Google Search Console Adds Multimodal Filter to Boost Visibility

What Happened

Google rolled out a new multimodal filter in Search Console’s Performance reports. The filter aggregates impressions, clicks and rankings that originate from visual search methods—Google Lens, Circle to Search on Android, image‑based queries, and Chrome’s right‑click “Search image” option. This addition aligns with Google’s broader effort to make AI‑driven search behavior more visible to site owners.

Key Details

• Filter availability – The option shows up in the Performance tab under “Search type.” Users can switch among Web, Image, and the new Multimodal view.
• Data scope – The report draws from the most recent 90 days and refreshes every 24 hours, just like other performance metrics.
• Granular breakdown – Inside the Multimodal view you can drill into each channel—Lens, Circle to Search, image uploads, and Chrome right‑click—to see its share of impressions and clicks.
• Integration with Core Web Vitals – The filter automatically pairs visual traffic data with your site’s Core Web Vitals, letting you spot any correlation between image‑driven visits and page‑speed or layout‑stability metrics.
• Export options – CSV and Google Sheets exports now carry the multimodal data, so you can analyze it in your favorite tools.

What It Means For Your Business

For small and local businesses that depend on visual discovery—restaurants showcasing dishes, interior designers displaying projects, or retailers highlighting products—the filter reveals exactly how many customers arrive via images. This insight enables you to:

• Optimize visual content – Pinpoint the images that generate the most clicks, then fine‑tune your photography, alt‑text, and image‑SEO strategies.
• Target AI‑search audiences – Because AI assistants frequently surface image‑based answers, knowing your visual traffic shows how these tools are referencing your brand.
• Allocate marketing budget – Direct spend toward the channels that deliver the highest ROI, be it paid image ads, social‑media photo posts, or local directory listings.
• Improve citations – With clarity on which modalities drive visits, you can prioritize updating citations on platforms that appear in visual search—Google Maps, Yelp, TripAdvisor, and similar services.

Actionable Steps

1. Enable the multimodal filter – Open Search Console, go to Performance → Search type, and select Multimodal.

2. Review your top images – Switch to the “Image” tab and identify the photos that earn the most impressions and clicks.

3. Audit alt text & schema – Verify that every image includes descriptive alt text and, when relevant, appropriate image‑schema markup.

4. Update local listings – Upload high‑quality photos to your Google Business Profile and any other local directory where you appear.

5. Track changes – Create a monthly report that pits visual‑traffic trends against overall site performance.

Future Outlook

Google’s multimodal reporting marks a move toward a more holistic view of how users discover content. As AI assistants keep surfacing visual answers, businesses that adjust now will be better positioned to appear in those results. Watch for future updates that could introduce additional modalities, such as video or augmented‑reality searches.

Why It Matters

For businesses that want to be discovered through AI‑driven search, understanding which visual queries drive traffic is invaluable. AI assistants frequently surface image‑based answers, and without data from Lens or image uploads you’re left guessing which assets perform best. The multimodal filter supplies concrete numbers, enabling you to prioritize image SEO, alt‑text, and schema markup that these tools rely on.

Local citations that appear in visual search—such as Google Maps photos or Yelp listings—also shape how AI assistants reference your brand. By monitoring visual traffic, you can fine‑tune those citations, making sure they’re complete, accurate, and image‑rich, which improves your odds of showing up in AI‑generated answer boxes. In short, the filter turns blind spots into actionable insights, keeping you ahead of AI‑centric discovery.