Multimodal Search

If your SEO strategy assumes every searcher types a text query, you are already optimising for a shrinking share of search behaviour. Multimodal search is not emerging — it is already happening at 20 billion Lens searches per month.

What is multimodal search?

Multimodal search describes any search interaction that uses more than one type of input or output. The most common forms in 2026:

• Text + Image (Visual Search): A user uploads or photographs an object and asks questions about it. Google Lens is the dominant platform. Pinterest Lens processes 3+ billion monthly visual searches.
• Voice + Visual: Speaking a query while a camera captures visual context. Google Assistant with Lens, Siri with Visual Lookup.
• Video Search: Searching within video content or using a video clip as the search query input. Used on YouTube and emerging in Google video carousels.
• AI Overview synthesis: Google's AI Overviews combine text, images, and video from multiple sources to answer complex queries — this is multimodal output from a multimodal retrieval system.

Google's Multitask Unified Model (MUM), introduced in 2021, underpins multimodal search. MUM processes text, images, and video across 75 languages simultaneously — allowing queries like photographing hiking boots and asking "are these suitable for Mt. Fuji in October?" to return contextually rich answers.

Why multimodal search matters for SEO

Three forces make multimodal search an SEO priority, not just a trend to monitor.

1. Demographic shift. 62% of Gen Z prefers visual search over text (ViSenze, 2024). As this cohort becomes the dominant online purchasing demographic, businesses without visual search optimization lose relevance systematically.
2. New ranking signals. Image alt text, descriptive filenames, ImageObject schema, VideoObject schema, and Speakable schema all affect multimodal search visibility. Pages with strong multimodal signals appear in Lens results, voice answer boxes, and video carousels — surfaces that text-only pages cannot reach.
3. E-commerce conversion impact. Visual search bridges intent to purchase. A user photographing a product they saw in a magazine is moments from buying. Retailers without optimised product images are invisible at the highest-intent moment in that journey.
4. Local search amplification. Voice searches are 3x more local than text searches. "Near me" and local intent queries increasingly come through voice and visual input, making multimodal optimization intersect directly with local SEO.

How multimodal search works

The underlying mechanics differ by modality but share a common architecture: convert input to embeddings, retrieve matching content, rank and present results.

Visual search (image input)

Google Lens converts the photographed image into a vector embedding. This embedding is compared against indexed images and their associated text to find semantically similar content. The quality of that matching depends on: image clarity, alt text quality, surrounding page text, and schema markup.

Voice search (audio input)

Speech-to-text converts the spoken query into text. That text then enters standard search ranking with additional context: voice searches are typically longer, more conversational, and disproportionately local. Pages that match conversational query patterns and have local SEO signals rank well here.

Cross-modal search (combined inputs)

MUM enables combined inputs: photograph a plant and ask "how do I care for this in low light?" The model processes the visual (plant species identification) and the text (care instructions, lighting requirements) simultaneously, synthesising an answer that neither modality could produce alone.

Types of multimodal search and SEO tactics per type

Real multimodal search optimization examples

1. E-commerce visual search win

An outdoor apparel retailer optimised all product images: descriptive filenames (e.g., "mens-waterproof-trail-jacket-blue-large.webp"), detailed alt text describing material, colour, and use case, ImageObject schema on product pages, and multiple-angle photography. Within 12 weeks, Google Lens-referred sessions increased 34%. Cost: zero additional ad spend.

2. Local business voice search capture

A dental practice rewrote their service pages in conversational question-and-answer format, added FAQPage schema, and optimised their Google Business Profile for "dentist near me" queries. Voice search impressions (measured via Search Console) increased 41% over 6 months, with a measurable uptick in "how did you find us?" answers citing voice assistant.

3. YouTube channel discoverability via transcripts

A SaaS company added full transcripts to all tutorial videos on YouTube and implemented VideoObject schema on their website's embedded player pages. Search Console began showing video-rich result appearances within 8 weeks of implementation. Organic video views from Google Search (not YouTube internal) increased 28%.

Multimodal search vs traditional text search — what changes

6 best practices for multimodal search optimization

1. Write descriptive image alt text. Not "image.jpg" or "photo of product" — include material, colour, use case, and context. "Navy waterproof trail running jacket for men, shown from front against mountain background" gives Lens and Google Images a rich signal.
2. Use descriptive filenames before uploading. Google can read filenames. "blue-running-shoes-mens-size-10.webp" ranks differently from "IMG_3042.jpg".
3. Add ImageObject schema to key pages. Structured data helps Google parse your images accurately and surface them in visual search carousels and AI Overviews.
4. Write in conversational question-and-answer format. Voice queries are longer and more natural than typed queries. Pages structured around natural questions ("What should I wear for a winter trail run?") match voice intent better than keyword-optimised headers.
5. Transcribe and timestamp every video. Full text transcripts make video content indexable. Chapter timestamps (added via YouTube's chapter feature or VideoObject schema) let Google surface specific video segments in search results.
6. Implement Speakable schema for voice answer priority. Speakable markup tells Google Assistant which sections of your page are most appropriate to read aloud in response to voice queries.

Common multimodal search mistakes to avoid

• Generic or missing alt text — the most common gap. Audit your site with Screaming Frog or Ahrefs Site Audit to find images without alt attributes.
• AI-generated images without context — an AI image saved as "generated-image-001.webp" with no alt text contributes nothing to visual search.
• No video transcripts — video content without text transcripts is invisible to search engine crawlers. Every minute of video is an indexing opportunity wasted.
• Ignoring mobile image optimization — visual searches are predominantly mobile. Large, slow-loading images hurt both page speed and visual search performance.
• Not tracking multimodal channels separately — if you don't segment Lens-referred sessions, voice impressions, and video appearances, you can't measure the impact of multimodal optimization and will struggle to justify the work.

Frequently asked questions

Related glossary terms

Sources

Akshay VR

Marketing Head · theStacc · ex-Sr Marketing Specialist, ARKA 360 · Malappuram, Kerala

Akshay leads editorial and content operations at theStacc. He tracks how search behaviour changes across modalities and writes the playbooks that help teams stay visible as users move from typing to tapping, speaking, and photographing.

LinkedIn Author page More terms by Akshay