Google adds tool to track image search traffic for online

Monika Kruger • October 25, 2026 • 2 min read

Google’s new tool helps ecommerce businesses track image search traffic in Search Console, revealing how visual queries drive product discovery.

The feature appears under Performance > Search results > Search type: Web > Multimodal. Photo: Luca Sammarco/Pexels

Google Search Console has added a new feature that lets ecommerce businesses track how frequently users view or interact with images in search results. This tool addresses a growing trend: shoppers increasingly rely on visual searches to discover products, and merchants now have a way to quantify that traffic.

The feature appears under Performance > Search results > Search type: Web > Multimodal. Unlike traditional text-based queries, image searches do not display specific search terms—only key metrics such as views, clicks, average click-through rates, and position rankings for images in search results. This data helps businesses understand which visuals are driving engagement, even when the exact image is not visible to them.

Google’s image search functionality has been available for years, but this marks the first time merchants can directly measure its impact on their traffic. Users can access image search through Google.com by clicking the camera icon, via Chrome’s new tab interface, or through the mobile app by uploading a photo. The new filter highlights which images generate the most interaction, providing actionable insights for optimization.

To enhance visibility, businesses should submit an image sitemap in Search Console. While platforms like Shopify and WooCommerce already include images in standard sitemaps, a dedicated image-only sitemap can improve tracking precision. Standard SEO practices, such as using descriptive filenames, alt text, and captions, remain essential, as images that rank well in text searches often perform strongly in visual searches as well.

Image quality plays a critical role in user engagement. Low-resolution or blurry photos, which are common in product thumbnails, can reduce clicks. Testing higher-resolution versions of underperforming images can determine whether improved clarity increases interaction. For example, a pixelated thumbnail on a Shopify “Collections” page might benefit from a sharper replacement, potentially boosting clicks.

Visual search relies on matching objects regardless of orientation, color, or positioning, but results often prioritize images most similar to the user’s uploaded photo. To maximize reach, businesses should diversify their product images by including multiple angles, close-ups, and variations in color. A search for a plush toy’s front view, for instance, may rank better if additional side or detail shots are available.

Performance data from the multimodal filter can inform strategic adjustments. By exporting this data into a generative AI tool, businesses can identify trends, such as which colors or textures correlate with higher rankings. For example, product pages featuring certain materials might consistently outperform others, suggesting that merchants add similar visuals to their listings to improve visibility.

Continuous testing is necessary to refine visual search strategies. Monitoring changes in the multimodal reports will reveal which modifications, whether higher resolution, additional angles, or color variations, drive the most views and clicks. Implementing these adjustments systematically and tracking results over time ensures sustained improvements in visual search performance.