Consumer Behaviour
Brand demand
Search 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.
What it is
Brand demand is search and prompt volume for terms that name a specific company, product, or person. It reflects deliberate intent to reach a known entity rather than to explore a generic topic.
Why it matters
As generic queries are absorbed by AI Overviews and chat assistants, branded queries remain the strongest moat because they route users directly to brand properties instead of being answered away by a synthesised summary.
How it works
Brand demand is observed through branded query volume, the rate at which people add a brand name to generic searches, and how often a brand is asked for by name inside generative assistants.
When it applies
It applies whenever measuring discovery durability and the resilience of traffic against zero-click answers.
Examples
- A user types the brand name plus reviews rather than the generic product category.
- Someone asks an assistant for that specific tool by name instead of asking for the best option.
- Branded navigational queries climb after a campaign while generic category clicks decline.
How it is measured
- Branded search and prompt volume over time
- Ratio of branded to non branded query volume
- Share of branded navigational queries reaching owned properties
- Frequency of the brand being named directly inside assistant conversations
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.
- 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.
- 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).