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. We have no benchmark here we can stand behind, so take it from your own last campaign: total installs ÷ paid installs − 1.
Spend schedule
Front-loaded drops the daily spend 12% a day. On a 30-day campaign that puts about 55% of the budget in the first six days.

The LTV curve

Cumulative revenue per install, in USD. If your LTV is already net of the store cut then every number on this page is net too — the tool cannot tell which you typed.

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

Spend against revenue

Nothing to draw yet.

Cumulative revenue Cumulative spend

Two clocks run through this page. The results on the right age every install to the same day: D180 means once every install has lived 180 days. The table below reads the campaign's own calendar, where the cohort you bought on the last day is still young. Neither is wrong. The gap between them is the money that has not arrived yet.

Cumulative spend and revenue by campaign day, calendar basis
Campaign day Spend that day Cumulative spend Cumulative revenue (calendar) Net (calendar) Return (calendar)

If you are wrong by 20%

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

Profit at day 180 on a cohort basis, across CPI and LTV changes
CPI change 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 calendar: the first day cumulative revenue crosses cumulative spend. Not the day a single cohort pays back, and not day one of an empty form — money has to go out before it can come back.
  5. ROAS at D7/D30/D90 and profit at D90/D180 are cohort numbers: total installs × LTV at that age ÷ budget. They answer “once every install is 30 days old”. The table answers “on day 30 of the campaign”, which is a smaller number while the late cohorts are still young.
  6. Break-even CPI = (1 + organic uplift) × LTV at the horizon.
  7. CPI is treated as constant. In a real campaign it rises as you scale and as creatives fatigue. We have no figure for that we can source, which is what the sensitivity table is for — read the CPI +20% row as the likelier one.
  8. Revenue here is net of the store cut only if your LTV input already was. The tool cannot tell which you typed, so check.