Fakespot Financial Model
Enterprise/Security Startup Financials (Free Excel Download)
Real-time eCommerce fraud protection - detects fake reviews, unreliable sellers, and counterfeit/problem purchases via a browser extension.
professionals from Deloitte
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About this model
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.
A turnkey financial model
Live formulas, no hardcoded values
Outputs are driven by live formulas, so the workbook updates from its assumptions instead of relying on hardcoded results.
All assumptions in one tab
Inputs are clearly marked in the Assumptions tab and separated from calculations, making it clear what to change and what to leave intact.
Statements always balancing
For integrated-statement models, the balance sheet, cash flow, and supporting schedules tie through properly.
Distinct schedules for clarity
Debt, working capital, taxes, and cash flow can get messy quickly. We group calculations in clear schedules, not across disconnected tabs.
No hidden macros or external links
There are no unexplained external workbook links or macros to undermine auditability or portability.
Changes flow through the model
Update a key driver and see the impact carry through the forecast, financing, and return outputs. We never use hardcoded numbers in formulas.
About Fakespot
fakespot.com
How to build a detailed financial model for Fakespot
A complete walkthrough of the business, drivers, and assumptions behind the downloadable Fakespot model - distilled from its pitch deck and publicly available information.
Product & value proposition
- Chrome Extension that integrates with Amazon, eBay, Best Buy, Sephora, and Walmart (Walmart listed as "Coming Soon").
- Three core capabilities:
- Detects and eliminates fake reviews (grades seller reliability).
- Spots unreliable sellers in real-time and recommends better sellers.
- 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).
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:
- Assumptions - all drivers in one place, toggleable by scenario.
- User Funnel - installs, MAU, paying users by month.
- Revenue Build - affiliate revenue + subscription MRR/ARR; optionally B2B API.
- P&L - gross profit, OpEx (R&D/headcount, hosting/ML infra, G&A), EBITDA.
- Headcount - roles, hire dates, fully-loaded cost.
- Cash & Runway - cash burn, months of runway given funding raise (amount unknown).
- Dashboard - MAU, MRR, burn rate, runway KPI cards + monthly chart.
Frequently asked
Is the Fakespot financial model free?+
Yes. The Fakespot model is a free Excel (.xlsx) download with live formulas. Sign up with your email and the workbook is yours to keep, review, and edit.
What's included in the model?+
A 5-year monthly forecast with P&L, cash flow and runway, valuation (exit multiple plus a DCF cross-check), MOIC/IRR returns, and unit economics, with live formulas throughout.
How was this model built?+
It was built from Fakespot's pitch deck and publicly available information, then structured to investment-banking standards as a fully editable Excel model.
Can I change the assumptions?+
Yes. You can change assumptions and the live formulas will recalculate in the downloadable Excel model.
Have more financial modelling questions? Contact us
Created by ex-finance professionals
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