Howuserbehaviour changed in 2026

Live demo · the same buyer intent expressed two completely different ways depending on which search surface they reach for.

1. The three behavioural shifts that matter

First, query length. The average Google query is around 3-4 words. The average ChatGPT or Claude query is closer to 18-25 words and almost always a full sentence with context. Second, the click is no longer the goal - the answer is. Users read the synthesised response and act on it without ever clicking the source. Third, the model surfaces tradeoffs and recommendations that classic SERPs never did, which means the conversation often ends in a decision instead of a comparison shopping spiral.

All three shifts feed back into how you should think about keyword research and content production. We unpack the new keyword model in the long-tail keywords for AI search guide.

2. The new query shape

Question-shaped prompts are rich in context: industry, team size, prior tools tried, budget, constraints, timeframes. The user is essentially briefing the model the way they'd brief a consultant. That's a gift to brands that publish consultant-grade content - long-form, claim-by-claim, opinionated, structured. Pages written for the old click-bait SERP lose because they don't match how the model is actually being asked the question.

The format that wins these question-shaped retrievals is documented in our write content AI assistants cite guide - and the structural schema patterns are in the schema markup for AI search guide.

3. The zero-click reality

In classic search, your job was to win the click. In AI search, your job is often to be the brand the model recommends inside the answer - even if no click happens. That sounds bad until you realise the recommendation itself is worth more than the click, because the user has already done their research, asked for a shortlist, and the model handed them three options with reasons. Being one of those three is the new top-of-funnel.

The compression is real though - Google AI Overviews have measurably reduced organic CTR for the queries they appear on, and the data is documented in our how AI Overviews changed CTR guide. Brands that adapt to recommendation-as-traffic compound; brands stuck on raw click counts decline.

4. Rewriting the funnel

The classic awareness → consideration → decision funnel still applies, but the touchpoints move. Awareness is now "the model knows your brand exists when asked about your category." Consideration is "the model includes you in any recommendation set for your category." Decision is "the model recommends you specifically when the user describes a constraint that fits you." Each stage maps to different GEO levers - entity strength for awareness, citation graph for consideration, structural clarity and specific positioning for decision.

The full integration of GEO with classic SEO disciplines is in our GEO vs SEO guide, and the per-platform variations live in our rank in ChatGPT, Claude, Perplexity and AI Overviews playbooks.

5. Measuring AI search behaviour

The old funnel metrics - impressions, clicks, CTR - still matter for the parts of the journey that touch classic search. But you also need a parallel measurement layer for AI search visibility: which prompts trigger your brand, which assistants cite which pages, and how the citation share is moving over time. The full stack lives in our track AI search visibility guide, and the attribution patterns - how to actually tie GEO to pipeline - are in our measure GEO ROI guide.

Recap

AI search is a different distribution layer, not a faster version of Google. Queries are longer, richer in context and end in decisions instead of clicks. The funnel still exists - it just runs through different surfaces, with different metrics and different content shapes winning at each stage. Brands that map their actual buyers' question-shaped prompts, build entity strength and structural clarity around those prompts, and measure citation share alongside classic SEO win the new layer.

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