VE

Verishop Financial Model

• For brands: sell on Verishop and partner networks; manage business via "Dash" dashboard; brand profile/storytelling; collective data insights; returns software; image management; negotiated vendor rates; micro-financing
• For consumers: AI-driven "Explorer" feed browsing $1B+ inventory across 3,000+ brands; two-click purchase; no site redirects; Livestream Shopping with in-stream purchase; "Shop Party" (patented)
• Platform extensions: Snap Mini integration; Instagram, YouTube, shoppable video distribution
• Technology moat: proprietary recommendation engine (computer vision AI); patent-pending Live Shopping; patented Shop Party; automatic video editing + product tagging; merchant onboarding in <30 minutes

• 972K+ retail businesses created in 2021
• 91% of US shoppers like to try new brands/products; 36% "love to try new brands" (US), 42% globally; 55% "sometimes try" (US), 49% globally

Revenue streams mentioned:

1. Transaction / take-rate: brands sell on Verishop; commission on GMV implied (rate not disclosed)
2. Partner network distribution: brands sold across partner networks (Snap, Instagram, YouTube); revenue share implied
3. SaaS / software services: "Dash" merchant management tool; returns software; image management; Creator Studio (onboarding, product matching, stylist program, monetization, reporting) - pricing not disclosed
4. Data / insights: collective data insights sold or bundled - pricing not disclosed
5. Micro-financing: described as a service offering; economics not disclosed
6. Negotiated vendor rates: implied referral/rebate revenue

No explicit pricing tiers, take-rate percentages, or SaaS ARR figures disclosed.

All from slide 15 ("Verishop By the Numbers"), as of ~May 2022:

• 3,000+ brands on platform
• 400K+ SKUs
• $1B+ GMV inventory value on platform
• 99.5% merchant retention (LTM average ending May 2022)
• NPS = 70 (survey July 1, 2019 – May 1, 2022)
• 85 full-time employees, 40%+ in tech
• 1,000+ hours of livestream video content library

• Competitive context: fragmented independent brand landscape; brands currently discovered via social feeds with poor shopping UX
• Moat claims:
• Patented "Shop Party" technology
• Patent-pending Live Shopping
• Proprietary AI recommendation engine (computer vision)
• 99.5% merchant retention - high lock-in via services and data
• Network effect: more brands → better discovery → more consumers → more brands
• 1,000+ hours of auto-generated video content library (unique asset)
• Direct competitors not named in deck.

• Archetype + why: Marketplace GMV model with SaaS/services layer. Verishop is asset-light, multi-revenue-stream: the primary engine is GMV (brands transacting on platform) with a blended take-rate, layered on top of recurring software/services revenue (Dash, Creator Studio, data insights) and a micro-financing spread. A pure 3-statement model is secondary; GMV → revenue → contribution margin is the core.

• Forecast horizon & granularity: 5 years (2023–2027); quarterly for years 1–2, annual for years 3–5. Given early-stage traction data (May 2022 as base), monthly cohort detail is optional but useful for merchant onboarding curve.

• Key drivers & assumptions:
• Active brands on platform - base: 3,000; growth: +30% Y1, decelerating to +15% by Y5 (marketplace S-curve; high existing retention supports base)
• Merchant retention rate - 99.5%; held flat (best-in-class; sustaining it is a key model risk)
• Average GMV per brand per year - ~$33K/brand implied if total inventory is $1B across 3,000 brands (note: $1B is stated inventory value, not transacted GMV; actual GMV likely a fraction - assume 30–50% sell-through rate = ~$10K–$16K transacted GMV/brand/year)
• Blended take-rate - 15–20% of transacted GMV (typical for fashion/lifestyle marketplace; Verishop rate not disclosed)
• SaaS/services ARPU per brand - $500–$1,500/year/brand for Dash + data + returns software bundle (no pricing disclosed)
• Consumer count & AOV - Not in deck; model as derived: GMV / AOV = orders; AOV $60–$80 for lifestyle/apparel
• Micro-financing revenue - small (1–3% of GMV as origination fee); model as optional add-on
• Content/platform distribution revenue - small initially; may grow as Snap Mini and shoppable video scale
• Headcount and opex - base: 85 FT employees; scale ~20% Y1, slower thereafter; tech = 40%+ of headcount, so R&D heavy
• Gross margin - 60–75% on SaaS/services revenue; 40–55% blended (marketplace platforms carry customer service, payment processing, and partial fulfillment support costs)

• Scenarios (Base / Bull / Bear - which variables flex):
• Base: Brand growth +30%/+25%/+20%/+15%/+15%; take-rate 17%; SaaS ARPU $800
• Bull: Brand growth accelerates via platform integrations (Snap, Instagram scale); take-rate expansion to 20%; financing revenue material contributor
• Bear: Brand growth stalls (<10%/yr); consumer demand softens; take-rate compression from competition; SaaS conversion low

• Required sheets / outputs:

1. Assumptions - all drivers, toggleable scenarios
2. GMV Build - brands × avg GMV/brand × sell-through
3. Revenue Bridge - take-rate revenue + SaaS/services + financing
4. P&L - revenue, COGS/payment processing, gross profit, S&M, R&D, G&A, EBITDA
5. Headcount plan - by function
6. Cash flow & runway
7. KPI Dashboard - brands, GMV, take-rate %, NPS, merchant retention, NTM revenue