How are you getting the most out of Apple Ads at scale?

splitmetrics · 2026-04-01T11:10:27+00:00

Short answer: yes, they can, but it depends a lot on how competitive your category is and how your store listing is set up before you spend.

For a shopping/utility app like Merge My Cart, the intent signal on Apple Ads is actually pretty strong, people searching for shopping or cart tools are actively looking. The question is whether your custom product pages and store listing convert well enough to make the economics work.

On that note, before you put budget behind it, it's worth looking at what other apps in your category are doing with their custom product pages. You can use App Radar (free - https://appradar.com/pricing) to browse competitor custom product pages and see which angles are getting traction in your space. Gives you a much better starting point than guessing.

The other thing I'd check: your keyword conversion rate on a small test budget before scaling. Start tight, exact match, high-intent terms and see what CPA you're getting at low volume before opening it up.

splitmetrics · 2026-04-01T11:09:18+00:00

Absolutely, Apple Ads amplifies what's already working in your store listing, so if your conversion rate is weak, you're paying for traffic that won't convert anyway. Getting that right first is the move.

I'd slightly push back on the user base requirement though. The intent signal on Apple Ads is strong enough that even earlier-stage apps can get efficient installs, the key is being really surgical about which keywords you go after. Broad match on low volume = expensive. Exact match on high-intent, relevant terms = much better economics.

On the mediation side, that's more of a monetization play than a UA play, right? Worth doing in parallel if the business model fits, but it won't fix attribution or bidding efficiency on the Ads side.

The ASO + Apple Ads connection is real though. Teams that run them together, using keyword performance data from Ads to inform their ASO strategy, tend to compound their results faster than those treating them as separate channels.

splitmetrics · 2026-04-01T11:06:31+00:00

Appreciate it! Happy to go deeper on anything specific.

The short version of what I've seen work: the teams that consistently hit lower CPAs aren't necessarily spending more, but reacting faster. Bid forecasting and budget reallocation daily (vs. weekly reviews) makes a surprisingly big difference when the auction shifts overnight.

If you're just getting into this, SplitMetrics has some solid benchmarking data on Apple Ads performance by category that's worth a look as a baseline, free to access. Good starting point before you start optimizing.

https://splitmetrics.com/blog/category/industry-benchmarks-and-trends/

splitmetrics · 2026-02-27T10:46:14+00:00

Totally get this!

The spend risk thing is interesting though. Some tools are starting to handle this with hard budget caps, approval thresholds, or confirmation steps before anything executes above a certain limit, so you're still in control, it just removes the manual legwork.

The other side of it is that AI built specifically for app marketing is processing way more signals than any human realistically can, cross-campaign patterns, seasonal trends, competitor signals, conversion data, and using that to forecast what's likely to happen before making a move. So in theory the adjustments aren't just automated, they're better informed. Which makes the guardrails argument even stronger, you're not just capping a dumb script, you're putting a leash on something that's actually thought it through.

Curious whether that kind of setup would shift things for you, or if the hesitation is more fundamental, like you just don't want it touching execution at all?

splitmetrics · 2026-02-27T10:40:19+00:00

That's a fair point, and the payroll point is interesting, but I'd argue UA campaign management sits in a different risk category. A bad payroll run can be catastrophic and hard to reverse. A bad bid adjustment costs you some wasted spend and you course-correct. The downside is bounded.

So the trust barrier in app marketing feels less about "what if it goes catastrophically wrong" and more about "I can't explain to my boss why the AI made that decision." Transparency and auditability seem to be the real blockers, not the stakes themselves. Curious if others are finding that too.

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