# EQL Financial Model B2B SaaS platform managing scarce-product ("hype") launches for retailers - draw/raffle entry capture, fairness algorithm, smart checkout, and analytics. - Canonical: https://finamodel.com/startups/eql - Excel download: https://finamodel.com/startup-models/eql.xlsx - Category: Consumer/DTC - Model type: SaaS ARR / Valuation - Funding round: N/A - Funding: $186M - Founded: 2022 - Geography: Multi-market; 10 markets, 6 languages [DECK]; example customer in New Zealand (Foot Locker NZ). Australia-headquartered (AUD raise). - Customer: B2B2C ## About the company EQL is a B2B platform for retailers running scarce-product and hype launches. It provides draw entry, traffic infrastructure, fairness algorithms, checkout, and analytics without forcing brands to replace their existing commerce stack. The company had run more than 1,000 high-demand launches across 10 markets, analyzing 1.5 million entries and serving more than 720,000 users. Its commercial model is enterprise retail software for brands that need reliable, trusted launch operations. The model is SaaS and launch-volume revenue. Retail customers, launches, entries, platform fees, usage pricing, expansion, and churn determine revenue. Infrastructure capacity, launch outcomes, enterprise sales, and retention drive profitability. ## 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 EQL is a headless commerce layer that sits on top of a retailer's existing stack and manages high-demand product launches end-to-end: - **Hype Infrastructure**: custom domains and purpose-built stack to absorb traffic spikes without crashing the retailer's core site. - **Fairness Algorithm ("EQLizer")**: proprietary draw/raffle engine that boosts odds based on loyalty, proximity, consecutive losses, and other ingestible data points. Trademark: "Run Fair®". - **Smart Checkout / Payments Connect**: linear, controlled payment processing; plugs into existing payment gateway; retailer remains merchant of record. - **Launch Automation**: end-to-end managed service - product page creation, entry capture, insights, and payments. Dedicated account management. - **Hype Analytics**: real-time demand and customer-insight dashboard across launches. - No-integration required; deployed in days. ## Market - Global streetwear market: $185B in sales - Resale / circular economy: $25–30B - Adjacent categories cited but not sized: luxury goods, collectibles, beauty, travel, tickets, NFTs, digital assets - No SAM or SOM presented. No growth rate given for the platform's addressable market. ## Traction & metrics All from slide 2: - Launches run: >1,000 high-heat launches - Markets: 10 markets, 6 languages - Entries analysed: >1.5M via proprietary fairness algorithm - Users served: >720K (delivered Run Fair® launches to) - Funding raised: AUD $25M (Insight Partners + Airtree Ventures) - Headcount: 21 staff (large engineering focus), targeting ~40 by end of 2022 No revenue, ARR, GMV, churn, NRR, or launch-fee figures disclosed. ## Competition / moat Not explicitly addressed in a dedicated slide. Implied moats: - Proprietary "Run Fair®" certified fairness algorithm (EQLizer) - differentiated vs generic raffle plugins. - 1,000+ launches of operational data and ML training data from 1.5M+ entries - No-integration headless architecture reduces friction vs platforms requiring deep re-platforming - Named brand customers (Foot Locker, Crocs) act as social proof / switching-cost moat ## Team & funding ask / use of funds - Investors: Insight Partners, Airtree Ventures - Total raised to date: AUD $25M - Current headcount: 21 (large engineering focus) → target ~40 by end of 2022 - Founding story: originated from a 2019 Nike/Jordan x Maison Chateau Rouge collab launch ("Fearless collection") --- ## Recommended financial model **Archetype + why:** B2B SaaS / per-launch fee hybrid model. EQL charges retailers to run launches on its platform; revenue is most likely a combination of (a) recurring SaaS platform fee and/or (b) per-launch fee, with a potential managed-services layer for account-managed customers. No pricing is disclosed, so the model must be structured to flex around both monetisation assumptions. **Forecast horizon & granularity:** - 3–5 years; monthly for Year 1–2, quarterly for Years 3–5. - Key unit: launches per month × revenue per launch (or ARR per customer if subscription pricing is assumed). **Key drivers & assumptions:** | Driver | Value | Source | | -- | -- | -- | | Launches to date | >1,000 | - | | Active markets | 10 | - | | Users in platform | ~720K | - | | Headcount at model start | ~21–40 | - | | Engineering % of headcount | ~60–70% | typical early-stage infra SaaS; deck emphasises "Large Eng focus" | | Revenue per launch | AUD $500–$2,000/launch; no pricing disclosed; enterprise SaaS comps suggest higher per-customer ACV with volume discounts | | Launches/month growth | 15–25% YoY; based on 1,000+ cumulative across ~3 years implying ~30–50/month current run rate | | Gross margin | 65–75%; SaaS infrastructure business; managed-services component may compress to 50–60% blended | | CAC | high (enterprise/mid-market motion); 6–12 month sales cycle; no data given | | Churn | low (5–10% annual logo churn) given brand-name customers and account management; no data given | | S&M as % of revenue | 25–35% (growth stage, account-managed) | | R&D as % of revenue | 30–40% (large eng focus, pre-scale) | | G&A as % of revenue | 10–15% | **Scenarios (Base / Bull / Bear - which variables flex):** - **Base**: launches grow ~20% YoY; revenue per launch stable; managed-services revenue included; gross margin 65%. - **Bull**: category expansion (luxury, beauty, NFTs materialises); launch volume +35% YoY; new market entry accelerates; ACV increases as platform matures. - **Bear**: retail slowdown reduces hype launch frequency; commoditisation of raffle tech compresses pricing; account-managed costs stay elevated; gross margin compresses to ~50%. **Required sheets / outputs:** 1. Assumptions & Drivers 2. Revenue Build (customers × launches × revenue/launch or ARR × NRR) 3. P&L / Income Statement (3-statement if investor-facing) 4. Headcount plan (engineering-heavy; ties to opex) 5. Cash & Runway (bridge from AUD $25M raise) 6. Sensitivity table (revenue/launch vs. launches/month; or ACV vs. logo growth) 7. KPI dashboard (launches/month, total users, revenue per launch, gross margin) ## Frequently asked questions ### Is the EQL financial model free? Yes. The EQL 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.