Compare acquisition cost at the install and qualified-player stages
A cheaper install is not automatically a cheaper qualified player. A reproducible illustrative example with editable inputs and explicit limits.
A cheaper install is not automatically a cheaper qualified player. This lab keeps spend fixed and calculates cost at two nested stages of a clearly defined acquisition cohort. The qualification rule is an input decision, not a hidden judgment supplied by the calculator.
Define the calculation before using it
Define a qualifying event and observation window before opening the worksheet. Count installs and qualifying players from the same cohort, with each player counted once at each stage. Divide spend by each count and report the qualification fraction alongside the costs. Avoid substituting all active users for qualified newly acquired players.
Work through the synthetic example
With 2,000 units of spend, 1,000 installs and 200 qualifying players, CPI is two and cost per qualified player is ten. A qualification share of 20% explains the gap. The worksheet does not decide whether ten is acceptable; that requires the game’s economics and a justified definition of useful player behaviour.
| Output | Worked-example result |
|---|---|
| Cost per install | 2.0000 |
| Cost per qualified player | 10.0000 |
| Qualification share % | 20.0000 |
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 qualified 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’s export schema separates the reporting date, UTC timestamp and event parameters. The worksheet uses simplified aggregate inputs; it does not claim that an export already contains a correctly reconciled cohort. See Google Analytics BigQuery Export schema, especially “event_date, event_timestamp, event_value_in_usd and event_params fields”.
The exercise assumes the qualification group is a subset of the install cohort. Missing identities, repeat installs or a changed event definition need reconciliation before the ratio can support a channel comparison.
Background: UA metrics explained: CPI, ROAS, LTV and payback and Reading an MMP dashboard without fooling yourself. These existing articles provide context; the present calculation does not verify every archived claim.
