← All questionsResponsive Search Ads

How do I evaluate whether multiple RSAs in one ad group are cannibalizing each other?

A quick primer

When multiple RSAs run in one ad group, the system has to decide, before each individual impression, which of your own ads to enter into the auction — and only then, which combination of assets to assemble inside it. That effectively turns multiple RSAs into parallel forecasting models competing for the same auctions inside your own account, rather than against outside competitors. The industry describes the direct consequence of this as impression fragmentation: instead of one ad quickly accumulating enough data to self-optimize, impressions get spread across multiple ads, each of which ends up under-supplied with data — and the whole ad group underperforms as a result, not because the copy is weak, but because none of the variants ever built up enough statistics.

Independent research from Adalysis quantifies the skew in impression distribution: with comparable pinning, the ad with the higher Ad Strength gets more impressions in 56.8% of cases — a meaningful but not absolute tilt, and one that compounds with each additional RSA added to the group. It's worth distinguishing two genuinely different phenomena that look identical on the surface ("one ad gets far more impressions than the other") but call for different diagnoses: (1) healthy competition for the same audience/semantics, where one piece of copy genuinely outperforms the other — an expected outcome of an A/B comparison, not a problem in itself; and (2) cannibalization in the narrow sense — a situation where both ads target the same semantics, both are potentially viable, but both are artificially starved of data purely because traffic is split, so neither variant ever reaches stable performance.

What to check before you touch anything

  • The impression split between the ads in the group over a comparable period for each — how uneven it really is, and whether the skew is stable over time or still forming.
  • The overlap in semantics/search terms that actually trigger each ad — via ad-level segmentation in the search terms report. High overlap means genuine internal competition for the same queries; low overlap means the ads serve different segments, and "cannibalization" in the strict sense doesn't really apply.
  • Whether the group's overall traffic is enough to justify splitting it across multiple ads at all — fragmentation is especially costly on a low-frequency group.
  • Whether pinning differs between the ads — with different pinning, the impression comparison won't be clean, since part of the skew is structural rather than content-driven.
  • Whether performance metrics (not just impressions — CTR, conversions, cost per conversion/ROAS) line up in favor of the same ad that's leading on impressions — if so, that's more likely a healthy selection process; if the impression leader isn't the conversion leader, that's a more concerning signal.

Possible approaches

  • If semantic overlap is high and one variant consistently and statistically significantly wins on the relevant business metrics — turn off the losing ad, consolidating data and traffic on the one that works.
  • If semantic overlap is high, there's an impression skew, but the impression leader isn't the conversion/CPA leader — that's a specific sign of cannibalization driven by the algorithm's CTR preference rather than genuinely better copy. A controlled experiment with proper traffic splitting is warranted here, not an immediate shutdown of the lower-impression variant.
  • If semantic overlap is low — the ads aren't really cannibalizing each other; they're serving different segments. The right move is restructuring the group (splitting the relevant semantics into separate ad groups), not fighting a "cannibalization" problem that doesn't actually exist here.
  • If the ad group is low-frequency and simply doesn't have enough traffic to support multiple ads — consolidate into a single RSA by default, without waiting for measurable cannibalization to show up: on low traffic, splitting the data hurts regardless of how the impressions end up distributed.
  • Recheck the impression split and semantic overlap between the group's ads on an ongoing basis, not as a one-off — cannibalization can emerge over time as the algorithm builds up a preference for one variant.