Google Ads · Status at source date: Analyst observation
Rising Shopping CTR can hide falling ad impressions
Mike Ryan reports rising Shopping ad click-through rates alongside falling impressions. He suggests that changes in when Google shows AI Overviews could be one explanation, but the post does not establish causation. Advertisers should read CTR alongside impressions, clicks and conversion outcomes before treating an increase as improved campaign performance.
What changed
Mike Ryan’s original post describes rising Shopping ad CTR alongside falling impressions. His explanation is explicitly a hypothesis: changes in which queries receive AI Overviews could alter the mix of opportunities on which ads appear. The post does not establish that Google uses this serving rule, and the underlying data has not been independently audited. Mike remains the credited analyst; the linked publisher’s coverage is secondary.
CTR is clicks divided by impressions. As an illustrative example, 100 clicks from 10,000 impressions gives a CTR of 1%. If the next period produces the same 100 clicks from 8,000 impressions, CTR rises to 1.25%. That is a 25% relative increase in the rate, even though the advertiser gained no additional clicks. These figures are a worked example, not numbers taken from Mike’s chart or a measured result for an advertiser.
A rate can improve because people respond more strongly to the same opportunity, because the opportunity mix changes, or because both happen together. An aggregate CTR chart cannot distinguish those explanations on its own. The interpretation also depends on the advertiser’s goal: improved efficiency may be useful, while reduced reach could still matter to a business trying to grow total demand.
Why it matters for advertisers
Review the numerator and denominator before describing a CTR increase as a campaign improvement. Put impressions, clicks, cost and conversion outcomes beside the rate, then check whether the same products and periods are being compared. Changes in availability, promotions, budgets or the mix of products may offer alternative explanations worth investigating. This does not disprove Mike’s hypothesis; it makes clear what additional evidence would be needed to separate a platform-wide effect from changes inside a particular account.
What to check next
- Compare impressions and clicks alongside CTR, using consistent date ranges and product groups.
- Check total conversion volume and value before deciding that a higher rate represents better business performance.
- Label the AI Overviews explanation as a hypothesis and record account-level changes that could also explain the pattern.
Sources & contributor credit
- Analyst observation and hypothesis · Checked Mike Ryan, original LinkedIn post
Original publication date unconfirmed. The date above is the source check.
- Discovery coverage · Search Engine Land
This date belongs to the coverage, not the original post.
Source attribution checked
Mike Ryan authored the analysis; the chart identifies Smarter Ecommerce as its data source. Search Engine Land is secondary coverage.
Attribution evidence and limitations
Read Mike Ryan’s complete public post, which distinguishes his interpretation from an established causal explanation. Quoted earnings-call participants are not creators of this chart.
The original post identifies Mike Ryan and his profile. The secondary article points back to his work.
Visually inspected the displayed chart and its smarter-ecommerce.com source label. The underlying data and causal hypothesis have not been independently audited.
Attribution checked . This is a review date, not the original publication date.
- Published on this site
- Article updated
This update reflects the dated source reporting. Availability may have changed. Further coverage of this same development will be added to this page.
