A lot of teams assume that if a creative performs well or poorly, the answer lives inside the creative itself — the copy, the visual, the hook. In reality, the signal is buried under several layers of structural problems that make creative analysis in Google Ads extraordinarily noisy. Here's a breakdown of the four most common traps.
Your PMax or App campaign may be running what looks like strong creative assets — but 90% of actual performance could be coming from Search placements entirely, not from the creative assets you're evaluating.
On top of that, older creatives tend to dominate delivery simply because they've already accumulated historical performance data. The algorithm knows what to expect from them. New creatives, even genuinely better ones, may barely receive impressions regardless of their actual potential — because the system hasn't learned to trust them yet.
Sometimes a creative "fails" not because it's bad, but because the campaign environment is structurally hostile to it. Overly aggressive setup suffocates performance before the creative gets a fair evaluation. Common culprits include:
Meanwhile, other creatives in better-configured campaigns accumulate data steadily and appear to "perform well" — even if the creative itself is mediocre.
In most Google Ads setups, you can't reliably connect the specific creative a user was shown with what that user did afterward. You're almost entirely dependent on Google's attribution model — which is a black box in its own right.
This makes it nearly impossible to measure the downstream signals that actually matter for business outcomes:
Machine learning for creative performance sounds attractive in theory. In practice, it runs into a fundamental problem: creatives change constantly, and most individual assets never accumulate enough statistically significant data before being replaced by new iterations.
The lifecycle of a creative in a live campaign is often too short and too sparse to train a reliable model against. You end up with a dataset full of noise and truncated runs — which is exactly the wrong input for predictive modeling.
Given these four structural constraints, creative optimization in Google Ads is often less about identifying the "best creative" in any meaningful sense — and more about navigating a set of interconnected systems:
The teams that do this well aren't necessarily the ones with the best creatives — they're the ones who've built a rigorous understanding of the environment those creatives are operating in.
Curious how other teams deal with this — especially for PMax and App campaigns. Are you building custom attribution layers? Using third-party tools? Accepting the noise and working around it? Would love to hear what's actually working in practice.