# Mine Financial Model

Consumer data-privacy assistant that scans your email inbox to map your digital footprint and automates GDPR/CCPA deletion requests to companies.

- Canonical: https://finamodel.com/startups/mine
- Excel download: https://finamodel.com/startup-models/mine.xlsx
- Category: Enterprise/Security
- Model type: SaaS ARR / Valuation
- Funding round: Series A
- Funding: $9.5M
- Founded: 2020
- Geography: Global launch; strong early traction in US, Europe, Israel [DECK, slide 15]
- Customer: B2B

## About the company

Mine is a consumer privacy assistant that scans a user's email inbox to map their digital footprint and automate GDPR and CCPA deletion requests. It is designed to make the personal-data relationships hidden in inboxes more visible and actionable.

The product begins as a consumer freemium service, with a B2B privacy platform under development. That dual-sided ambition means user trust and consumer acquisition matter at first, while business-facing privacy tools could later create a separate recurring-revenue engine.

The model treats consumer and B2B economics separately: users, activation, paid conversion, and support costs for the consumer layer; customer wins, ACV, and retention for SaaS. Privacy operations, product investment, and acquisition spend drive the cash runway.

## 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

- Smart Data Assistant (branded "Mine"): scans email subject lines (not email content) to identify all companies that have collected a user's personal data
- Generates a personalised digital footprint map across categories: Financial, Identity, Online Behaviour, Social Network
- Automates GDPR/CCPA deletion ("right to be forgotten") requests on behalf of users
- Individual Risk Score based on proprietary ML/NLP algorithms; Value Level Score (VLS); continuous footprint discovery
- Vision: "dynamic consent" on the consumer side; "Enriched Requests" standard for enterprises to process privacy requests faster
- No behaviour change required; fits mainstream (non-tech-savvy) users

## Market

- No explicit TAM/SAM/SOM figures in deck.
- Context provided: US data breach victims running into hundreds of millions; 1B+ records exposed in US alone, 160M+ SSNs
- Privacy regulations spreading globally: GDPR, CCPA, PIPEDA, LGPD, PDPA Singapore, OAIC Australia, ICO DPA UK, India, Argentina, Nevada/NY/Texas/DC laws
- Framing: substantial shift in data power from companies toward consumers underway

## Revenue model

- **B2C - Freemium**: product free to end users; no paid tier pricing disclosed; described as "still free until EOY 2020"
- **B2B - "Enriched Requests" SaaS**: sell to companies to help them process incoming consumer privacy requests faster via a standardised API/platform; described as "in beta phase" at time of deck
- No pricing (B2C premium tier or B2B seat/request fees), no ARPU, no ACV figures disclosed.

## Traction & metrics

All figures as of ~September 2020 (8 months post-launch, official launch 21 January 2020):
- **>100,000 sign-ups** - "mostly organic"
- **>1,000,000 deletion requests sent** to companies
- **~28% conversion rate** from site visitor to sign-up - "6x higher than industry benchmark"
- **15,000 users saved** from a potential data breach ("Epic Save")
- **40%–60% reduction** in users' online data exposure
- **Accumulative signups grew 2x every cohort** during COVID period (Dec 2019–Sep 2020) - y-axis values blurred/confidential in image
- **Product Hunt**: #1 Product of the Day (Jan 22, 2020), #1 Product of the Week, #2 Product of the Month; 4,352 upvotes
- Zero revenue disclosed (product free at time of deck)

## Competition / moat

- Moat described as tech + product:
  - Individual Risk Score (proprietary ML/NLP)
  - Footprint Continuous Discovery
  - Value Level Score (VLS)
  - Privacy Policy Analysis
  - Mainstream UX - no behaviour change required
- Regulatory tailwind: GDPR/CCPA enforcement gives legal weight to user requests
- No competitive landscape slide or named competitors in deck

## Team & funding ask / use of funds

- **Gal Golan** - Co-Founder & CTO; background at SalesPredict, Microsoft
- **Gal Ringel** - Co-Founder & CEO; background at Verizon Ventures, Nielsen; Forbes 30 Under 30
- **Kobi Nissan** - Co-Founder & CPO; background at INSEAD, King, Accenture
- **16 employees** at time of deck
- **Raised $3M in total**; investors: Battery Ventures, Saban Ventures, Intel Ignite

---

## Recommended financial model

**Archetype + why:**
Dual-stream freemium + B2B SaaS model. The near-term driver is B2C user growth (free, leading to eventual premium conversion); the medium-term revenue driver is B2B SaaS (Enriched Requests). Model should capture both streams separately, with B2C as the user acquisition funnel that feeds B2B pipeline credibility. Closest archetypes: consumer freemium → premium conversion waterfall (B2C arm) + ARR SaaS model (B2B arm).

**Forecast horizon & granularity:**
- Monthly for Year 1–2 (product is pre-revenue; monthly granularity needed to track user growth and conversion inflection)
- Quarterly for Years 3–5
- Total horizon: 5 years

**Key drivers & assumptions:**

*B2C - User funnel:*
- Monthly new sign-ups (starting base: ~100K cumulative at Sep 2020); growth rate post-deck
- Site visitor → sign-up conversion: 28%
- Free → premium conversion rate
- B2C premium ARPU / month
- Monthly churn on premium subscribers

*B2B - Enriched Requests SaaS:*
- B2B launch date
- Number of enterprise/SMB clients
- B2B ACV / seat pricing
- B2B logo churn
- Revenue per privacy request processed

*Costs:*
- Headcount: 16 employees at deck date;
- Avg fully-loaded cost per employee
- Cloud / infrastructure costs
- S&M:
- R&D:

*Balance sheet / cash:*
- Cash raised: $3M; burn rate
- Runway

**Scenarios (Base / Bull / Bear - which variables flex):**
- **Base**: 28% visitor-to-signup sustained; B2C premium conversion 3%; B2B ARR ramp begins Q1 2021; 20 B2B clients by end Y1
- **Bull**: COVID privacy awareness sustains elevated sign-up growth; B2C conversion 5%; B2B 50 clients Y1 at higher ACV; regulatory enforcement tightens and drives enterprise urgency
- **Bear**: Growth decelerates sharply post-COVID; B2C monetisation delayed to 2022; B2B sales cycle long (6–12 months); cash runs out without raise; user fatigue on privacy tools

**Required sheets / outputs:**
1. **Assumptions** - all drivers, toggleable by scenario
2. **B2C Funnel** - monthly visitors → sign-ups → free MAU → premium subscribers → MRR
3. **B2B ARR** - client adds, churn, ACV, ARR bridge
4. **P&L** - Revenue (B2C + B2B), Gross Profit, Opex (R&D / S&M / G&A), EBITDA, Net Income
5. **Headcount Plan** - by department, cost build
6. **Cash Flow & Runway** - monthly burn, cash balance, months of runway
7. **KPI Dashboard** - Total sign-ups, MAU, premium subs, B2B clients, ARR, burn, runway

## Frequently asked questions

### Is the Mine financial model free?

Yes. The Mine 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.