Loom Financial Model

Loom lets employees record quick videos (screen + face + voice) and share them asynchronously. Three pillars:

• Effective - easier to explain complex things over video than text
• Efficient - record once, recipients watch on-demand; no scheduling required
• Expressive - face + voice conveys tone and nuance that text cannot

Core pain points addressed: notification overload, scheduling conflicts, low morale in distributed teams. Competes in the space between Slack (text async) and Zoom (sync video), not against them directly.

No explicit TAM/SAM/SOM figures given. Market framing is via video consumption proxies:

• YouTube: 10B hours of video/day
• Snapchat: 10B videos watched/day
• Facebook: 8B video views/day
• Skype: 3B minutes on video/day
• Zoom: 450K total teams

Workplace demand signals:

• 47% of employees are recording more video than before
• 41% the same; only 12% less
• Top driver: easy-to-use tools (75% of surveyed employees)

No dollar TAM, SAM, or SOM stated. No third-party market-size figures cited.

Freemium PLG → per-seat SaaS:

• Go-to-motion: Bottom-up (individual free adoption inside org) → top-down sale to CIO once enough seats are active
• Target buyer: CIO at company with 30+ employees using Loom across 4+ time zones
• CLTV expansion: A la carte add-on features from roadmap + usage-based data caps to drive upsell
• Monetization timeline: "Monetize June 2017" noted on roadmap and pricing slide

Overall growth (Sept 2016 – Feb 2017, slide 5):

• Minutes Recorded/Month: grew from ~30K (Sept) → ~100K (Nov) → ~120K (Dec) → ~270K (Jan) → ~375K (Feb)
• Weekly Active Users: grew from ~750 (Sept) → ~2,750 (Nov) → ~3,000 (Dec) → ~6,000 (Jan) → ~10,500 (Feb)

Activation & retention (slide 6):

• 160% growth in new users sharing a video (leading activation indicator)
• 34% of new users still Weekly Active 18 weeks after sign-up (WAU retention)
• % of sign-ups who share a video: 5% (Nov) → 6% (Dec) → 6.5% (Jan) → 8.5% (Feb) → 11.6% (Mar)

Enterprise account-level adoption (slides 7–8):

• HubSpot: ~35 videos/month (Oct–Jan), spiked to ~90 in Feb; new users grew from ~2 to ~10
• Nielsen: ~180–200 videos/month (Oct–Dec), ~400 in Jan–Feb; new users grew from ~1 to ~15
• Uber: ~12 videos (Oct) → ~40 (Nov) → ~20 (Dec) → ~18 (Jan) → ~52 (Feb); new users 2→7
• Google: ~7 videos (Oct) → ~24 (Nov) → ~13 (Dec) → ~40 (Jan) → ~55 (Feb); new users 2→26

Customer testimonials - usage depth (slide 9):

• Riskalyze (Jeff Beaumont, Director of Support): 945 videos, 45 users; "~350% faster than text and screenshot"
• Nielsen (Jennifer Shin, Senior Data Scientist): 720 videos, 73 users
• HubSpot (Ben Ratner, Head of Growth): 230 videos, 38 users

Pipeline (slide 12):

• LinkedIn (CS & Sales, 1,822 employees): $223,000 ARR potential deal
• HubSpot (entire org, 1,962 employees): $240,000 ARR potential deal
• Both described as Q4 2017 potential deals

• Pricing: $12/seat/month stated as "initial pricing"
• Implied ACV per enterprise deal: ~$120–$124/seat/year × seat count. Example deals imply $223K–$240K ARR for ~1,800–2,000 employee orgs → effective ARPU ~$122–$130/employee/year - but these are potential, not closed deals
• No CAC, LTV, gross margin, or payback period disclosed.

No explicit competitive landscape slide. Positioning implied:

• Loom sits between Slack (text async) and Zoom (sync video) - not a replacement but a complement
• Moat levers mentioned: viral sharing mechanic (recipient watches → signs up), branded pages, audio indexing, transcription, workflow automation on roadmap
• 160% growth in video-sharing activation suggests strong product virality

Team:

• Joe Thomas - CEO, Co-founder. Former Director of Product at MyLife ($27M raised; grew product line from $0 to $600K MRR)
• Vinay Hiremath - Head of Engineering, Co-founder. Former Lead Engineer at Upthere ($77M raised; built video streaming and storage systems)
• Shahed Khan - Head of Design & BD, Co-founder. Age 19 at time of deck; Analyst at Upfront Ventures at 18; Product Designer at Weebly at 18

Funding ask:

• Raising: $3M
• Use of funds: "Monetize with 1,000+ teams and progress towards Enterprise teams"
• No pre-money valuation, existing cap table, or previous raise disclosed

Archetype + why: B2B SaaS PLG model - freemium funnel driving per-seat ARR with enterprise upsell. The motion is bottom-up (viral individual adoption) converting to top-down team/org deals. Best modelled as a cohort-based SaaS ARR model tracking free → paid conversion by cohort, seat expansion, and enterprise deal pipeline separately.

Forecast horizon & granularity:

• 3 years (2017–2019), monthly for Y1, quarterly for Y2–Y3
• Y1 is pre- and post-monetization (monetization begins June 2017)

Key drivers & assumptions:

*Funnel - Free tier:*

• Monthly new sign-ups: ~2,500–3,000 implied from ~10,500 WAUs at 34% 18-week retention
• MoM sign-up growth rate: ~25–30%
• Free-to-paid conversion rate: 5–10%; no paid customers yet as monetization hasn't launched

*Funnel - Paid tier:*

• Seat price: $12/seat/month
• Average team size (SMB): 10–20 seats per team; target of 1,000+ teams implies ~$1.4M–$2.9M ARR at scale
• Target buyer threshold: 30+ employees, 4+ time zones
• Seat expansion rate (net revenue retention): 110–120% - PLG products with usage-based upsell typically expand within accounts
• Monthly churn (paid): 2–3% (logo), consistent with early-stage SaaS with strong retention signals

*Enterprise pipeline:*

• Pipeline ACV: $223K–$240K per deal (LinkedIn, HubSpot examples)
• Enterprise deal close rate: 20–30% of pipeline; deals are still "potential" as of deck date
• Enterprise seat price: negotiated discount to $10/seat/month at scale

*Engagement / usage:*

• Minutes recorded/WAU: ~35–40 min/WAU (implied from ~375K mins / ~10,500 WAUs in Feb)
• Video-share activation rate: 11.6% of sign-ups as of Mar 2017; driver of organic virality

*Cost structure:*

• Gross margin: 65–75% - video storage and transcoding are meaningful COGS for a video SaaS; comparable to early Loom/Wistia benchmarks
• Headcount: 3 co-founders; sales/CS hiring post-raise
• S&M: minimal pre-monetization; primarily organic/PLG; ramps post-June 2017 for enterprise push
• R&D: dominant opex bucket given engineering-heavy team

Scenarios (Base / Bull / Bear - which variables flex):

• Base: 25% MoM sign-up growth; 7% free-to-paid conversion; 115% NRR; 1 enterprise deal closed in Y1
• Bull: 35% MoM growth (viral acceleration); 12% conversion; 130% NRR; 3 enterprise deals in Y1
• Bear: 15% MoM growth; 4% conversion; 105% NRR; 0 enterprise deals close; monetization delayed to Q3 2017

Required sheets / outputs:

1. Assumptions - all drivers in one place, colour-coded inputs
2. Funnel - monthly new sign-ups → WAUs → paid conversions → cohort MRR waterfall
3. ARR Bridge - new ARR, expansion ARR, churned ARR, net new ARR by month
4. Enterprise Pipeline - deal-by-deal tracker with seat count, ACV, close probability, weighted ARR
5. P&L - Revenue, COGS (storage, transcoding, CDN), Gross Profit, S&M, R&D, G&A, EBITDA
6. Cash & Runway - starting cash (post $3M raise), monthly burn, months of runway
7. KPI Dashboard - WAUs, total teams, paid seats, MRR, ARR, NRR, CAC, LTV/CAC, gross margin