Price an acquisition target in qualified players rather than installs
A qualified-player target implies an install requirement only after the qualification fraction is specified. A reproducible illustrative example with editable inputs and explicit limits.
A qualified-player target implies an install requirement only after the qualification fraction is specified. This worksheet translates that target into a media envelope with an assumed CPI. Neither input is a country benchmark; comparable cohort evidence must be supplied before using them in a real plan.
Define the calculation before using it
Define the qualifying event, horizon and eligible cohort. Divide the target by the assumed qualification fraction, round required installs upward, then multiply by CPI. Keep the install requirement visible so the cost is not presented as a precise budget detached from the funnel assumptions that produced it.
Work through the synthetic example
A target of 1,000 qualified players, a 20% qualification assumption and CPI of two imply 5,000 installs and 10,000 media units. An overstated qualification fraction can make the plan insufficient even when CPI matches expectations. That is a funnel assumption to investigate before diagnosing a bidding failure.
| Output | Worked-example result |
|---|---|
| Required installs | 5000.0000 |
| Implied media budget | 10000.0000 |
Use the artifact and preserve its assumptions
Open the editable calculator to change the inputs and inspect the sensitivity view. The CSV records synthetic inputs and expected outputs; the JSON fixture keeps the equations available for reproduction. These calculations have been checked against the stated example. No measured campaign data is included.
The sensitivity rows vary only rate by 20% below and above the entered value. They are scenarios, not confidence limits or a forecast distribution. A row outside the model’s constraints is labelled rather than turned into a plausible-looking result. Save the chosen inputs with the decision so another reader can distinguish a changed assumption from a changed formula.
Evidence and limits
Google describes conversion lag as a reason recent CPA and ROAS can change. The worksheet’s scenario assumptions are supplied by the reader, not generated or endorsed by Google’s forecast system. See About conversion lag reporting, especially “How it works; Campaign types eligible for conversion lag reporting”.
The model does not supply an expected qualification rate. Use mature comparable cohorts and investigate changes to the creative, event definition and player population.
Background: Annual and quarterly UA budget planning and A Q4 UA Budget Planning Checklist Before the Rush. These existing articles provide context; the present calculation does not verify every archived claim.
