Voice search is the act of submitting a query to a search engine or AI assistant by speaking rather than typing. It encompasses queries to smart speakers (Amazon Alexa, Google Home), smartphone assistants (Siri, Google Assistant), in-car systems, and voice-enabled AI products.
Voice search and AI engine optimization
Voice queries are the most extreme form of conversational search — they’re typically longer, more natural in phrasing, and often local or action-oriented. Examples:
- “Hey Siri, what’s a good CRM for a small business”
- “Alexa, find me a project management tool that integrates with Slack”
- “OK Google, what are the best noise-canceling headphones under $300”
How voice answers are generated
Modern voice assistants draw on multiple data sources to generate spoken answers:
- Google Assistant / Siri: Pull heavily from featured snippets and Knowledge Graph data
- Alexa: Mix of Bing search results and Alexa Skills content
- AI-powered voice (emerging): ChatGPT, Gemini, Claude voice modes use LLM + RAG
The shift toward LLM-powered voice means the same signals that improve text-based AI visibility also improve voice visibility.
Optimizing for voice queries
Conversational content: Write naturally-phrased questions and direct answers. “How much does [Product] cost for small businesses?” performs better than “Product pricing SMB.”
Local schema markup: Voice searches are frequently local intent (“near me,” “open now”). LocalBusiness schema with accurate hours, location, and category data feeds voice assistants.
Featured snippet targeting: Voice assistants typically read the featured snippet. Content that wins featured snippets is often what gets read aloud as the voice answer.
Direct, spoken-friendly answers: A voice answer needs to make sense when read aloud — no tables, no lists with heavy formatting. The first 40–50 words of the answer need to stand alone as a complete response.