What is Multimodal Search?
Learn what Multimodal Search means, why it matters for search rankings, and how consistent content publishing keeps your business visible in Google.
Definition
Multimodal search is the ability to search using multiple input types simultaneously — such as text, images, voice, and video — to find information, rather than relying on text queries alone.
What Is Multimodal Search?
Multimodal search allows users to search using multiple types of input at the same time. Instead of typing text into a search box, users can combine text, images, voice, video, and even gestures to express what they are looking for.
Example: A user takes a photo of a plant, uploads it to Google Lens, and asks verbally: “How do I care for this plant?” The search system processes both the visual input (the plant image) and the text/voice query to provide a specific care guide.
Google’s Multitask Unified Model (MUM), announced in 2021, is the underlying technology powering multimodal search capabilities. MUM can understand information across text, images, and video in 75 languages simultaneously.
Types of Multimodal Search
Text + Image Search
Users upload an image and add text context to refine results.
Use cases:
- “Find me a shirt like this but in blue” (upload image + text modifier)
- “What is this and how much does it cost?” (upload photo + question)
- “Show me recipes using these ingredients” (upload photo of ingredients)
Platforms: Google Lens, Pinterest Lens, Bing Visual Search, Amazon Lens
Voice + Visual Search
Users speak a query while the device camera captures visual context.
Use cases:
- “What is the name of this building?” (point camera + voice)
- “How do I fix this?” (show broken appliance + voice)
- “Where can I buy this?” (show product + voice)
Platforms: Google Assistant with Lens, Siri with Visual Lookup
Video Search
Users search within video content or use video as a query input.
Use cases:
- “Find the moment in this video where [event happens]”
- “Show me tutorials for [technique shown in uploaded video]”
- “What song is playing in this clip?”
Platforms: YouTube search, Google video search, TikTok search
Cross-Language Multimodal Search
MUM enables searches that combine content in different languages and formats.
Example: A user in Japan uploads a photo of a hiking trail and asks in Japanese: “Is this trail difficult?” MUM can find English-language hiking guides about that trail, understand the difficulty descriptions, and present the answer in Japanese.
Why Multimodal Search Matters for SEO
Search Behavior Is Changing
Younger users increasingly prefer visual and voice search over typing. TikTok is now a search engine for Gen Z. Instagram and Pinterest are product discovery platforms. Optimizing only for text-based Google search misses these audiences.
Statistics:
- Google Lens is used for 20 billion searches per month (Google, 2025)
- 62% of Gen Z prefers visual search over text search (ViSenze)
- 27% of the global online population uses voice search on mobile (Google)
- Pinterest Lens processes over 3 billion visual searches monthly
New Ranking Factors Emerge
Multimodal search introduces optimization requirements beyond traditional text SEO:
| Input Type | Optimization Requirements |
|---|---|
| Image search | Alt text, image file names, image schema, high-quality visuals |
| Voice search | Conversational content, featured snippets, FAQ schema, local SEO |
| Video search | Video transcripts, thumbnails, timestamps, video schema |
| Visual search | Product images on white backgrounds, structured data, image SEO |
E-Commerce Impact
Multimodal search is transforming product discovery:
- Users photograph products they see in real life and find where to buy them
- Shoppers upload inspiration photos and find similar items
- Visual search reduces the gap between “I like that” and “I bought it”
Retailers without optimized product images and visual search capabilities lose customers to competitors who have them.
How to Optimize for Multimodal Search
Image Optimization
1. Descriptive file names
Rename images from IMG_1234.jpg to blue-running-shoes-mens.jpg
2. Detailed alt text
Describe what is in the image for screen readers and search engines:
alt="Nike Air Zoom Pegasus 39 men's running shoes in blue and white"
3. Structured data for images Use ImageObject schema with name, description, and contentUrl properties.
4. High-quality visuals Use clear, well-lit images that show products from multiple angles.
5. Image sitemaps Submit image sitemaps to Google Search Console for faster discovery.
Video Optimization
1. Video transcripts Add full transcripts to video pages. Search engines cannot watch videos but can read transcripts.
2. Timestamp chapters Break videos into chapters with descriptive titles. These appear in search results and improve user experience.
3. Video schema markup Use VideoObject schema with name, description, thumbnailUrl, and uploadDate.
4. Compelling thumbnails Thumbnails are the “title tags” of video search. Design clear, readable thumbnails.
5. Host videos strategically YouTube for discovery and reach. Self-hosted (Wistia, Vimeo) for conversion-focused content.
Voice Search Optimization
1. Conversational content Write in natural language that matches how people speak, not just how they type.
2. Featured snippet targeting Voice assistants often read featured snippets as answers. Structure content to win snippets:
- Direct answers in the first paragraph
- Numbered lists for steps
- Tables for comparisons
- FAQ sections with concise answers
3. Local SEO Voice searches are 3x more likely to be local than text searches. Optimize Google Business Profile and local citations.
4. Page speed Voice search users expect immediate answers. Fast-loading pages perform better.
5. Schema markup Use FAQPage, HowTo, and Speakable schema to help voice assistants extract answers.
The Future of Multimodal Search
Near-term (2026-2027):
- Google expands Lens integration across more search surfaces
- Shopping becomes increasingly visual and AI-assisted
- Video search improves with automatic scene detection
Medium-term (2027-2028):
- AR-powered visual search becomes mainstream
- Real-time translation in visual search
- Cross-modal search (search audio with images, text with video)
Long-term:
- Brain-computer interfaces for thought-based search
- Fully immersive search in VR/AR environments
- Predictive search that combines biometric data with multimodal inputs
Related Terms
From understanding Multimodal Search to ranking for it
Understanding Multimodal Search is the starting point. The businesses that actually benefit from it are the ones consistently publishing SEO content. Not just understanding the concept. Most companies know what they should be doing; the bottleneck is execution. theStacc removes that bottleneck by publishing 30 keyword-optimized articles to your site every month, automatically.
See how theStacc worksRelated Terms
Search Generative Experience (SGE) is Google's AI-powered search feature that generates direct answers to queries using large language models, displaying synthesized information at the top of search results.
Voice search uses spoken commands to search the internet via assistants like Siri and Alexa. Learn voice search trends, statistics, and how it impacts.
Build rankings around terms like "Multimodal Search". Automatically
30 keyword-optimized articles published to your site every month. Rankings compound while you focus on your business.
Start Your $1 Trial$1 for 3 days · Cancel anytime