Shopping ad performance is undergoing a quiet but consequential transformation. Click-through rates are climbing while impressions are declining, a pattern that may be directly linked to how Google deploys AI Overviews in search results. According to an analysis by Mike Ryan, head of ecommerce at Smarter Ecommerce, the data across thousands of campaigns reveals a consistent divergence: advertising impressions are shrinking, but the clicks that remain are converting at a higher rate. This inversion of the familiar “crocodile effect” seen in organic search — where AI Overviews generate visibility without traffic — demands a fundamental rethinking of how advertisers evaluate campaign health, benchmark performance, and allocate budget in an AI-mediated search environment.
Shopping Ads Show Opposite Pattern of the SEO ‘Crocodile Effect’
The term “crocodile effect” has been used in SEO circles to describe what happens when AI Overviews appear in search results: publishers see a wide mouth of impressions but very few clicks — a big visual presence with little substance in terms of traffic. For organic content, AI Overviews can suppress click-through rates by answering queries directly within the search results, reducing the incentive for users to visit publisher pages.
Shopping ads, however, appear to be experiencing the reverse phenomenon. Impressions are dropping, but among the smaller pool of users who do see Shopping ads, a greater proportion are clicking through. This is not necessarily a sign of improved ad creative, better bids, or stronger product feeds. It may be a structural shift in how Google decides which queries receive Shopping ad placements in the first place.
Ryan observed this pattern in Smarter Ecommerce’s Market Observer benchmark data, which aggregates performance across a broad set of advertiser accounts. The rising CTR was initially puzzling because it did not correspond to a meaningful increase in total clicks. When Optmyzr published separate ecommerce data showing Shopping CTR up 17 percent year over year — closely matching the roughly 20 percent increase Ryan was seeing in his own data — he decided to investigate more systematically.
What the Data Actually Shows Across Thousands of Campaigns
Ryan examined data from thousands of Shopping and Performance Max campaigns spanning hundreds of advertiser accounts. The pattern was remarkably consistent. Clicks were often relatively flat or only slightly down compared to previous periods. Impressions, however, had fallen far more noticeably. When impressions decline faster than clicks, CTR rises automatically — it is a mathematical inevitability, not necessarily a reflection of better ad performance.
This distinction is critical for advertisers who monitor CTR as a primary success metric. A rising CTR can mask declining absolute performance if impression volume is eroding simultaneously. An advertiser could see a 20 percent improvement in CTR while total clicks remain flat or decline, meaning the campaign is reaching fewer potential customers even though those it does reach are engaging at a higher rate.
The data does not suggest that Shopping ads are generating significantly more customer interest. It suggests they are being shown less often, and the impressions Google retains are concentrated on queries where users are more likely to click.
Optmyzr Data Confirms the Broader Trend
Optmyzr’s independent analysis showing a 17 percent year-over-year increase in Shopping CTR provided external validation of what Ryan was observing internally. The proximity of that figure to the roughly 20 percent increase Ryan had documented in Smarter Ecommerce’s data made it unlikely that the pattern was an artifact of a single dataset or a specific advertiser segment. The trend appears to be widespread across the Shopping ad ecosystem.
The AI Overviews Hypothesis: ‘Experience Switching’ at Work
Ryan’s central hypothesis is that Google’s AI Overviews may be functioning as a filter for Shopping ad delivery. The mechanism he theorizes is straightforward: Google may be more likely to serve AI Overviews for search queries that have a lower predicted likelihood of generating an ad click. Conversely, it may preserve traditional Shopping ad placements for queries where users have demonstrated a stronger click propensity or commercial intent.
If that is happening, Google could reduce the total number of Shopping ad impressions without reducing clicks proportionally. The remaining impressions would be concentrated on higher-intent queries, naturally inflating the aggregate CTR. This would allow two seemingly contradictory outcomes to coexist: AI Overviews could suppress some advertising opportunities while the ads that do appear continue to monetize effectively.
Ryan describes this dynamic as “experience switching.” Google already serves very different Shopping experiences in its main search results depending on the query. Sometimes users see the familiar product grid with multiple merchant listings. Other times they see a more compact carousel or a single sponsored product. AI Overviews add another layer of variation.
Shopping Ads and AI Overviews Rarely Appear Together
One piece of anecdotal evidence supporting the hypothesis is that Ryan rarely observes Shopping ads and AI Overviews appearing together on the same search engine results page. It tends to be one or the other. If this pattern holds systematically, it suggests Google is making deliberate choices about which experience to serve for each query, rather than layering both simultaneously.
This selectivity would represent a meaningful shift in how Google manages its search results real estate. Historically, the SERP has been a layered environment where organic listings, paid ads, knowledge panels, and other features coexist. AI Overviews may be changing that architecture by forcing Google to choose which experience dominates for a given query, at least in the current implementation.
Why Rising CTR Doesn’t Mean What You Think It Means
For paid media managers and ecommerce advertisers, the implications are significant. CTR has long been one of the most commonly monitored metrics in Shopping campaigns. It is used to evaluate ad relevance, compare performance across campaigns, and inform optimization decisions like bid adjustments and feed optimization.
If CTR is now being influenced — or driven — by Google’s decisions about which queries receive Shopping ads, the metric becomes less reliable as a standalone performance indicator. A campaign with a rising CTR could be performing worse in absolute terms if impression volume is declining. Conversely, a campaign with a stable or declining CTR could be performing better if impressions are expanding into less commercially qualified queries.
Advertisers need to look beyond CTR and pay closer attention to impression volume, total clicks, and overall traffic levels when judging campaign performance. The relationship between these metrics matters more than any single number in isolation.
What the Featured Snippet Answer Looks Like
For readers who want a concise, direct answer to the core question: What is causing Shopping ad CTR to rise while impressions fall? The leading hypothesis, based on analysis by Mike Ryan of Smarter Ecommerce, is that Google may be using AI Overviews to replace Shopping ad placements on queries with lower commercial intent, while preserving ad impressions for searches where users are more likely to click. This selective delivery reduces total impression volume but concentrates clicks among a smaller, higher-intent audience, mathematically inflating the aggregate click-through rate.
The Timing Factor: Correlation vs. Causation
Ryan is careful to note that the data does not prove AI Overviews are causing the shift in Shopping ad metrics. The timing could be coincidental. AI Overviews were rolled out broadly in 2024 and expanded throughout 2025, and the CTR trends Ryan observed emerged during a similar window. But other factors could be contributing or even primarily responsible.
Changes in Google’s ad auction dynamics, shifts in consumer search behavior, macroeconomic conditions affecting shopping patterns, or updates to Google’s Shopping ad policies could all influence impression volume and CTR. The AI Overviews hypothesis is compelling because it offers a mechanistic explanation that aligns with what Google has publicly stated about its AI-first approach to search. But it remains a hypothesis, not a proven causal relationship.
Ryan acknowledges that AI Overviews could be contributing through a different mechanism than the one he proposes, or that something else entirely could be driving the decline in impressions. The correlation is suggestive but not definitive, and advertisers should treat it as a working theory rather than a settled conclusion.
Historical Context: How Search Experiences Have Evolved
The current situation echoes earlier transitions in search advertising. When Google first introduced Universal Search, blending images, videos, and news into organic results, advertisers had to adapt to a more crowded SERP. When Google expanded Shopping Ads from a separate tab into the main search results, the competitive dynamics changed again. Each shift required advertisers to recalibrate their metrics and strategies.
AI Overviews represent a similar inflection point, but with a twist. Earlier changes typically added more elements to the SERP without removing existing ones. AI Overviews may be operating differently by displacing both organic and paid elements in certain query categories. If Google is choosing between showing an AI Overview or showing Shopping ads for a given query, that is a zero-sum dynamic that previous search updates largely avoided.
The longer-term trajectory, however, may move back toward integration. Ryan expects Google to evolve from the current “either/or” approach toward “both/and” experiences as it develops AI-native advertising formats and incorporates Shopping ads directly within AI Overviews. Several Google announcements and patent filings suggest the company is exploring ways to monetize AI Overviews with native ad units, including product listings displayed within the AI-generated response.
What Advertisers Should Monitor Going Forward
For advertisers navigating this transition, the key signal is not simply that Shopping CTR is rising. It is understanding why it is rising and whether the underlying causes are structural or temporary. If higher CTR is being driven partly by Google showing Shopping ads less frequently and on more commercially valuable queries, traditional CTR benchmarks could start telling a very different story about campaign performance.
Advertisers should consider several adjustments to their measurement and optimization frameworks. First, track impression volume and total clicks alongside CTR, and watch for divergences between these metrics. A widening gap between declining impressions and stable or rising CTR warrants investigation. Second, segment performance by query type or product category to identify where impressions are being lost and where CTR is improving. Not all segments will be affected equally. Third, review campaign settings that influence where and when Shopping ads appear, such as targeting, negative keywords, and device bid adjustments, to ensure alignment with Google’s evolving SERP composition.
Fourth, consider using impression share data more actively. If impression share is declining while CTR is rising, it may indicate that Google is restricting ad delivery to a narrower set of queries, which could limit reach even if conversion rates remain healthy. Fifth, test Performance Max campaigns against standard Shopping campaigns to see which format is more resilient to AI Overviews’ impact. Performance Max campaigns have broader placements across Google’s inventory, which may offset some of the impression compression seen in standard Shopping.
Strategic Implications for Budget Allocation
If AI Overviews are systematically reducing Shopping ad impressions on lower-intent queries, advertisers may need to rethink how they allocate budget across the funnel. Historically, Shopping ads have captured demand across a wide spectrum of commercial intent, from broad product research to specific purchase queries. If Google is now filtering out the lower-intent impressions, Shopping ads become more of a bottom-of-funnel channel, and advertisers may need to invest more heavily in upper-funnel channels like demand generation, brand search, or social media to fill the gap.
This would represent a significant change in the role Shopping ads play in the overall media mix. It could also affect how advertisers model attribution, since the path to purchase may shift as Shopping ads become less visible during the research phase of the customer journey.
What to Watch for in the Coming Quarters
Several signals will indicate whether Ryan’s hypothesis is correct and whether the trend is accelerating or stabilizing. If Google begins testing or rolling out Shopping ads within AI Overviews more broadly, that would suggest the company sees the current “either/or” approach as a transitional phase rather than a permanent state. If impression volumes stabilize or recover while CTR remains elevated, it could mean the market is adjusting to a new equilibrium.
Advertisers should also watch for changes in Google’s public statements about AI Overviews monetization. Google has discussed ad placements within AI Overviews in general terms, but specific details about Shopping ad integration are still emerging. Any announcements about native Shopping units inside AI Overviews would signal a shift toward the “both/and” experience Ryan anticipates.
On the data side, third-party benchmarks from platforms like Smarter Ecommerce, Optmyzr, and others will be valuable for tracking whether the trend is continuing, accelerating, or reversing across different verticals and campaign types. Advertisers should compare their own account-level data against these benchmarks to identify anomalies that may indicate account-specific issues rather than market-wide shifts.
Ultimately, the rise in Shopping CTR amid falling impressions is a reminder that metrics do not exist in a vacuum. In an AI-mediated search environment, the relationship between what Google shows and how users respond is becoming more complex, more dynamic, and more dependent on Google’s algorithmic choices. Advertisers who treat CTR as a standalone success signal risk misreading the market. Those who dig into the underlying mechanics of impression volume, click propensity, and query-level intent will be better positioned to navigate a search landscape that is evolving faster than at any point in the past decade.
