A quick primer
Start with the nature of the tool itself: audience signals are audience suggestions that help Google AI optimize toward the goals you've selected, and adding them is optional. The same page carries a critical caveat: PMax may still serve outside your signals if the AI predicts a strong chance of conversion — meaning a signal is never a hard boundary on where impressions can go.
Signals are set at the asset group level, not the campaign as a whole. A Performance Max campaign is built around asset groups, each pulling creative together around a theme or audience, and that's the level where audience signals apply. Google's own guide to building an asset group confirms signals are one of several inputs alongside creative and landing pages, meant to guide the model, not serve as an exhaustive recipient list.
The practical takeaway: since a signal isn't a targeting boundary, you can't answer "how much impact did this signal group have" through a direct attribution report — no such report exists. The only documented path is comparing signal composition and quality (customer data, custom segments, demographics) against actual changes in traffic volume and quality after they're added or changed, treated as a before/after comparison rather than in-the-moment attribution.
What to check before you touch anything
- What level the signals are set at — if the campaign has multiple asset groups, each may carry its own, different signals; comparisons only make sense within one group.
- Which signal types are in use in the group — customer data (customer lists, website visitors), custom segments, demographics, additional segments (in-market, affinity, life events) — the effect isn't the same for each type.
- Whether there's a stable period before the signal change — a fair before/after comparison needs a comparable time window with no other simultaneous changes (budget, creative, bids).
- Whether the group has enough traffic for a meaningful comparison — on a low-frequency asset group, the effect of a signal change can get lost in noise.
- Whether "signal" is being confused with "exclusion" — a signal only hints; exclusions work differently and are covered separately.
Possible approaches
- Compare the group's traffic volume and quality over comparable periods before and after a signal change, rather than looking for direct attribution of impressions to a specific signal.
- If the campaign has multiple asset groups with different signals — compare them against each other as a relative benchmark: a group built on specific, real customer-data signals (customer lists) usually behaves more predictably than one built on broad segments (affinity, in-market).
- If traffic doesn't change noticeably after adding signals, that doesn't necessarily mean the signal "isn't working" — the model may already be converging on a similar audience on its own, and the signal simply isn't shifting the distribution of impressions much.
- Before drawing conclusions about a specific signal group, rule out coarser causes of low traffic (budget constraints, weak creative, a narrow listing group), since signals aren't the only variable affecting impression volume.