Google Search Console Multimodal Search: How to Analyze Lens & Visual Search Traffic

By Rahul Saini, Founder / SEO Strategist at Search Counsel Co. Published September 2026. Material review: September 27, 2026.

What changed in Google Search Console multimodal reporting?

On September 24, 2026, Google announced a new Web: multimodal search type in Search Console. Google says the reporting includes web searches where an image was used as part of the search, including Google Lens, Circle to Search on Android, image uploads to Google Search, and Chrome’s right-click “Search this image” feature. The filter appears in the Search results Performance report and in Google’s Generative AI performance reporting.

This is a reporting change, not an announcement of a new ranking system. That distinction matters. Google is giving site owners a new way to isolate image-led discovery, but the announcement does not say that websites need a new “multimodal SEO” formula or that one specific markup field, alt-text pattern, or image tactic controls visibility.

Web: text-based, Web: multimodal, and Image search are different concepts

The easiest mistake is to assume that “multimodal” is simply a new name for Google Images traffic. It is not. The two filters answer different questions. Web: multimodal describes the input behavior: an image was used as part of the search. Image search describes a search result surface.

This distinction matters when you build reports. A page can perform well in image-led web discovery without that being the same thing as ranking strongly in Google Images. Do not combine the datasets as if they are interchangeable.

Why the Queries dimension disappears

The new report changes the normal Search Console workflow because the Queries dimension is not available for multimodal traffic. Search Engine Journal, citing Google’s updated help documentation, notes that Google withholds specific text-query data because multimodal searches mostly use images rather than text. That does not mean every multimodal search contains zero words. It means Search Console does not provide the normal query string for this traffic.

If you are used to starting every diagnosis with a query list, this is a major change. SearchCounselCo’s guide to missing Search Console keyword data is useful context here: incomplete query visibility is not automatically a tracking failure. With multimodal reporting, the absence of the Queries dimension is expected behavior.

How to read multimodal Search Console data without making the wrong conclusion

The safest way to use the report is to separate what Google documents, what publishers have observed, and what remains unknown. That prevents a new dashboard from becoming a source of false causation.

The reporting migration problem: old Web totals are not a clean benchmark

The most important measurement issue is that multimodal data is newly exposed. Search Engine Roundtable documented a follow-up in which John Mueller said, after checking with Google’s team, that the data “wasn’t previously in the counts”. That means a reporting dashboard can show more impressions after the feature appears even when the underlying visibility of your existing text-search pages did not improve.

A safer migration rule is simple:

• For text-search trends: compare Web: text-based with Web: text-based.
• For multimodal trends: create a new multimodal baseline and measure forward.
• For old all-Web comparisons: add a note explaining the reporting change before interpreting year-over-year or period-over-period movement.

This is especially important for client reports, executive dashboards, and automated KPI summaries. If the definition of reported Search visibility changes, the comparison needs a methodology note.

Do not confuse the new report with the September 2026 spam update

There is another timing complication. Google’s September 2026 spam update began on September 24, the same date Google announced multimodal Search Console reporting. Google says the ranking update applies globally and may take up to two weeks to complete.

If impressions, clicks, position, and page visibility move during the same period, you have at least two separate variables: a new reporting dataset and an active ranking update. Do not attribute every September 24 change to multimodal search. If you are diagnosing a broader traffic change, use the same discipline described in SearchCounselCo’s Search Console AI-powered configuration guide: isolate the affected pages, metrics, devices, countries, and time periods before deciding what changed.

Average position is useful, but easy to overread

Google defines average position as the average position of the topmost result from your site or page for recorded impressions. A multimodal average position of 2.3 therefore does not prove that one image consistently holds position #2 in Google Lens. Search surfaces can contain complex result elements, and low-volume datasets can swing quickly.

Use average position as a trend signal, not as a literal screenshot of where one image ranks. Google’s own Search Console documentation recommends paying close attention to changes in impressions and clicks rather than relying on position alone.

A page-first multimodal SEO audit: the 30-minute workflow

Because the query dimension is missing, the unit of analysis changes from keyword first to page first. The goal is not to guess what image someone submitted. The goal is to learn which URLs Google surfaces after image-led searches, whether those visits are meaningful, and which patterns are worth testing.

Step 1: Export a clean baseline before changing anything

Open Search Console’s Search results Performance report, select Web: multimodal, choose a stable date range, and export the data. Google explicitly points users to the Export function in its announcement. Save the file with the property, date range, and export date in the filename.

Do not immediately rewrite alt text or replace images. A baseline is more valuable than a rushed optimization because the report is new and the data can be sparse.

Step 2: Start with Pages, not position

Sort URLs by multimodal impressions and clicks. Then classify the pages into meaningful groups such as product pages, category pages, tutorials, blog posts, service pages, location pages, or media-heavy resources. This tells you where visual discovery is happening, which is more useful than staring at a sitewide total.

If Search Console page-level reporting is unfamiliar, start with the broader reporting logic in SearchCounselCo’s branded vs non-branded Search Console guide: isolate one dimension at a time, verify the filter, then compare the right cohort instead of making a conclusion from the main graph.

Step 3: Compare the same URL against Web: text-based

For a promising URL, keep the date, country, and device filters consistent and switch from Web: multimodal to Web: text-based. You are looking for a relationship, not a winner. Some pages may be strong in both modes. Others may have almost no text discovery but disproportionate multimodal visibility.

That comparison helps answer a better question: Is this page useful because of the topic, because of the visual object it contains, or because both work together?

Step 4: Segment by device and country before forming a theory

Multimodal behavior is highly tied to interfaces such as mobile cameras and Circle to Search, so device splits can be meaningful. Country differences can also expose product availability, language, visual demand, or market-specific search behavior. But treat those as hypotheses until the sample is large enough to support them.

Step 5: Connect visibility to on-site outcomes

Search Console tells you what happened before the click. It does not tell you whether those visitors purchased, submitted a form, read deeply, or immediately left. If multimodal pages begin receiving meaningful clicks, compare those landing pages in analytics. SearchCounselCo’s GA4 and AI visibility tracking guide covers the broader principle: separate visibility from downstream visits and outcomes rather than treating an impression as business value.

The SearchCounselCo multimodal page opportunity matrix

The following framework is not a Google metric. It is a prioritization model for deciding where to investigate first.

The SearchCounselCo Multimodal Evidence System

The strongest use of the new report is not “optimize every image.” It is a controlled evidence process that separates a reporting change from a visibility change and a visibility change from business value.

Use a reporting reconciliation worksheet before you call anything “growth”

For a clean trend, compare Web: text-based against Web: text-based and build a separate Web: multimodal baseline. If you want one directional planning metric, SearchCounselCo recommends tracking multimodal visibility share as an internal analytical ratio:

Evidence confidence ledger

Compact 30-minute execution path

1. 0–5 minutes: export a stable multimodal baseline.
2. 5–10: sort Pages by impressions/clicks and classify page type.
3. 10–15: compare the same URLs under Web: text-based.
4. 15–20: split by device/country and remove weak-sample conclusions.
5. 20–25: inspect page/image/product-data quality only on high-priority URLs.
6. 25–30: write one hypothesis, one change and one success metric per page.

Worked example: how to diagnose one page without overclaiming

This example is hypothetical. It demonstrates the analysis method and should not be read as SearchCounselCo client data.

A weak conclusion would be: “multimodal CTR is half the text CTR, so the product image is poor.” The evidence does not support that jump. The better process is to confirm the sample size, compare device mix, check whether the landing page satisfies a visual-shopping task, review image quality and product data, then inspect downstream engagement/conversions. Only after that should you define a test.

A useful hypothesis could be: “This product already earns meaningful image-led impressions, but the mobile landing experience and product-context clarity may be limiting clicks or downstream value.” The test should then change one factor at a time and use the next like-for-like multimodal period as the comparison.

What should you optimize for multimodal search?

Start with established Google image and page-quality guidance, then test from your own baseline. Google’s image SEO documentation recommends high-quality images, relevant surrounding text, descriptive and useful alt text, sensible filenames, and making images accessible to Google. Those are documented image-search practices. They are not a promise that one field will increase the Web: multimodal metric.

1. Make the visual object useful and unambiguous

If the page contains products, parts, landmarks, diagrams, ingredients, apparel, furniture, tools, plants, or other visually identifiable entities, use images that clearly show the object. Avoid forcing critical visual information into tiny thumbnails, decorative collages, or images that are unrelated to the page’s core task.

2. Put descriptive context around the image

Google’s image guidance says page content helps it understand images. Use headings, captions where they genuinely help, product names, specifications, explanatory copy, and nearby text that makes the image’s subject and purpose clear. This is not about keyword stuffing an alt attribute. It is about making the page understandable as a whole.

3. Make sure the important image and page can actually be crawled and indexed

A visually excellent asset cannot support search visibility if Google cannot reliably access the page or image. Check robots rules, indexability, canonical signals, lazy-loading implementation, image URLs, rendering, and template-level blockers. If your audit exposes those issues, SearchCounselCo’s technical SEO service is the relevant path because the problem is access and processing, not copywriting.

4. Ecommerce sites should connect visual assets to product data

For ecommerce, Google gives unusually concrete guidance. Its ecommerce documentation says that if you want products to be found in Google Lens, make sure product details are uploaded to Google Merchant Center, opt in to product listings, and follow Google Image best practices. Google also says Product structured data can make product information eligible for richer appearances in Google Search, including Google Images and Google Lens.

The practical ecommerce stack is therefore: high-quality product imagery + accurate landing-page content + crawlable/indexable resources + Merchant Center product data + valid Product structured data where applicable. Do not reduce the strategy to “add schema.” Structured data can support eligibility and understanding; Google does not say it guarantees higher multimodal rankings.

5. Improve the landing page, not only the image

A multimodal impression is still attached to a destination. If the page has weak product details, vague headings, poor mobile UX, thin content, confusing variants, or an intent mismatch, better photography alone may not solve the business problem. Use the new report to identify pages worth inspecting, then improve the whole experience.

Who should care most about multimodal search?

Bad conclusions the new report can tempt you to make

API support, Generative AI reporting, historical data, and other limits

Can you pull Web: multimodal through the Search Console API?

As of September 27, 2026, Google’s documented Search Analytics API lists discover, googleNews, news, image, video, and web as supported type values. It does not document a separate multimodal type in the Search Analytics query reference. Google’s multimodal launch announcement instead tells users to use Search Console’s Export function for external analysis.

If you run Looker Studio, a warehouse, an agency reporting stack, or a custom Search Console pipeline, do not silently treat ordinary web API data as if it reproduces the new text-based vs multimodal UI split. Document the limitation until Google publishes API support or clarifies how the split should be retrieved programmatically.

Testing boundary: SearchCounselCo did not independently test undocumented API enum values for this version, so this page does not claim that every possible unofficial value fails. The claim here is narrower and verifiable: a separate multimodal type is not present in Google’s public API documentation as of the review date.

Can creators track Instagram, TikTok, X and YouTube with Search Console?

Yes, where the feature is available. Google now supports platform properties for Instagram, TikTok, X and YouTube, allowing creators and publishers to monitor how their platform accounts or channels perform on Google Search. Google notes that platform properties are still rolling out gradually, so the option may not yet appear for every account.

This creates a useful new analysis path for visual discovery: creators should not assume multimodal search is only a website/ecommerce issue. If you manage a supported platform property, check whether the same multimodal reporting becomes available there and keep the platform dataset separate from your website property when reporting results.

Search results report vs Generative AI performance report

The same phrase “Web: multimodal” can appear in two different reporting contexts, but the metrics are not identical. Google’s Generative AI performance report focuses on impressions from generative AI features and supports page, country, and device analysis. The normal Search results Performance report includes clicks, impressions, CTR, and average position.

If you are already comparing classic Search with AI-driven discovery, SearchCounselCo’s Google AI Mode SEO guide provides broader context. Keep the datasets separate: multimodal describes search input behavior, while Generative AI reporting describes visibility in Google’s AI search features.

How far back does multimodal Search Console data go?

Google announced the global rollout on September 24, but it has not published one universal historical start date that every site should expect. If your property displays earlier rows, record the earliest stable date visible in that property rather than assuming every account has identical backfill.

For reporting purposes, the safest wording is: “Multimodal reporting became publicly announced on September 24, 2026; the historical range available in this property may differ.”

What if the multimodal filter is not showing?

Google says sites will start seeing metrics if they receive traffic from qualifying multimodal searches as the integration rolls out. A missing or empty view does not prove an image-indexing problem. Check the selected property, date range, report type, and whether the site has enough relevant visual-search activity before opening a technical investigation.

If broader Search Console filters or page data also look inconsistent, first verify that the problem is not a reporting/filter issue. SearchCounselCo’s SEO consulting work is designed around this kind of diagnosis when the business question spans Search Console data, content, technical SEO, and prioritization rather than one isolated fix.

What Google has confirmed vs what SEO teams still need to test

FAQ

The one thing to do next

Open Search Console, export your first stable Web: multimodal Pages report, and save it as a baseline before making any SEO changes. Then compare only the top multimodal URLs against the same URLs under Web: text-based. If the pattern is large enough to matter and you still cannot tell whether the issue is reporting, page quality, or technical access, use a focused Search Console-led SEO diagnosis rather than changing every image on the site.