Apple's new ad slots and Google's Power Pack: what shifted

User-acquisition tooling doesn't usually change enough in a single year to reshape budget allocation, but 2026 is one of the years it did — both major platforms restructured how paid search-driven installs actually get delivered, and treating either change as a minor tooling update is going to leave real performance on the table.

What changed on the App Store side

Apple expanded search ad placements beyond the original auction-based search results slot, rolling out additional positions — a "Today Tab" placement and a "you might also like" product-page slot that appears under competitor listings — starting in early 2026 and reaching global availability across ad markets by the end of that rollout. Existing search-results campaigns became automatically eligible for the new placements without requiring advertisers to opt in or reconfigure anything, which means budgets that were previously spent entirely on the original search-results auction are now, by default, also competing across two additional placement types.

That default eligibility matters because it changes your effective competition and your effective reach without you having done anything — which cuts both ways. It's a genuine opportunity if your creative and bidding strategy adapts to the new inventory. It's a quiet source of wasted spend if your existing campaigns are running on assumptions tuned for a single-placement auction and nobody's checked whether that's still the right configuration now that the auction dynamics changed underneath them.

What changed on the Google side

Google restructured its automated app-campaign tooling around what it's calling a "Power Pack" — Performance Max, Demand Gen, and AI Max working in combination, where Demand Gen builds awareness, AI Max engages users specifically inside Search, and Performance Max coordinates spend across the full funnel at scale. This is a meaningful shift from treating these as three separate campaign types a marketer manually balances, toward a more automated, more opaque system that makes more of the allocation decisions itself.

Separately, co-branded partnership ads for app campaigns now let creators on short-form video platforms display app promotions with custom call-to-action panels directly in their content — a formalization of creator-driven install traffic that used to require a separate, manually managed influencer relationship outside Google's own campaign tooling entirely.

The common thread: more automation, less manual control

Both changes push in the same direction — platforms are increasingly making placement and budget-allocation decisions themselves, inside a more automated system, rather than leaving those decisions to be manually tuned by the advertiser. That's not inherently bad; automated allocation across placements a human couldn't realistically monitor in real time can genuinely outperform manual tuning, especially at the scale and speed decay curves in creative performance now demand.

But it does mean the lever a studio actually controls shifts. You're increasingly not choosing which specific placement your ad appears in — you're choosing the creative, the target audience signals, and the budget, and trusting the platform's automated system to allocate across placements on your behalf. That raises the bar on the inputs you do control, because they're now the only thing standing between "the automated system optimizes well for us" and "the automated system optimizes toward something that isn't actually our best outcome."

What this means for budget planning

The practical shift is toward feeding better signal into these automated systems rather than trying to manually override their placement decisions, which increasingly aren't fully exposed to the advertiser anyway. Creative quality and audience-signal accuracy matter more, not less, in a more automated placement environment — a weak creative asset that used to underperform in one specific placement now underperforms across whatever set of placements the automated system decides to test it in, amplifying the cost of a mediocre asset rather than containing it.

It also means measurement discipline matters more. When a platform's automated system is making more of the allocation decisions, the only way to know whether it's actually working well for your specific game is to measure post-install outcomes carefully — retention, conversion to purchase, actual revenue per install by campaign — rather than trusting the platform's own reported performance metrics as the final word. Automated systems optimize toward the signals they're given, and if the signal you're feeding back is incomplete or delayed, the system optimizes toward a version of "success" that doesn't fully match your actual business outcome.

Where to spend the next planning cycle's attention

Rather than trying to manually chase the new placements individually, audit whether your creative library is strong enough to perform well across a wider, more automated placement mix — since that's now the actual lever available. And check whether your post-install measurement pipeline feeds back into these campaign tools quickly and accurately enough for their automated optimization to actually work in your favor, rather than optimizing toward install volume alone because that's the only signal that reached it in time.

The platforms changed how they deliver installs. What they didn't change is that an install only matters if what happens after it — retention, first purchase, sustained engagement — actually justifies what you paid to get it. See how gamemantra measures what happens after the install, the part no UA platform's dashboard was ever built to answer for you.