Unit Economics Dashboard Calculator

Compute a unit economics snapshot: gross profit LTV, CAC payback, LTV:CAC, and break-even targets from a few inputs.

Unit economics answer: do we create enough gross profit per customer to justify what we spend to acquire them, and how fast do we get cash back-

This dashboard calculator computes gross profit LTV, CAC payback months, LTV:CAC, and simple break-even targets. It's designed for fast scenario testing and planning.

Prefer an explanation- Read the guide.
 
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%
Used as a shortcut lifetime estimate; cohort LTV is more accurate.
%
 
$
Set 0 to disable target CAC calculation.
Tip: you can type commas (e.g., 10,000).

Example

Using the default inputs, the result is:
5.33x
ARPA (monthly)
$800
Gross margin
80%
Monthly churn (logo)
2%
CAC (per new customer)
$6,000
Target payback (months, optional)
12

How to calculate

  1. Enter ARPA, gross margin, and monthly churn to estimate gross profit LTV.
  2. Enter CAC to compute payback months and LTV:CAC ratio.
  3. Optionally add a target payback to see a max CAC target.

Formula

Gross profit LTV ~ (ARPAxgross margin) / churn; Payback ~ CAC / (ARPAxgross margin); LTV:CAC ~ LTV / CAC
  • Uses logo churn as a shortcut lifetime estimate (1/churn).
  • Assumes constant ARPA and gross margin over lifetime.
  • For accuracy, use cohort-based LTV and segment-level retention curves.

FAQ

What LTV:CAC is good-
It depends on growth stage and payback constraints. Many teams use ~3x as a rough rule of thumb, but payback and cash constraints matter more than a single ratio.
Why can this be misleading-
Because churn is rarely constant and expansion can change revenue over time. This is a planning shortcut; validate with cohort curves when possible.

Common mistakes

  • Using revenue LTV instead of gross profit LTV (overstates value).
  • Mixing monthly churn with annual ARPA (time unit mismatch).
  • Comparing fully-loaded CAC to revenue-based LTV (mismatch).

Quick checks

  • Keep time units consistent (monthly vs annual) across inputs and outputs.
  • Segment by cohort/channel/plan before trusting a blended average.
  • Use the related guide to avoid common definition and denominator mismatches.