Google App Campaigns vs Apple Search Ads vs Meta Ads for Mobile Game UA

How do these three channels differ?

Google App Campaigns run across Search, Google Play, YouTube and Google’s display network. You supply creative assets, a goal (installs, in-app actions, or value) and a target; Google’s system decides placements and audiences. It is not “Google Search for apps”: you cannot bid on specific queries or cleanly separate intent — the system controls the levers.

Apple Search Ads places ads in App Store search results (and other App Store surfaces), bought on keywords. It is one of the few mobile channels with explicit stated intent, and it measures within Apple’s privacy framework.

Meta (Facebook, Instagram, Audience Network) runs app campaigns largely through its automated Advantage+ products. Performance depends on the signal Meta receives — especially on iOS — and on a steady supply of creative.

When should a game use each?

Google App Campaigns — scale on Android in particular, broad reach, and value-based optimisation once you have enough in-app value events.
Apple Search Ads — capturing category searches on iOS, defending brand terms, and testing store-page messaging against intent.
Meta — creative-led reach and audience modelling, especially for genres with strong visual hooks.

Mobile ad networks (AppLovin, Unity, Moloco, Mintegral, Liftoff and others) and TikTok round out most game UA mixes, particularly for ad-monetized titles.

What goes wrong most often?

Optimising for installs and calling a cheap CPI a win. The system found cheaper installs, as asked; whether they are better users is a separate question.
Crediting brand search fully. Many brand-keyword installs would have happened organically; test incrementality.
Reading iOS results too early. Privacy-framework postbacks are delayed and aggregated.
Too few creatives. Algorithm-led channels need creative volume to find audiences; see the Creative Brief Builder.

How do you compare them fairly?

On matured cohort ROAS by channel at equal age, with marginal returns on recent budget steps, not just blended results. If you have enough data, optimise toward the event closest to value; if not, use the earlier event — but keep checking whether it is still a useful proxy as budget grows.