# RAW Dating App Financial Model

RAW is an authenticity-first dating app using dual-camera real-time photos and phone verification to eliminate catfishing, scammers, and ghosting.

- Canonical: https://finamodel.com/startups/raw-dating-app
- Excel download: https://finamodel.com/startup-models/raw-dating-app.xlsx
- Category: Enterprise/Security
- Model type: SaaS ARR / Valuation
- Funding round: Angel
- Funding: $3M
- Founded: 2024
- Geography: U.S. (large cities); target users 17+ with primary focus on women 21–27 in major U.S. cities [DECK slide 7].
- Customer: B2B

## About the company

RAW is an authenticity-first dating app using dual-camera real-time photos and phone verification to reduce catfishing, scammers, and ghosting. It is a consumer mobile product where trust and a high-quality user base are core to the experience.

The materials prioritise user acquisition, with 55% of the raise allocated to marketing. Revenue is not specified, so the defensible framing is freemium dating-app economics: subscriptions and potential in-app purchases driven by an active-user base.

The model builds MAU from installs, activation, retention, and paid acquisition, then converts users into premium subscriptions or purchases. Marketing, safety and moderation, platform costs, product development, and cash burn are tested across growth and conversion scenarios.

## 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

- Dual-camera mechanic: users snap simultaneous front + back selfie ("Raw") daily; stale or edited photos cannot be uploaded.
- Swipe feed shows only fresh daily Raws - no old profile photos.
- Match window gives 72 hours to initiate conversation ("RAWmance").
- Phone verification required at sign-up to eliminate bots/scammers.
- Positioning: "Not another fake dating app" - solves catfishing, scams, ghosting, toxicity.
- Target persona: Gen Z / young professionals, described as "authentic, open, active, honest" - women-led product ("for women, by women").

## Market

- $638.8M scammed on dating apps in 2023.
- 29% of dating app users admit to ghosting.
- 27% of online daters were catfished in the prior year.
- 75% of users faced abuse/toxic behavior on dating apps.

## Revenue model

Not explicitly stated in deck. Slide 9 messaging ("Don't put a price tag on love - other apps push you to pay for love") implies a free-to-use positioning, but no monetization model, pricing tiers, or revenue streams are described. No ARPU, subscription price, or in-app purchase detail given.

## Traction & metrics

Growth timeline shown across three milestones:
- Phase 1 (Aug): MAU 40,000 | SF (store feature or success factor?) 7% | Features: Phone verification, real-time photos, daily photo updates.
- Phase 2 (Jan–Feb): MAU 55,000 | SF 12% | Features: Live activity (no FOMO).
- Phase 3 (May): MAU 100,000 | SF 14% | Features: Limited time to start new chat (no ghosting).

Note: It is ambiguous whether these are actuals or targets. The 2022 deck date and the "launched 2023" language on slide 2 are inconsistent - the timeline likely represents a forward roadmap/target set at the time of fundraise, not historical actuals.

"SF" metric is not defined in the deck. Could be "Success Factor," "Store Feature rate," or a custom engagement metric. Treat as unknown until clarified.

## Competition / moat

Competitive matrix shows RAW vs. three unnamed competitors (//1 yellow, //2 red, //3 black - likely Tinder/Bumble/Hinge or similar):
- RAW scores highest bar-height on: Phone verification, Real-time photos only, Daily photo updates, Limited time to start a new chat.
- Competitors have zero or partial coverage of these columns.
- "Coming soon" features: Live activity, Limited unreplied chats, Respect meter, Meditation feature.
- Moat claims: mandatory daily Raws as a technical barrier to fake profiles; 72-hour match expiry to reduce ghosting; phone verification at sign-up.

## Team & funding ask / use of funds

- Founder & CMO: Marina Anderson - ex-Bumble, ex-Prequel, ex-XOXO.
- No other team members named.
- Funding ask: $2M pre-seed.
- Use of funds:
  - Marketing: 55% (~$1.1M)
  - MVP development: 30% (~$600K)
  - Overheads: 15% (~$300K)

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## Recommended financial model

**Archetype + why:** Consumer mobile freemium / subscription (dating app). The product is a user-acquisition-first app (55% of raise goes to marketing) with MAU as the primary KPI. Revenue model is unstated but standard dating app monetization is freemium with premium subscription tiers (e.g., unlimited swipes, Boost, see who liked you) and potentially in-app purchases. Model should follow a MAU funnel → paid conversion → subscription MRR/ARR structure, consistent with Bumble/Hinge comps.

**Forecast horizon & granularity:**
- 3 years (Year 1–3), monthly granularity for Year 1, quarterly for Years 2–3.
- Year 1 milestones anchored to the roadmap timeline in the deck (Aug / Jan / May).

**Key drivers & assumptions:**

| Driver | Value |
| -- | -- |
| MAU at Phase 1 (Aug) | 40,000 |
| MAU at Phase 2 (Jan–Feb) | 55,000 |
| MAU at Phase 3 (May) | 100,000 |
| "SF" metric Phase 1 / 2 / 3 | 7% / 12% / 14% |
| Pre-seed raise | $2,000,000 |
| Marketing spend % of raise | 55% = ~$1.1M |
| MVP dev spend % of raise | 30% = ~$600K |
| Overheads % of raise | 15% = ~$300K |
| Monthly subscription price | $14.99/month |
| Free-to-paid conversion rate | 5% of MAU |
| Paid subscriber churn (monthly) | 8% |
| CAC (blended, paid + organic) | $2.00–$5.00 |
| D30 retention | 25% |
| MAU growth beyond May Year 1 | 15% MoM declining to 5% by Month 24 |
| Annual gross margin | 70% |
| Headcount ramp | 3 FTE Y1, 8 FTE Y2, 15 FTE Y3 |

**Scenarios (Base / Bull / Bear - which variables flex):**
- Base: MAU hits 100K by May Year 1 per roadmap; 5% paid conversion; $14.99/month sub price.
- Bull: MAU hits 200K by end of Year 1 (viral growth, Gen Z organic sharing); paid conversion 8%; CAC $2.
- Bear: MAU stalls at 55K after Phase 2; paid conversion 2%; CAC $5; runway < 12 months on $2M raise.

**Required sheets / outputs:**
1. Assumptions - all drivers, centralized, tagged /.
2. MAU Build - installs → actives → paid subs, monthly.
3. Revenue - subscription MRR (free-to-paid × price × (1−churn)); optional: in-app purchase line.
4. P&L - revenue, gross profit (after app store fees), S&M (marketing spend), R&D (MVP dev), G&A (overheads), EBITDA.
5. Cash - opening $2M, monthly burn, runway (months to zero), fundraise timing trigger.
6. KPI Dashboard - MAU, paying users, MRR, CAC, LTV, LTV/CAC, months runway.
7. Scenario toggle - Base / Bull / Bear switchable from assumptions sheet.

## Frequently asked questions

### Is the RAW Dating App financial model free?

Yes. The RAW Dating App 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.