefits) 6. Cash flow & runway (monthly burn, cash balance, implied runway to next raise) 7. Summary KPIs: ARR, clients, burn rate, months of runway, LTV/CAC (once data available) ## Frequently asked questions ### Is the Kero Sports financial model free? Yes. The Kero Sports 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.