Consumer Behaviour
Job-to-be-done (search)
Also known as: JTBD
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.
What it is
The job-to-be-done in search is the functional outcome a person wants to accomplish when they issue a query, which often differs from the literal words they type. It is the unit of analysis for intent-aligned content strategy rather than the keyword itself.
Why it matters
AI systems increasingly answer the underlying job directly, so content built around the outcome rather than the phrasing is more likely to be retrieved, synthesised, and cited across many query variations.
How it works
Strategists infer the job by clustering queries that share an outcome, reading the follow-up questions users ask, and mapping content to the decision or task the searcher is trying to complete.
When it applies
It applies during content planning and intent research, before deciding what to publish or how to structure an answer.
Examples
- A query for best CRM software maps to the job choose a tool my small team can adopt quickly.
- A how to fix a leaking tap search maps to the job stop the leak myself today without a plumber.
- Several differently worded queries about pricing all serve one job decide whether this fits my budget.
How it is measured
- Share of query variants mapped to a single defined outcome
- Coverage of follow-up questions answered within one piece of content
- Citation rate across the cluster of queries serving the job
- Conversion or task completion among visitors arriving on intent
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.
- Consumer behaviour signalObservable patterns in how users phrase queries, refine searches, and choose answers, used by both ranking systems and generative models to infer intent and quality.
- 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).