Tool 14 — Free, no sign-up

Spend it, then wait for it.

A budget, a CPI and an LTV curve are enough to know when the money comes back and whether it comes back at all. Cohorts are tracked day by day, not averaged into one lump.

The campaign

Extra installs per paid install, from store ranking and word of mouth. 15–35% is typical; anything above 60% needs proof.
Spend schedule
Front-loaded decays about 12% a day, which is what a launch burst actually looks like.

The LTV curve

Do not have these? Fit them from D1, D7 and D30 and they land here automatically.

Spend against revenue

Cumulative revenue Cumulative spend
Day Spend that day Cumulative spend Cumulative revenue Net Return

If you are wrong by 20%

Profit at D180. Rows move CPI, columns move the whole LTV curve.

LTV −20% LTV base LTV +20%

How it works / caveats

  1. Paid installs per day = that day’s spend ÷ CPI. Total installs add the organic uplift on top.
  2. Each day’s installs are their own cohort. Revenue on day D = the sum over every cohort of installs × LTV at that cohort’s age.
  3. LTV between your four anchors is interpolated linearly in ln(1 + age), so value accrues fast early and slowly later. After D180 it is held flat, which understates a game with a long tail.
  4. Payback day is the first day cumulative revenue crosses cumulative spend, not the day a single cohort pays back.
  5. ROAS at D7/D30/D90 is cohort ROAS: total installs × LTV at that age ÷ budget.
  6. Break-even CPI = (1 + organic uplift) × LTV at the horizon.
  7. CPI is treated as constant. In real campaigns it rises as you scale and as creatives fatigue, usually 20–40% between the first and last week. The sensitivity table is there for exactly that.
  8. Revenue here is net of the store cut only if your LTV input already was. Check.