As an angel investor, you see dozens of startup pitches every month. The problem? Most deals look promising on the surface — until you dig deeper.
After 3 years of angel investing in 14 startups (2 exits, 3 failures, 9 still running), I developed a systematic scoring framework. Here's why it works:
The 80/20 of Due Diligence
80% of deal quality comes from just 3 categories: Team, Market, and Traction. Yet most investors spend equal time on everything. My scorecard weights these at 25%, 20%, and 15% respectively.
How to Score Objectively
For each category, ask 3-5 specific sub-questions on a 0-5 scale:
- 0 = dealbreaker negative
- 1 = well below average
- 2 = below average
- 3 = average
- 4 = above average
- 5 = exceptional
I never give a 4 or 5 without specific evidence (prior exits, revenue growth, patents).
The Scorecard in Action
Let's score a real example: NeoHealth AI (healthtech, seed round).
- Team: 4.0 (founders exited previous startup for $15M)
- Market: 4.5 (AI health market growing 40% YoY, $50B TAM)
- Product: 3.5 (working MVP, 200 users, 90% retention)
- Traction: 2.0 ($5K MRR, pre-revenue traction limited)
- Business Model: 3.0 (SaaS with enterprise contracts, 75% gross margin)
- Competition: 3.5 (unique AI workflow, 6 months head start)
- Financials: 3.0 (burn $50K/mo, 18 months runway, valuation $5M)
- Terms: 4.0 (standard YC SAFE, no liquidation preference)
Weighted total: 3.45 → Buy (yellow zone)
Without this framework, the 2.0 traction score might have killed the deal. But with proper weighting, it's a solid buy. (I invested and it's now at $500K ARR.)
Try It Yourself
I've turned this framework into a Google Sheets template — the Angel Investment Scorecard. It has pre-built formulas, color-coded heatmaps, and a deal flow tracker. Grab it at: https://microtoolsb2b.gumroad.com/l/angel-investment-scorecard
What's your deal scoring process? Share below — I'm always looking to improve.
