SG

Sano Genetics Financial Model

• 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.

• 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.

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.

• 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.

• Deck claims "positive unit economics" and "strong customer retention and expansion".
• No CAC, LTV, gross margin, payback period, or churn figures disclosed.

• 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:

• 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.

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.