Money, Pricing & Model

How do I estimate LTV when I have only three months of data and no idea what my real churn or retention curve is yet?

A starting point

With three months of data you cannot compute a trustworthy LTV, so stop trying to project lifetime and instead track a shorter window you can actually observe, like 3 month or 6 month revenue per customer. Use a conservative retention assumption, model a range (pessimistic, base, optimistic), and revisit as real cohorts age. As a starting point, an honest 6 month revenue number beats a confident 3 year LTV built on a guessed churn rate.

Go deeper

Hand-picked from around the web, each with a note on why it earns your time.

3 resources 2 link-checked Watch Read Use

Watch

▶️ Video
✓ Link checked Free Beginner

Why we picked it If you have never actually built a retention curve, reading about them only gets you so far, so this is a visual walkthrough of how a cohort table becomes a retention curve and how investors read it. It goes from raw user retention to net dollar retention step by step, which is the mental model you need before you try to bound LTV from three months of data. Watch it once, then go build your own tiny version in the template below.

Customer Retention & Cohort Analysis: How VCs Calculate Customer Retention

On YouTube Short explainer (under 15 minutes)

Customer Retention & Cohort Analysis: How VCs Calculate Customer Retention
  • A cohort table (customers acquired per month, then retained each following month) is the raw material a retention curve is drawn from.
  • Reading the shape of the curve, where it drops fast and where it flattens, matters more than any single average when your history is short.
  • Net dollar retention (expansion offsetting churn) can tell a very different story than logo retention, so watch both as your cohorts age.
Watch on YouTube youtube.com →

Read

📄 Article
Free Intermediate

Why we picked it This is the piece that made cohort-based LTV the default way serious investors think, and it is exactly the right medicine when you have three months of data and no settled curve. Hsu argues you should read empirically realized cohort LTV instead of plugging numbers into a formula, and he shows why young cohorts should stay short lines (you genuinely do not know their LTV yet) rather than being extrapolated into a confident-looking figure. Treat it as a way to bound and shape your estimate, not to manufacture one.

Diligence at Social Capital, Part 3: Cohorts and (revenue) LTV

From Social Capital / The Startup (Medium) by Jonathan Hsu About 20 minute read

  • Prefer empirically realized cohort LTV over a formula: with thin history, a single LTV number is a guess dressed up as math.
  • Young cohorts are short lines on purpose, do not extrapolate them; watch the shape (flat, sub-linear, linear, super-linear) instead of chasing one figure.
  • What you are really looking for early is evidence of linear or super-linear LTV in at least some cohorts, which tells you retention is holding, not just decaying.
Open medium.com →

Use

🛠️ Tool
✓ Link checked Free Beginner

Why we picked it This is the template to stop guessing a single LTV number and plot your actual cohorts instead. You type in only the basics (how many customers you acquired each month and how many stayed each following month) and it works out retention, churn, MRR, and per-cohort lifetime value for you, so three months of data becomes three honest cohort rows rather than one made-up figure. It is a founder-friendly starting point, not a forecasting engine, which is exactly what you want this early.

The easiest spreadsheet for churn, MRR, and cohort analysis

From andrewchen.com by Christoph Janz (Point Nine Capital), guest post on Andrew Chen's blog Downloadable Excel template plus short guide

  • You only enter basic cohort data (acquired and retained per month); retention, churn, MRR, and LTV are calculated automatically.
  • Plotting your real cohorts side by side shows you the retention curve you actually have instead of a churn rate you assumed.
  • With only a few months in, treat the LTV cells as a live, updating estimate that firms up as each cohort ages, not a locked-in answer.
Open andrewchen.com →

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