Consumer Behaviour
Consumer behaviour signal
Observable patterns in how users phrase queries, refine searches, and choose answers, used by both ranking systems and generative models to infer intent and quality.
What it is
Consumer behaviour signals are observable patterns in how people phrase queries, rephrase them after seeing results, dwell on or abandon answers, and choose which source to act on. Ranking systems and generative models treat these patterns as evidence of intent and answer quality.
Why it matters
In AI search the model has no fixed ranking to lean on, so aggregated behaviour becomes a primary proxy for whether an answer satisfied the user, shaping which content gets surfaced and cited next time.
How it works
Signals are captured across the session: the sequence of queries, follow-up prompts, clicks, copy actions, time before refinement, and whether the user stops searching, then aggregated to infer satisfaction at scale.
When it applies
It applies wherever a system observes interaction data, from a search results page to a multi-turn chat with a generative assistant.
Examples
- A user rephrases broad and narrow versions of the same question, revealing the system underdelivered on the first attempt.
- A searcher copies a snippet from one answer and stops searching, signalling that answer resolved the task.
- Rapid back-and-forth between two results before settling suggests low confidence in either source.
How it is measured
- Query reformulation rate within a session
- Answer abandonment or follow-up rate
- Dwell time and copy or save actions on a result
- Task completion or search exit after a given answer
Related terms in Consumer Behaviour
- Agentic browserA browser (or browsing layer) that uses an LLM agent to interpret pages, summarise content, and take actions on behalf of the user. Arc Search, Perplexity Comet, Browser Company's Dia, Dia browser, and similar.
- Brand demandSearch volume for branded terms. In an AI-search world, brand demand is the single strongest moat, generic queries are absorbed by AI Overviews and ChatGPT, while branded queries route users directly to brand properties.
- Job-to-be-done (search)The functional outcome a searcher is trying to achieve when they issue a query. Distinct from the literal query text. The unit of analysis for intent-aligned content strategy.
- Prompt-shaping behaviourHow users refine the language of their prompts mid-conversation to get a better answer. Reveals a shift from keyword behaviour (typing fewer terms) to conversation behaviour (typing more, more naturally).