# Sano Genetics Financial Model SaaS + transactional platform connecting pharma/biotech with patients and biobanks/registries for personalized medicine recruitment and data access. - Canonical: https://finamodel.com/startups/sano-genetics - Excel download: https://finamodel.com/startup-models/sano-genetics.xlsx - Category: Biotech/Pharma - Model type: SaaS ARR / Valuation - Funding round: Series A - Founded: 2022 - Geography: UK-founded; customers in US, UK, Australia [DECK slide 5]. - Customer: B2B2C ## About the company Sano Genetics connects people, biobanks, registries, and pharma companies for personalised-medicine studies. Individuals can register, receive sponsored DNA testing and reports, and match with research, while biobanks use white-label or integration tools and sponsors use a platform for recruitment through Phase 2, Phase 3, and observational work. Revenue includes recurring SaaS subscriptions from pharma and biotech, volume or performance fees for testing and recruited patients, and revenue sharing with partner registries and biobanks. The private-by-design model gives participants consent control, although the deck does not disclose pricing, ACV, per-sample fees, or customer numbers. Sano reports revenue doubling every six months, more than 1.1 million people in its genetic-data network, 20-plus programmes powered, and customers in the U.S., U.K., and Australia. The model should forecast sponsors, studies, patients, platform ARR, recruitment fees, data cost, expansion, and retention. ## 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 - Patient-facing app: individuals register, take a DNA test (free, sponsored), get genetic reports, and match with personalized medicine research studies. - Biobank/registry-facing: white-label or integration layer that delivers a superior user experience and enables revenue-sharing with biobank partners. - Pharma/biotech-facing: SaaS platform for patient recruitment across R&D, observational studies, Phase 2, and Phase 3 clinical trials; DNA testing and recruitment services on volume/performance basis. - "Private-by-design" data model - patient controls consent. - Vision: digital access to >1 billion people with rich omics and medical data. ## Market - TAM cited as $1T opportunity for personalized medicine. - Personalized medicines on track to surpass non-personalized in next 5 years. - 80% of clinical trials are delayed; delay cost $600k–$8M per drug per day to pharma. - Governments, non-profits, and private sector spending billions per year on genomics datasets. - No SAM/SOM breakdown or segment sizing in deck. ## Revenue model Three stated revenue streams: 1. **Monthly recurring revenue (MRR/SaaS)** - platform subscription charged to pharma/biotech clients. 2. **Volume and performance-based revenue** - DNA testing kits and patient recruitment fees; leverages partner network. 3. **Revenue-share** - with patient registries and biobanks that opt into the Sano partner network. No pricing tiers, per-seat, or per-sample dollar figures disclosed in the deck. ## Traction & metrics - Revenue doubling every 6 months. - Revenue chart (slide 10): actual bars shown for H1 2020, H2 2020, H1 2021; H2 2021 is projected (taller teal bar). No Y-axis values visible - absolute revenue figures not disclosed. - Pharma and population-scale biobank customers in US, UK, Australia. - >1.1M people in genetic data network. - 20+ personalized medicine research programmes powered. - 83% NPS-proxy: users would recommend Sano to family and friends. - No ARR, MRR, customer count, or ACV figures disclosed. ## Unit economics - Deck claims "positive unit economics" and "strong customer retention and expansion". - No CAC, LTV, gross margin, payback period, or churn figures disclosed. ## Competition / moat - Incumbents built on 1980s-era fragmented infrastructure. - Moat implied by: (a) three-sided network effect (patients, biobanks, pharma), (b) >1.1M people already enrolled, (c) 20+ active research programmes, (d) proprietary omics + medical record linkage layer. - No direct competitors named in deck. ## Team & funding ask / use of funds **Team**: - 20 people total: 6 Tech, 5 Marketing & Partnerships, 3 Commercial, 2 Product, 2 Operations, 2 Project Management. - CEO & Co-founder: Patrick Short (Sanger Institute, Cambridge). - COO & Co-founder: Charlotte Guzzo (JP Morgan, Cambridge). - CTO & Co-founder: William Jones (EBI, Cambridge). - Head of Product: Mike Allen (Push Doctor, GSK). - Head of Marketing: Lauren Wong (HelloFresh). - Head of Precision Enrolment: Liam Eves (hVIVO). **Ask**: - Raising $11M. - Use of funds: (1) grow team in US and Europe; (2) triple revenue YoY; (3) sign flagship partner in each of 6 key international markets; (4) expand platform to 50+ diseases and 6+ countries by mid-2023; (5) raise Series B. - No prior round or current valuation disclosed. --- ## Recommended financial model **Archetype + why:** Multi-revenue-stream SaaS + transactional marketplace model. The business has two distinct revenue layers: (1) recurring SaaS/platform fees (predictable, high-margin), and (2) volume/performance-based transactional revenue from DNA testing and recruitment (variable, lower-margin, scales with programmes). A single-statement SaaS ARR model undercounts the business; a blended SaaS + services/transactional P&L with a 3-statement output is appropriate. **Forecast horizon & granularity:** - H1/H2 semi-annual actuals already exist (H1 2020 – H1 2021); model should match that cadence then switch to quarterly from Q1 2022, with annual summary through 2025. - Series A close assumed ~end of 2021; Series B milestone targeted mid-2023 per deck. **Key drivers & assumptions:** *Revenue - SaaS/Platform:* - Number of pharma/biotech clients - Average contract value (ACV) per client - Net revenue retention / expansion rate - New logo adds per half *Revenue - DNA Testing & Recruitment (transactional):* - Programmes enrolled per period - Average participants recruited per programme - Revenue per participant recruited *Revenue - Biobank Revenue-Share:* - Number of biobank/registry partners - Revenue-share % of partner programme fees *Cost structure:* - Headcount: 20 today; post-raise hiring plan targets US + EU expansion - Salary mix - COGS for DNA kits - Sales & marketing spend - R&D - G&A *Network / data metrics:* - People in genetic data network; growth rate - Diseases covered **Scenarios (Base / Bull / Bear - which variables flex):** - **Base:** Revenue doubles every 6 months (as per deck) through 2022; moderates to 2x/year thereafter. - **Bull:** Series B closes on schedule (mid-2023); 2 flagship partners per market rather than 1; ACV expansion 20% above base. - **Bear:** Growth slows to 50% annual; one market fails to generate a flagship partner; DNA testing volumes disappoint (clinical trial delays persist beyond incumbent). **Required sheets / outputs:** 1. **Assumptions** - all drivers above with / tags. 2. **Revenue build** - separate tabs/sections for SaaS MRR, transactional recruitment, biobank revenue-share; roll up to total revenue. 3. **Headcount plan** - by department (mirrors deck breakdown: Tech, Mktg/Partnerships, Commercial, Product, Ops, PM). 4. **P&L (Income Statement)** - quarterly, 2020A–2025E; gross margin and EBITDA highlighted. 5. **Cash & runway** - starting from $11M raise; monthly burn rate; months-to-Series-B. 6. **KPI dashboard** - ARR, net new ARR, NRR, patients enrolled, active programmes, biobank partners. 7. **Scenario toggle** - Base / Bull / Bear switching on revenue growth rate, ACV, and new logo pace. ## Frequently asked questions ### Is the Sano Genetics financial model free? Yes. The Sano Genetics 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.