# Fakespot Financial Model

Real-time eCommerce fraud protection - detects fake reviews, unreliable sellers, and counterfeit/problem purchases via a browser extension.

- Canonical: https://finamodel.com/startups/fakespot
- Excel download: https://finamodel.com/startup-models/fakespot.xlsx
- Category: Enterprise/Security
- Model type: SaaS ARR / Valuation
- Funding round: Series A
- Funding: $4M
- Founded: 2020
- Geography: US-focused (Amazon, eBay, Best Buy, Sephora, Walmart integrations) [DECK slide 3].
- Customer: B2B

## About the company

Fakespot is a consumer trust-and-safety product that identifies fake reviews, unreliable sellers, and potential counterfeit or problematic purchases. Its browser extension analyses e-commerce activity in real time to help shoppers judge whether a listing and its reviews are credible.

The deck does not disclose a settled revenue model, so the business should not be presented as a conventional SaaS company. The product has a consumer distribution engine through installs and active users, with plausible monetisation paths in affiliate referrals, premium features, or later API licensing.

The model therefore starts with installations, monthly active users, retention, and user acquisition. It keeps affiliate revenue and paid conversion as separate, explicit assumptions, linking click-through or premium uptake to revenue, then tests support, product, marketing, and cash needs.

## 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

- Chrome Extension that integrates with Amazon, eBay, Best Buy, Sephora, and Walmart (Walmart listed as "Coming Soon").
- Three core capabilities:
  1. Detects and eliminates fake reviews (grades seller reliability).
  2. Spots unreliable sellers in real-time and recommends better sellers.
  3. Prevents counterfeit and problem purchases.
- Differentiator vs. Honey / Wikibuy: finds better sellers without coupons; adds fraud/counterfeit protection layer on top of price comparison.
- In-product UI shows a seller grade (e.g., "C"), "Seller Approved" / "Seller Warning" badges, and a "Consider this better seller" recommendation with savings, return policy comparison, and counterfeit risk.

## Competition / moat

- Named competitors/comparables: Honey, Wikibuy.
- Claimed differentiation: only platform combining fake-review detection + bad-seller protection + better-seller recommendation in one tool.
- Moat implied: proprietary AI/ML review-analysis engine and seller-rating model (referenced as "Fakespot Guard"); not detailed further.

## Team & funding ask / use of funds

- Saoud Khalifah - Founder & CEO; Forbes 30 Under 30.
- Rob Gross - Co-Founder & COO; leads operations, product, customer success.
- Josh Applebaum - Lead Backend Engineer; ex-Shutterstock commerce platform.
- Chris Koehler - Full-Stack Engineer.
- Sen Tian - Data Science Lead; NYU Data Science Ph.D candidate (graduating 2020).

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## Recommended financial model

- **Archetype + why:** Freemium B2C consumer app - user funnel model (installs → MAU → paid conversion or affiliate revenue). The deck shows a browser extension with no disclosed subscription pricing, so the most defensible archetype is a **user-growth + monetization ramp model** with two potential revenue legs: (a) affiliate/referral fees per seller redirect, and (b) optional premium subscription. If B2B API licensing emerges as a revenue line, a SaaS ARR layer can be added. Do not use a pure SaaS ARR model until pricing/B2B traction is confirmed.

- **Forecast horizon & granularity:** 3 years (Y1–Y3), monthly for Y1 then quarterly for Y2–Y3. Extension-install and MAU curves move fast enough to need monthly resolution in the near term.

- **Key drivers & assumptions:**

| Driver | Value |
| -- | -- |
| Chrome Extension install base (starting) | Unknown |
| Monthly new installs growth rate | 10–20% MoM early, decaying to 5% by Y2 |
| Monthly Active Users / Installs ratio | 40% |
| Affiliate revenue per seller redirect click | $0.50–$2.00 |
| Affiliate clicks per MAU per month | 1.5 |
| Freemium-to-paid conversion rate (if subscription added) | 2–5% |
| Monthly subscription price (if launched) | $3–$5/mo |
| Gross margin on software | 80–85% |
| Headcount at model start | 5 (per team slide) |
| Engineering + DS hiring ramp | +2–3 per year |
| Avg fully-loaded engineer cost (NYC) | $150K–$180K/yr |
| S&M spend | Minimal; viral/word-of-mouth growth assumed dominant |

- **Scenarios (Base / Bull / Bear - which variables flex):**
  - **Bear:** Slower install growth (5% MoM), low affiliate take rate, no paid tier launched in forecast period.
  - **Base:** 12% MoM install growth decaying to 5%, $1 avg affiliate revenue per MAU/month, subscription launched in Y2 at 2% conversion.
  - **Bull:** Viral growth event (press, Mozilla/Apple Store feature), $1.50 affiliate rate, 5% paid conversion, B2B API deal in Y3.

- **Required sheets / outputs:**
  1. **Assumptions** - all drivers in one place, toggleable by scenario.
  2. **User Funnel** - installs, MAU, paying users by month.
  3. **Revenue Build** - affiliate revenue + subscription MRR/ARR; optionally B2B API.
  4. **P&L** - gross profit, OpEx (R&D/headcount, hosting/ML infra, G&A), EBITDA.
  5. **Headcount** - roles, hire dates, fully-loaded cost.
  6. **Cash & Runway** - cash burn, months of runway given funding raise (amount unknown).
  7. **Dashboard** - MAU, MRR, burn rate, runway KPI cards + monthly chart.

## Frequently asked questions

### Is the Fakespot financial model free?

Yes. The Fakespot 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.