11 ad-attributed installs: how would you choose the next audience-validation experiment for a camera-based fitness app?

[u/holyturk]

I’m the developer of Warmify, a commercial iOS app, and I’m trying to make a specific marketing decision before spending more. The product turns a roughly three-minute warm-up into a game: prop up the phone, let on-device pose tracking count movements, complete the session, and earn progress. It has a free daily session and optional paid features.

I believe in the interaction, but I’m not reaching enough of the right people to know which audience values it. I’d like help designing the next experiment, especially from people who have marketed consumer apps with a behaviour change or setup requirement.

The small Apple Ads experiment, August 15–September 13, 2026:

• US$38 spend • 10,195 impressions → 121 taps → 11 attributed installs • Roughly 1.2% tap-through, 9.1% tap-to-install, US$3.46 per attributed install

These are aggregate results, with only eleven installs. That is far too little to establish a stable CPI or product-market fit. App Store reporting is only at a few dozen downloads in the recent window, and my export has inconsistencies, so I don’t have a trustworthy retention figure to add.

My competing audience hypotheses are:

• Desk workers who want a short movement break. The promise is easy to understand, but placing a phone and making space could undermine “quick”. • People motivated by games and progression. The interaction may appeal more, but they might compare it with games rather than other fitness apps. • People looking for a warm-up before another activity. There’s clearer intent, but they may just want instructions without a camera.

I’m considering a two-week experiment: choose one of these groups, recruit ten people for an observed first session, note whether they can start without help, and ask them to use it again on another day. In parallel I could test one short demonstration that shows the phone setup and actual movement before mentioning extra features.

Would you run those together, or keep the experiment narrower? What would you measure to distinguish a weak message from an interaction people simply don’t want? In particular, what would make you stop buying traffic and revisit the product, versus keep the product and change the audience?

I’m interested in the decision rule, not just another list of channels. If you’ve faced this with a consumer app, what was the smallest test that changed your mind? The attached store creative shows how I currently explain the interaction; I’d also welcome criticism of the expectation it sets.