# BRD Financial Model

BRD is a non-custodial crypto wallet app (consumer) and blockchain infrastructure API platform (enterprise) targeting mass-market crypto adoption.

- Canonical: https://finamodel.com/startups/brd
- Excel download: https://finamodel.com/startup-models/brd.xlsx
- Category: Crypto/Web3
- Model type: Marketplace / GMV
- Funding round: Venture
- Funding: $15M
- Founded: 2020
- Geography: Global - 170+ countries [DECK, slide 2]; primary markets US and EU (Top 50 finance app) [DECK, slide 3].
- Customer: B2B2C

## About the company

BRD combines a non-custodial consumer crypto wallet with Blockset, an enterprise blockchain-infrastructure API. The consumer product targets mass-market ownership while the API gives businesses a way to build blockchain applications, creating distinct retail transaction and enterprise software revenue paths.

The business was operating globally across more than 170 countries, with the US and EU highlighted as key markets. At its growth-capital raise, BRD had previously raised $23 million of equity and $32 million through a token sale, and was seeking up to $50 million to expand the platform.

Use a dual-segment model. For the wallet, forecast monthly active users, transaction volume per user, fee yield, and acquisition cost; for Blockset, build customer additions, ARR, expansion, and churn. Consolidate the two P&Ls only after retaining their different drivers, then assess the growth-capital runway against product, infrastructure, and compliance spend.

## 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

Two products:

**BRD Consumer Wallet**
- Non-custodial, fully decentralized mobile wallet (iOS & Android); connects directly to blockchains - no BRD custody of user funds.
- Supports Bitcoin, Ethereum, BRD token, Dai, and other tokens.
- Revenue-generating services layered on top: fiat-to-crypto on-ramp, P2P transfers, token exchanges, interest-bearing accounts - all via licensed/regulated partner integrations.
- Roadmap ("Tomorrow"): direct deposit, ATM debit card, bill pay, high-yield savings, stock investing, lending.

**BRD Enterprise: Blockset**
- B2B SaaS - single API for blockchain data across all chains (BlockchainDB), white-label wallet SDK (Wallet Kit), managed node infrastructure (ChainOps).
- Value prop: save >50% on development costs, get to market 75% faster; 99.999% uptime SLA.
- First signed client: SBI Holdings (large Japanese bank).
- Launching Q1 2020.

## Market

- Blockset B2B TAM: $10B - source cited as Galaxy Digital Research.
- Blockset 3-year ARR target: $150M.
- Industry milestone framing: 50M bitcoin holders by 2017; 1B active crypto users projected by 2025.

## Revenue model

**Consumer (B2C)**
- Transaction fees on fiat-to-crypto purchases, P2P transfers, and token exchanges - revenue flows through licensed partner integrations (BRD earns a spread or referral fee).
- No explicit fee rate disclosed in deck.
- Revenue per user (MAU) = $17.03 as of Jun 2019 - implies blended per-active-user transaction take rate.
- Volume per user = $1,917.28/month as of Jun 2019 - implies ~0.9% effective take rate.
- Future monetisation roadmap: debit card interchange, interest margin on savings, lending spread, stock trading commissions.

**Enterprise (B2B - Blockset)**
- SaaS subscription / API usage fees.
- Target: $150M ARR in 3 years.
- No per-seat, per-call, or tier pricing disclosed.

## Traction & metrics

All figures from slides 2–3, 5–7, 14, 18:

| Metric | Value | Date / Period |
| -- | -- | -- |
| Total installs (iOS + Android) | 2.5M+ | At time of deck [slide 2, 3] |
| Assets under protection (AUP) | $6B+ | At time of deck [slide 3] |
| Gross transaction volume runrate | $500M/yr | At time of deck [slide 3] |
| Share of daily Bitcoin transactions | 2% | At time of deck [slide 3] |
| Countries | 170+ | [slide 2, 3] |
| App store ranking | Top 50 finance app in US & EU | [slide 3] |
| Monthly Active Users (MAU) | 40,000 → 161,000 | Jan 2018 → Jun 2019 (+303%) [slide 14] |
| Revenue per MAU | $2.44 → $17.03 | Jan 2018 → Jun 2019 (+600%) [slide 14] |
| Volume per MAU | $488 → $1,917 | Jan 2018 → Jun 2019 (+292%) [slide 14] |
| Active users (quarterly) | 174K → 452K | Q2 2018 → Q2 2019 (+116% YoY vs Coinbase +67%) [slide 7] |
| Monthly transaction volume (chart) | ~$1M → ~$31M | Jan 2018 → Jun 2019 [slide 5 image - y-axis max 32M, Jun 2019 bar ~$30–31M] |
| Transaction volume by quarter (roadmap actuals) | Q2'19: $95M; Q3'19: $158M; Q4'19: $212M | [slide 18] |
| User growth target | 2.5M → 10M | By 2020 [slide 6] |
| Transaction volume target | $1B | By 2020 [slide 18] |
| Enterprise first client | SBI Holdings | Signed [slide 10] |

## Unit economics

- **LTV (cohort):** Jun–Aug 2018 cohort reaches ~$30 LTV at month 11; Mar–May 2019 cohort (most recent) on trajectory toward ~$65–70 at month 12 (still accruing at month 5, ~$35).
- **CAC-3 (cost to acquire a paying user, 3-month definition):** ~$150 in Jul 2019; projected to fall to ~$35–40 by Jan 2020.
- **Projected CAC/LTV crossover:** CAC-3 projected to fall below LTV ~Dec 2019/Jan 2020.
- **Revenue per MAU:** $17.03 (Jun 2019).
- **Effective take rate on volume:** ~0.89%.

## Competition / moat

- Active user growth benchmark: BRD grew 116% in 2018 vs. Coinbase 67% - positioned as faster-growing despite bear market.
- Moat claims: non-custodial / decentralized architecture (users self-custody), 5-year head start (founded 2014), global regulatory-light model (no custody = no banking license required per jurisdiction), Blockset infrastructure moat from running nodes at scale.
- No explicit competitive matrix slide; no named competitors beyond Coinbase comparison.

## Team & funding ask / use of funds

**Team**:
- Adam Traidman - CEO, Co-Founder
- Aaron Voisine - Co-Founder
- Aaron Lasher - Co-Founder
- Brent Traidman - Chief Revenue Officer
- Samuel Sutch - CTO
- Spencer Chen - VP Global Marketing
- Ada Vaughan - Head of Business Development
- Kirill Gertman - VP of Product
- 40+ total employees

**Funding**:
- Funds raised to date: $23M equity + $32M token sale = $55M total
- Current round: Up to $50M in growth capital
- Existing investors: SBI Holdings, Liberty City Ventures, East Ventures, SAISON Ventures + 30 angels/family offices/CVCs
- Use of funds: Not explicitly stated in deck (implied: product roadmap execution - savings, debit card, stock trading - and Blockset launch/scaling).

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## Recommended financial model

- **Archetype + why:** Dual-segment operating model - (A) Consumer: transaction-fee / take-rate model driven by MAU × volume per user × take rate; (B) Enterprise SaaS: ARR build-up (Blockset). These two P&Ls feed into a consolidated 3-statement model. The consumer segment resembles a fintech/marketplace revenue model (volume-based); the B2B segment is a classic SaaS ARR ramp.

- **Forecast horizon & granularity:** Monthly for 2019–2020 (near-term operational detail); annual for 2021–2023. Deck is circa mid-2019 with targets to end-2020.

- **Key drivers & assumptions:**

  *Consumer segment:*
  - Total installs base: 2.5M; growth to 10M by end-2020 - implies ~300% install growth in 18 months
  - MAU / install ratio: ~161K MAU / 2.5M installs = ~6.4% activation rate
  - MAU growth trajectory: 40K (Jan 2018) → 161K (Jun 2019); extrapolate to ~400–500K by end-2020
  - Revenue per MAU: $17.03 (Jun 2019); assume gradual increase as product mix shifts to higher-value services (savings, debit card)
  - Monthly transaction volume per MAU: $1,917 (Jun 2019)
  - Implied take rate: ~0.89%
  - Gross transaction volume target: $1B annualised by 2020
  - CAC-3: ~$150 Jul 2019, projected ~$35 by Jan 2020; use $35–50 for 2020 forward
  - LTV: latest cohort trending ~$65–70 at 12 months; assume $70–80 steady-state
  - Gross margin on consumer revenue: ~70%

  *Enterprise (Blockset) segment:*
  - Launch: Q1 2020
  - 3-year ARR target: $150M
  - Implied ARR ramp: Year 1 (2020): ~$5–10M; Year 2: ~$40–60M; Year 3: ~$150M
  - Customer count ramp:
  - Gross margin on SaaS: ~75–80%

  *Opex:*
  - Headcount: 40+ employees at time of deck; scaling with $50M raise
  - S&M spend as primary CAC driver:
  - R&D / engineering: significant (blockchain infra + mobile app + Blockset platform)

- **Scenarios (Base / Bull / Bear - which variables flex):**
  - **Base:** MAU grows to ~400K by end-2020; take rate stable at ~0.9%; Blockset ARR reaches $10M by end-2020; CAC/LTV crossover Dec 2019 as projected.
  - **Bull:** 10M total installs hit by end-2020; higher conversion to MAU (10%); Blockset ARR $20M+ by end-2020; take rate expands as debit card/savings products launch.
  - **Bear:** Crypto market downturn (correlated risk - volume and user growth both suppressed as seen in 2018 bear market); MAU stalls at 200K; Blockset delayed or slow enterprise sales cycle; CAC/LTV crossover pushed to mid-2020.
  - Flex variables: BTC/crypto price (volume proxy), MAU growth rate, take rate, Blockset ARR ramp pace, CAC trajectory.

- **Required sheets / outputs:**
  1. **Assumptions** - all drivers centrally controlled
  2. **Consumer P&L** - MAU funnel × volume × take rate → revenue; CAC spend; contribution margin
  3. **Blockset SaaS P&L** - ARR bridge (new ARR, churn, expansion), ACV assumptions, MRR → ARR
  4. **Consolidated Income Statement** - both segments + shared opex (G&A, R&D)
  5. **Cash Flow** - operating CF; runway given $50M raise; burn rate
  6. **Balance Sheet** (simplified) - cash, deferred revenue (Blockset), equity
  7. **Unit Economics Summary** - CAC, LTV, LTV/CAC ratio, payback period by period
  8. **KPI Dashboard** - MAU, total installs, GTV, RPU, VPU, Blockset ARR, burn, runway

## Frequently asked questions

### Is the BRD financial model free?

Yes. The BRD 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.
