Measuring the impact of Google AI Overviews is no small feat. Google Search Console (GSC) includes performance data for URLs featured in AI Overviews, but it doesn’t allow you to isolate this data specifically. During a recent interview with Aleyda Solis, Google’s Danny Sullivan indicated that an AI Overview filter in GSC is unlikely to be introduced anytime soon, if at all. And Google leaders have stated that they have seen increased engagement and higher quality clicks from AI Overviews. Without that data visible in GSC, can we take Google at their word?

Fortunately, third-party tools like Semrush, Ahrefs, and Authoritas offer insights that can complement GSC data. These tools can identify which queries trigger AI Overviews and the URLs that rank within them, providing a starting point for analyzing their impact. By blending this data with GSC metrics, we can begin to piece together a clearer picture of how AI Overviews might influence visibility, clicks, and overall performance.

In this article, we’ll first examine how Google reports AI Overview performance data. Next, we’ll explore the capabilities offered by third-party tools. Then, we’ll demonstrate how to combine data from tools like Semrush with GSC reports to measure the impact of AI Overview visibility. By aligning query and URL data from SEO tools with GSC metrics such as impressions, clicks, and positions, we’ll determine the “true” impact of AI Overview visibility.

How Google Search Console Tracks AI Overview Performance

Performance data for URLs featured in AI Overviews is included in GSC, though there is no way to separate or filter this data specifically for AI Overviews. Additionally, any data from the “AI Overviews and more” experiment in Search Labs is not reflected in GSC reports. How is AI Overview data included in GSC?

Tracking Clicks

When a user selects a link to an external website from an AI Overview, that interaction is recorded as a click in GSC. This process mirrors how Google tracks clicks for other search features, ensuring consistency in reporting.

Counting Impressions

Impressions for links in AI Overviews are measured under the same rules as other search results. A URL is credited with an impression if its link is visible in the searcher’s browser. This includes cases where the user scrolls or expands the AI Overview to reveal additional links.

Understanding Position

Google assigns a single position to the AI Overview in the search results, often placing it at the top of the page. All URLs within the AI Overview inherit that position in GSC. For example:

If the AI Overview is ranked in position 1, all linked URLs within it will also be reported as occupying position 1, even if those URLs appear lower in organic search results. See below:

This approach aligns with how Google consolidates data for search features like the local pack and featured snippets.

Third-Party Tools for Tracking AI Overview Performance

A variety of AI Overview tracking tools have emerged to fill some of the gaps left by GSC, offering solutions to help SEOs, businesses, and website owners monitor AI Overview data and adapt strategies accordingly.

Key Features of Third-Party Tools

These tools provide capabilities that extend beyond what GSC currently offers, including:

• AI Overview Presence Tracking: Monitor the appearance of AI Overviews in search results and identify which queries or keywords trigger them.
• Keyword and Domain Insights: Analyze which keywords lead to AI Overviews and which domains are frequently cited.
• Competitor Analysis: Understand how competitors perform in AI Overviews, even if they don’t rank highly in organic search results.
• Historical Data and Trends: Track the evolution of AI Overviews over time to identify patterns and shifts in visibility.
• Performance Metrics: Gain insights into impressions, clicks, and other metrics specifically tied to AI Overview visibility.

Tools Overview

Here are examples of what third-party platforms offer:

• Free Tools: Platforms like Advanced Web Ranking (AWR) provide free tools for tracking the evolution of AI Overviews and their impact on specific industries or search intents.
• Subscription Tools: Tools like Ahrefs, Semrush, seoClarity, and Authoritas include AI Overview tracking as part of their paid offerings, integrating data on keyword performance, competitive insights, and visibility analysis.
• Specialized Tools: Platforms such as Market Brew and ZipTie offer unique features like content vectorization, embedding similarity analysis, and large-scale keyword tracking for AI Overview performance.

As AI Overviews evolve, these tools continue to adapt, introducing new features to help website owners and SEOs remain competitive.

Estimating Performance Impact from AI Overview Data

Measuring the performance impact of AI Overviews requires creative data blending, as Google doesn’t provide isolated metrics for these features in Search Console. By combining data from tools like Semrush and GSC, you can gain valuable insights into visibility and performance.

Here, we’ll outline two approaches: the net impact and absolute impact of AI Overview visibility.

Measuring the Impact of AI Overview Visibility

Understanding the impact of AI Overview visibility requires combining data from GSC and a third-party SEO tool such as Semrush or Authoritas. The third party tools identify which of queries and URLS you have AI Overview visibility for. GSC provides the performance data. The goal is to quantify the gains and losses in performance metrics—like clicks, impressions, CTR, and average position—resulting from changes in AI Overview visibility. This method, while not perfect, allows you to capture the estimated impact by accounting for both positive and negative effects.

1. Collect the Data

From Google Search Console: Export a comparison report for the last 7 days and the previous 7 days. This will include metrics such as clicks, impressions, CTR, and average position for your top queries.

From Semrush (or a similar tool): In Semrush, go to the Organic Search Report. Select the same time period as the last 7 days in your GSC data. Select SERP Features and Export. The goal here is to identify the queries that gained or lost visibility during the comparison period.

Semrush started collecting data for AI Overviews in September, allowing you to repeat this process over several 7-day time periods to build a larger dataset for calculations. A broader sample size enhances the accuracy of your insights and helps identify consistent patterns in the impact of AI Overview visibility on performance metrics.

We also have to take into account how often the links in AI Overviews change. By using shorter time periods, we can reduce the effects of volatility, providing a clearer picture of trends.

2. Align the Datasets

Match queries and URLs from GSC with those in your third-party data source using the query. This step can be done in Excel or Google Sheets with Vlookups, Python, or another data processing tool.

3. Calculate Gains and Losses Separately

For each query, calculate changes in performance metrics across clicks, impressions, position, and click-through rate by comparing the current period (last 7 days) with the previous period:

• Gains: Queries where AI Overview visibility was gained.
• Losses: Queries where AI Overview visibility was lost.

Key Points:

• Do not use absolute values for query-level changes. If visibility in AI Overviews correlates with a negative impact for a query (e.g., clicks decreased), this should be reflected in the calculation.
• Summarize total gains and total losses separately to understand their individual impact.

4. Quantify Overall Impact

To distill the impact of AI Overview visibility into a meaningful metric, calculate both the net impact and the average absolute impact:

Net Impact of AI Overview Visibility

Formula:

Net Impact (%) = Gains Impact (%) − Losses Impact (%)

This calculation reflects the overall directional effect of AI Overview visibility across your dataset, balancing the positive contributions (gains) and negative effects (losses). A positive net impact indicates that AI Overview visibility is driving an overall improvement in performance, while a negative net impact suggests a decline.

Average Absolute Impact of AI Overview Visibility

Formula:

Average Absolute Impact (%)=(∣Gains Impact (%)∣+∣Losses Impact (%)|) / 2

This calculation measures the magnitude of the impact of AI Overview visibility, independent of whether the effect is positive or negative. It provides a clearer understanding of how much performance is influenced by visibility changes, regardless of direction. This metric is particularly useful for assessing the scale of AI Overview visibility’s influence on key metrics.

Scenario:

Calculation Example:

• Click Gains Impact: +3%
• Click Losses Impact: -2.5%

Calculations:

Net Impact on Clicks: Net Impact (%)= 3 − 2.5 = 0.5%
Interpretation: AI Overview visibility results in a 0.5% net increase in clicks.

Average Absolute Impact: Average Absolute Impact (%)=3+2.52=2.75%
Interpretation: The magnitude of impact (whether gain or loss) is 2.75% for clicks.

5. Interpret the Results

Once the calculations are complete, you can answer the following questions about AI Overview visibility:

1. Does it help or hurt? If the net impact is positive, AI Overviews likely drive additional visibility and engagement.
2. What is the scale of the effect? The average absolute impact reflects the typical change in performance for queries influenced by AI Overviews.
3. Are gains outweighing losses? Comparing total gains and losses shows whether inclusion in AI Overviews consistently adds value.

What This Approach Tells Us

This process ensures that:

1. The true net effect is calculated: By balancing gains and losses, you can assess whether AI Overviews have an overall positive or negative influence.
2. Magnitudes of change are understood: Absolute values help discern the “true” impact of AI Overview visibility on performance metrics.

This method provides a structured way to evaluate whether optimizing for AI Overview visibility is worth your effort and resources. With clear metrics, you can make informed decisions about how AI Overview visibility contributes to your site’s success.

Assessing AI Overview Impact in My Work

In my experience with clients, I have not found a significant impact from AI Overview visibility—at least not yet. In client campaigns where AI Overview visibility was achieved, I’ve observed mixed results. For some queries, visibility in AI Overviews correlated with modest increases in clicks or impressions. However, for others, inclusion in the AI Overview appeared to siphon potential traffic away, as users seemed to find the summary sufficient without needing to click through to the source URL. This aligns with broader concerns in the SEO community about zero-click searches reducing organic traffic opportunities.

Why This Matters

The lack of a significant impact in my own findings doesn’t diminish the importance of monitoring and understanding AI Overviews. This feature impacts how Google presents information, and they will likely continue to change. As Google refines AIOs and searchers become more accustomed to consuming information through these interfaces, their influence could grow.

Balancing Effort and Impact

It’s important to approach AI Overview optimization pragmatically. Given the current limitations in measuring their performance, I recommend continuing to prioritize traditional SEO strategies that have a proven track record while keeping a watchful eye on AI Overview trends. Tools like Semrush and Authoritas can help identify opportunities without requiring a complete shift in focus. By balancing resources, businesses can remain competitive while positioning themselves to take advantage of AI Overview visibility as its role in search evolves.

Limitations

While combining data from GSC and third-party tools like Semrush can provide valuable insights into AI Overview performance, several limitations must be considered.

Query Alignment Challenges
Queries identified by third-party tools may not always match the data provided in GSC. Discrepancies in query phrasing, regional variations, or slight differences in keyword tracking methodologies can result in mismatched datasets. For example, Semrush had “top charities to donate” whereas GSC recorded it as “top charities to donate to.”

Issues with Volatility
AI Overviews and the URLs they link to can change frequently for the same query. Mordy Oberstein and Semrush found that, over a 90 day period, 91% of AI Overviews changed domains they linked to. This dynamic nature makes it difficult to track consistent performance over time and complicates efforts to measure long-term impact.

Tool-Specific Limitations
Each third-party tool has unique features and limitations. Some may lack historical data or provide only partial insights into AI Overview trends, making it necessary to use multiple tools for a more complete picture.

Data Delays
Both GSC and third-party tools often update data with delays. This lag can hinder real-time performance analysis and limit your ability to respond quickly to changes in search visibility.

Conclusion

Measuring the impact of AI Overviews requires combining data from multiple sources and adopting a strategic approach. While Google Search Console provides essential performance metrics, it lacks the granularity to isolate AI Overview data. Third-party tools like Semrush and Ahrefs fill this gap by identifying queries and URLs associated with AI Overviews, enabling a more comprehensive analysis of their influence on visibility and engagement.

By leveraging these insights, businesses can optimize existing content, target high-value queries, and adapt to evolving trends in AI Overview visibility. A consistent focus on refining strategies and monitoring performance ensures AI Overviews can drive meaningful traffic and conversions, contributing to long-term SEO success.