Separate payer concentration from average revenue per payer

An average can be dominated by a small set of payers. This lab calculates the share of revenue contributed by a selected high-revenue group and compares that group’s mean with the remainder. It does not identify whales or predict whether high spend will persist.

Define the calculation before using it

Choose the group-selection rule before looking at outcomes, then enter total revenue and payer count together with the selected group’s revenue and count. Subtract the group from the totals to derive the remainder. All values must cover the same window and revenue definition; refunds and multiple identities can otherwise distort both means.

Work through the synthetic example

In the illustrative cohort, ten of 100 payers contribute 6,000 of 10,000 currency units. The group produces 60% of revenue and averages 600 per payer, while the other 90 average about 44.44. That concentration explains why a few outcomes can move the cohort total, but it does not establish the right acquisition strategy.

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 topRevenue 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 documents refund events separately from purchases and calls for currency alongside value data. The model below is our own aggregate accounting exercise, not a claim about an actual game’s exported revenue. See Measure ecommerce: purchases and refunds, especially “Make a purchase or issue a refund; Recommendations”.

Selection after observing spend changes the interpretation of the comparison. This calculator provides no uncertainty estimate, causal attribution or permission to infer an individual player’s future spending.

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.