Ad-revenue and IAP games need different UA maths

A UA team that runs an ad-supported puzzle game and an IAP-led strategy game with the same LTV model, the same payback window and the same confidence rules is running one of them wrong. The two businesses convert an install into cash through mechanisms that fail in opposite ways: ad revenue is a broad, shallow stream whose main risk is the price of the stream, while IAP revenue is a narrow, deep one whose main risk is whether the few payers show up at all. The UA maths that manage those risks are different, and the widespread habit of blending both into a single ROAS is not a simplification. It is a mistake with a direction.

Some analysts will object that hybrid monetisation has made the distinction obsolete. The opposite is true. A hybrid game has both risk profiles at once, which means its UA team needs to run both sets of maths and then decide how to weight them, rather than collapsing them into one number that hides which risk is currently moving.

Two revenue mechanisms, two failure modes

In an ad-supported game, LTV is roughly sessions per user, times impressions per session, times the price of an impression. The first two are behavioural and reasonably stable once a cohort is a week old. The third is a market price the studio does not control. eCPMs move with advertiser demand and with seasonality, and they move again when networks change how they bid. The price of an impression depends on format and demand, and it can shift with no change in player behaviour at all.

The consequence is that an ad-game cohort's LTV forecast can be exactly right about the players and badly wrong about the money. The risk lives on the revenue side, not the retention side.

In an IAP-led game, a small fraction of payers dominates LTV, and their spend skews heavily. A cohort's 12-month revenue can hinge on a few dozen accounts. The risk is sampling: nothing guarantees that a cohort of 5,000 installs contains those accounts, and early revenue is a weak signal of whether it does. The studio fixes the price of the good it sells, so market risk is low; what varies is the composition of the cohort.

One has stable behaviour and a volatile price. The other has a fixed price and volatile behaviour.

What that does to the standard UA tools

Payback windows behave differently. Ad revenue arrives from the first session, so ad-supported games recover cost early and the tail is thin. A short window is honest for them. IAP games often see their heaviest revenue later, once players are further in, which argues for longer windows and, as the previous UA Ledger piece on payback windows argued, that is affordable only if the studio's money is cheap enough to wait.

LTV confidence behaves differently. For ad games, the retention curve after a week is a solid input and the error bar comes from the eCPM assumption. The right question in the review meeting is "what eCPM did we assume and what happens at 20 percent lower". For IAP games, the retention curve is not enough; the model needs a payer-conversion estimate and a spend-distribution estimate, and both need cohorts large enough to contain the tail. The right question is "how many payers does this forecast rest on".

Attribution behaves differently. IAP revenue attaches to a device and a transaction. You have to estimate ad revenue per user from impression-level data, or model it from network reports, and the accuracy of that estimate varies by mediation stack. A UA team optimising to ad LTV is optimising to a derived number, and the derivation is a source of error that has no equivalent on the IAP side.

The second-order effect: hybrid games import both risks

Where this bites hardest is the hybrid game, now the default in casual. Its cohort LTV is the sum of an ad stream and an IAP stream. Blend them and the forecast inherits eCPM risk from one and sampling risk from the other, while the single ROAS figure obscures which one is moving.

Consider an illustrative hybrid puzzle title where ads are 60 percent of revenue. A cohort comes in ten percent under forecast. If the shortfall is in ad revenue, the likely cause is a market price move, and the right response is to check eCPM by network and possibly hold spend until the price recovers. If the shortfall is in IAP, the likely cause is cohort composition, and the right response is to look at payer rate and channel mix. The same ten percent miss leads to opposite actions, and a blended ROAS gives no clue which is right.

A further wrinkle: the two streams interact. Heavier ad load can suppress IAP conversion; removing ads for payers shifts revenue from one column to the other. Any UA model that treats the two as independent is wrong in a way that gets worse as the hybrid balance shifts.

A framework: split, stress, then weight

Run the two streams as separate models and only recombine them at the end.

For the ad stream, build LTV from behavioural inputs and treat eCPM as a scenario variable, not a constant. Model it at a base level, then again at a low and a high one, and set the ROAS target against the low case. Where the low case fails, the cohort sits exposed to a market you cannot influence.

For the IAP stream, set a minimum cohort size before you let the model speak. Below that size, lean on a genre prior or a studio prior instead of the cohort's own early revenue. Track payer count, not just payer revenue, and treat a forecast built on fewer than a few dozen payers as provisional whatever the ROAS says.

Then weight. The blended target is the sum of the two stream targets, but the confidence rule is the weaker of the two. A hybrid cohort clears the bar when both streams clear their own, not when the sum happens to.

A few habits follow from that. Report ad ROAS and IAP ROAS on separate lines in every review, with the eCPM assumption printed beside the first. Give the two streams different payback windows if the curves justify it, and blend only at the longer one. When a cohort misses, name which stream missed before proposing an action.

Where the market is pushing

Unity's announcement last week that the ironSource network will shut at the end of April removes one bidder from the supply side that ad-supported games sell into, and any change to the buyer set for impressions is an eCPM event before it is anything else. A studio running an ad-heavy title that has not modelled that scenario is carrying a risk its ROAS dashboard cannot display.

That is the practical test of whether a UA team has the maths right. Ask what happens to the plan if eCPMs fall 20 percent, and separately what happens if the next cohort has half the expected payers. A team with one model will give one answer. A team with two will know which of those questions its game is actually exposed to.

Related archive reading

These articles provide related context and remain subject to their stated review status.

• Attribution Windows for Hybrid-Monetisation Games
• 4X games buy whales and pay for tourists