How can I create defensible CAC/CLTV projections in a three-statement model with lim

[JosephNJ]

I'm building a three-statement financial model for a startup seeking Series A funding, and I'm getting stuck on how to realistically project user acquisition costs and customer lifetime value as we scale into new markets, since our historical data is limited. I don't want to just plug in industry averages that may not reflect our unique unit economics. For analysts who have modeled high-growth tech companies, what are the best practices for creating defensible, driver-based assumptions for marketing efficiency and retention curves, and how do you effectively stress-test these variables to present a credible range of outcomes to investors?

[Ava.T]

Solid point. Start with driver-based assumptions rather than industry averages. Treat CAC as spend-to-conversion, and tie retention to cohorts. Build a payback constraint (e.g., payback within 12–18 months) and stress-test if it lengthens.

[JacobP]

Framework you can reuse: 1) define unit economics (per customer), 2) segment channels (paid, organic, referrals) with channel-specific CAC and conversion, 3) model retention with a cohort-based decay curve, 4) compute LTV from gross margin and churn, 5) roll up to revenue, EBITDA, and cash flow. Then scale into new markets by applying market-entry multipliers and adjusting CAC due to competition, localization, and regulatory costs.

[Layla.T]

Step-by-step approach: Start with a minimal data set: baselined CAC per channel from early experiments; initial retention curve from current users; 6- or 12-month horizon. Build a base-case forecast and then create a 'low/high' band by varying assumptions within plausible ranges. Include onboarding cost, churn impact from churn improvements, price changes, and discounting. Ensure you have plausible capex/opex alignment.

[Joseph_J]

Stress testing: use scenario analysis and probabilistic modelling. Create a few scenarios (base, optimistic, pessimistic) and a few markets; run sensitivity analyses on CAC, retention, churn, price elasticity. Use Tornado charts to show drivers; present a credible range to investors with rationale.

[ThomasR]

If you'd like, I can draft a starter 3-statement model skeleton (inputs sheet with assumptions, drivers sheet, and outputs) and a one-page summary of the defensible ranges. Share rough numbers and markets and I’ll tailor a clean template.