What is conversational search?
Conversational search is a way of searching in which you talk to a system in natural language and it remembers the context of the conversation. You ask a question, get an answer and then ask a follow-up such as "which of those works offline?" without repeating what you asked before.
Classic search works with short, separate queries: you type a few keywords, scan the results and start over with a new query. Conversational search is closer to asking an expert. AI assistants such as ChatGPT and Perplexity work this way, as does Google AI Mode. Voice assistants were an early form of it.
Technically, conversational search relies on language models and semantic search: the system interprets the meaning of the whole conversation rather than matching individual keywords, then looks for sources that fit. It is a core feature of most answer engines.
Why conversational search matters for your brand
People share more context in a conversation than in a search box. Instead of "CRM software", they ask for "a CRM for a 12-person recruitment agency that integrates with Outlook and costs less than 30 euros per user". Answers to such specific questions name only a few brands, so the brands that fit the details win.
Follow-up questions also change the outcome. Each turn narrows the shortlist: your brand can appear in the first answer and disappear after "which one is easiest to set up?" Visibility in conversational search depends on how well your content covers the details and objections that come up later in the conversation, not only the opening question.
How to optimize for conversational search
Collect the full questions customers ask, including the follow-ups you hear in sales and support conversations.
Create content for specific situations: audience, company size, budget, integrations and use cases.
Answer common objections openly, such as setup time, contract terms and limitations.
Write in clear, natural language that matches how people phrase questions out loud.
Track conversational prompts with realistic context, not only short keywords.
With prompt tracking, Trackbase runs questions like these every day across 11 AI models and shows which brands appear in the answers.
Examples of conversational search
A parent asks an AI assistant for a seven-seat family car under 40,000 euros, then follows up: "Which of these has the lowest running costs?"
A marketing manager asks for newsletter software for a Dutch nonprofit, then adds: "It must be GDPR-compliant and support double opt-in."
A traveler asks Google AI Mode for a quiet hotel near Utrecht Central Station, then narrows it down with "with a gym and late check-in".
A B2B buyer compares two payroll providers in a chat and ends with: "Which one offers better support for companies with staff in Germany?"
Frequently asked questions
What is the difference between conversational search and traditional search?
Traditional search treats every query as a new, separate request and returns a list of links. Conversational search keeps the context of earlier questions, so you can refine what you asked in natural language, and it usually answers with a written response. The result is fewer, more specific answers, often naming only a handful of brands.
Is conversational search the same as voice search?
Not exactly. Voice search describes the input method: speaking instead of typing. Conversational search describes the interaction: a dialogue in which the system remembers context. Voice assistants were an early form of conversational search, but much of it now happens in typed chats with AI assistants such as ChatGPT, Perplexity and Google AI Mode.
How do keywords change with conversational search?
Keywords still matter, but queries get longer and carry more context, such as audience, budget and requirements. Instead of optimizing one page per short keyword, cover the specific situations and follow-up questions around a topic. Tracking realistic, full-sentence prompts gives a better picture of your visibility than tracking single keywords.