Voice assistants, camera-based search, and multimodal AI are changing how shoppers discover products, and SEO apps are racing to optimize every spoken and visual query.

From Typed Queries to Conversations and Images

Typing “red running shoes” into a search bar is already giving way to more natural behavior. Consumers tap a microphone icon, say “show me breathable red running shoes under one hundred dollars that work on gravel,” or snap a photo of a favorite pair and ask for similar options. Voice and visual search usage continues to grow, with recent reports suggesting that more than one in five internet users now rely on voice search globally and that billions of voice assistants are in active use. Increv+4Backlinko+4Synup

In this environment, e-commerce marketers need SEO apps that understand intent beyond text. Voice and visual search optimization is no longer an experimental add-on; it is a core pillar of how products are found.

Multimodal SEO Apps: What They Actually Do

Multimodal SEO apps ingest product catalogs, media assets, and user queries across channels, then analyze them using models capable of processing text, images, and in some cases video.

The first job of these apps is to ensure that every product has rich, accurate metadata. Titles, descriptions, alt text, captions, and structured attributes like color, material, size, and use case must be aligned. AI models can now suggest improvements, detect missing attributes, and identify discrepancies between photos and descriptions.

The second job is query modeling. These apps cluster voice transcripts, text searches, and visual search intents to reveal how people actually express their needs. That insight helps marketing teams shape content, FAQ structures, and on-site search configurations.

Finally, the apps simulate how voice assistants and multimodal search platforms interpret a store’s content. They test prompts like “find me a cruelty-free moisturizer that works in humid climates” and reveal whether the brand appears among the top recommendations, then recommend content and data changes to improve that visibility.

Natural Language, Long-Tail Intent, and AEO

Voice search tends to be longer, more conversational, and more question-based than typed search. Industry research highlights that a large share of voice searches are phrased as full questions, often including qualifiers like “best,” “easy,” or “near me.” Increv+3Synup+3HubSpot

For ecommerce SEO, this plays directly into Answer Engine Optimization. Multimodal SEO apps help teams create answer-focused content aligned with long-tail, high-intent questions. They surface recurring patterns, such as “what should I wear to…” or “how do I maintain…,” and map them to guides, product bundles, and rich snippets.

Rather than manually writing thousands of Q&A pairs, marketers use generative AI to draft them at scale, then use the app’s testing features to see which answers are most likely to be selected by voice assistants and AI overview features.

Image Quality as a Ranking Signal

Visual search hinges on clear, high-quality images. While search engines do not explicitly state that image quality alone determines rankings, it heavily influences user behavior and the ability of AI to recognize products.

Modern SEO apps run automated checks on image resolution, background clarity, and consistency. They recommend retouching or reshooting images that fail to meet thresholds, and they help teams generate additional visual assets, such as lifestyle imagery and multiple angles, to support AR try-ons and 3D viewers.

As AR and VR shopping environments grow, these assets become even more critical. Spatial computing experiences require detailed, accurate 3D representations of products, and SEO apps increasingly monitor whether these assets are correctly tagged and discoverable. SaaS SEO Agency

Technical Foundations for Voice and Visual SEO

To succeed in voice and visual search, the technical underpinnings of a site must be solid. Multimodal SEO apps continuously scan for issues such as missing alt attributes, duplicate image filenames, inaccessible navigation patterns, and slow media delivery.

They also manage schema for products, reviews, how-to content, and local information. In voice-driven scenarios, structured data is often the bridge that allows assistants to retrieve store hours, return policies, and product availability without sending users to a website.

On the analytics side, the apps aggregate data from search consoles, voice search logs, and assistant integrations to show how multimodal traffic behaves differently from traditional search traffic. That insight informs future content and UX decisions.

Closing Thoughts and Looking Forward

As more shopping journeys start with a spoken question or a camera tap, voice and visual search optimization will separate ecommerce leaders from laggards. SEO apps that understand multimodal signals and provide clear guidance on content, data, and media will be essential tools for marketing teams.

Over the next few years, expect these apps to integrate with emerging multimodal assistants that can see, listen, and reason about context in real time. Brands that invest early in structured data, high-quality media, and answer-focused content will be best positioned to appear in these new discovery layers, even when there is no traditional search results page in sight.

References:
Backlinko, “31 Fascinating Voice Search Statistics (2024),” Backlinko, https://backlinko.com/voice-search-stats
Synup, “80+ Industry Specific Voice Search Statistics for 2025,” Synup, https://www.synup.com/en/voice-search-statistics
DemandSage, “51 Voice Search Statistics 2025: New Global Trends,” DemandSage, https://www.demandsage.com/voice-search-statistics/
Digital Silk, “Top 35 Voice Search Statistics You Shouldn’t Miss in 2025,” Digital Silk, https://www.digitalsilk.com/digital-trends/voice-search-statistics/
Increv Academy, “45 Fascinating Voice Search Statistics (2024),” Increv, https://increv.co/academy/voice-search-stats/

Author and Co-Editor: Claire Gauthier, – eCommerce Technologies, Montreal, Quebec;
Peter Jonathan Wilcheck, Co-Editor, Miami, Florida.

#VoiceSearchSEO #VisualSearch #MultimodalAI #EcommerceDiscovery #ARCommerce #ImageOptimization #AnswerEngineSEO #ConversationalSearch #ProductMetadata #DigitalShelf