How to do GEO, in the right order.

Why GEO matters now

AI search query share crossed a threshold in 2026. Standalone AI engines (ChatGPT, Perplexity, Claude, Gemini, Copilot) plus Google AI Overviews and AI Mode now intercept a meaningful slice of buyer research that previously hit traditional Google results. Pew Research clocked a 47% CTR drop on informational queries with AI Overviews present; Seer measured 55% across their client base; MailOnline reported 56%. The traffic is not coming back. Brands either get cited inside the AI answer or get cut out of the consideration set entirely. That is the practical case for running GEO as a discipline, not a side project. The five stages below sit inside the longer GEO field guide if you want the wider context first.

Stage 1: Baseline

Run your brand and three to five named competitors through each major AI engine on the 10-20 buyer queries that matter. Capture what shows up. Use the free AI Search Visibility tool or a paid tracker (Profound, AIClicks, Otterly) for ongoing monitoring. The baseline tells you which engine is the actual gap (often it is not the one you assumed) and which queries are open vs locked. Skipping this stage is the single most common mistake - teams jump to fixes without knowing where the gap actually sits.

Output of this stage: a citation matrix - rows are priority queries, columns are engines (ChatGPT, AI Overviews, Perplexity, Claude minimum), cells show present/absent and the cited domains. This becomes the reference document the rest of the programme works from.

Stage 2: Foundations (the SEO-overlap layer)

Bot access verified for GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot, Google-Extended. Schema chain in place - Article, Person, Organization, FAQPage where the page answers explicit questions. Named author on every content piece with proper credentials. Content depth on priority topics (1,500-2,500 words on the queries that matter). This stage is recognisable as SEO; it is the work that AI engines and Google both reward.

Time cost: 30-90 days depending on existing SEO maturity. If foundations are weak, this is where most of the early budget goes. If foundations are strong, skip to Stage 3 and inherit the wins.

Stage 3: Citation surface (engine-specific)

This is where you target the gap the baseline revealed. Bing index health for ChatGPT and Copilot. Recency hygiene for Perplexity. Knowledge Graph and YouTube transcripts for Gemini. FAQ schema density for AI Overviews. Reddit presence where your audience lives (and on engines that weight Reddit for context, like ChatGPT). Do not do every engine - prioritise based on which one your buyers actually use.

Important: per Ahrefs' May 2026 controlled test on 1,885 pages, schema alone does not move AI Overview citation rates - it is hygiene rather than a standalone lever. The ranking position and the named-author signal do most of the work. Do not get stuck adding more schema thinking it will fix a content-depth problem.

Primary sources for the GEO stack: Princeton GEO paper (Aggarwal et al., 2023), Google's official AI search guidance (May 2025), Search Engine Land's AI crawler optimisation guide, Profound (citation tracking).

Stage 4: Measurement

Weekly or fortnightly sampling against your priority queries. Track three metrics: citation frequency (how often you appear), citation share-of-voice (your appearances vs named competitors), and query coverage (how many of your priority queries cite you at all). Daily creates noise because AI outputs vary per session. Monthly is too slow if you are actively running work.

What to ignore: raw retrieval signals (your page appears in the model's context but is never named), branded queries (vanity, no signal about category authority), and traffic numbers from GA4 alone (AI engines often cite without a click - your brand mention is the win, not the visit).

Stage 5: Iteration

Quarterly content refresh based on the measurement data. Identify queries where you appear and queries where you do not. Where the gap is content-shaped (a competitor with more depth gets cited), add depth. Where the gap is extraction-shaped (your content is there but ChatGPT picks a cleaner source), restructure for extraction. Where the gap is authority-shaped, work the citation pool slowly (Wikipedia entry, Reddit presence, trade publication mentions - none of these compound in a month).

How to spot a GEO programme that is failing

Three patterns I see repeatedly:

• Six weeks of work then silence. GEO is an ongoing discipline. Burst-then-quiet does not build citation density. If the programme has a "campaign" shape, it is not actually GEO.
• "We added schema everywhere." Schema as the headline action means the consultant did not run a baseline. There was no diagnostic - just hygiene work badged as strategy.
• No named-author work on the agency's own site. If they have not done Person schema + credible bios + sustained Reddit presence on their own brand, they cannot do it on yours. Check before you sign.

Common mistakes

• Skipping the baseline. Teams optimise for ChatGPT when the gap is actually in AI Overviews. Wasted budget.
• Doing every engine. Spread thin across six engines, deep on none. Pick the two or three that match your buyer.
• Treating GEO as a campaign. It is an ongoing discipline. Six weeks of work followed by silence does not build citation density.
• Confusing retrieval with citation. Reddit shows up in ChatGPT retrieval all the time. Citation is rare. Track citation, not retrieval-adjacent metrics.
• Outsourcing community presence. Reddit accounts that look fake get downranked. The named expert on your team has to do the posting.