Understand how ChatGPT picks brands
Before optimizing, get the mechanism right. ChatGPT can name your brand from two sources: knowledge baked into its training data, and live web results it retrieves when browsing is active. Training presence is built slowly through broad, favorable coverage across the open web; retrieval presence depends on having clear, current pages that the model can pull and quote in the moment. The strongest GEO targets both.
The practical implication is that you're optimizing for a reader that reasons over consensus. ChatGPT doesn't 'rank' you so much as synthesize what the web seems to agree on, then state it confidently. So the goal isn't to win one page or one keyword — it's to make the overall signal about your category point clearly and repeatedly toward you.
This also means single-source wins don't carry far. One great landing page helps, but a model gains confidence from corroboration: many independent sources describing you the same way. Plan your effort around building that chorus, not just polishing your own megaphone.
Step 1 — Map the questions your buyers actually ask
Start with the prompts, not the keywords. List the real questions a prospect would type into ChatGPT at each stage: category-level ('What's the best tool for X?'), comparison ('A vs B vs C'), use-case ('best option for a small team that needs Y'), and objection ('is X worth it for Z?'). These prompts are your battleground; everything else is in service of winning them.
Then run them. Ask ChatGPT each question and record the answer verbatim — whether you're mentioned, how you're described, and which competitors get named in your place. This baseline is the single most clarifying thing you can do, because it converts a vague worry ('are we visible in AI?') into a specific, trackable list of wins and gaps.
Do this on a recurring cadence rather than once. Answers drift as the web and the model change, so a one-time check gives you a snapshot when you need a trend line. A tracker like GetNamed is designed for exactly this — running your prompt set across ChatGPT and seven other engines weekly so you can watch movement over time instead of guessing.
Step 2 — Build the authority ChatGPT can find
Authority is the raw material of every AI recommendation, and it lives mostly off your own domain. The brands ChatGPT names confidently are the ones the web talks about: included in 'best of' roundups, reviewed on third-party sites, discussed in communities, and compared against alternatives. Your job is to earn legitimate presence in those places.
The honest way to do this is unglamorous but durable. Reach out to publications and reviewers who genuinely cover your space and make the case for inclusion. Get listed in credible directories and comparison sites. Encourage real customers to leave real reviews. Each of these adds an independent voice to the consensus the model reads.
What does not work — and actively backfires — is manufacturing that consensus. Fake reviews, spammed mentions, and astroturfed forum posts are exactly the manipulation signals engines filter for, and getting caught damages the reputation you're trying to build. GetNamed's approach reflects this: any outreach it surfaces is opt-in and waits for your approval, and it draws a hard line against fake reviews, spam, and prompt injection.
- Earn inclusion in genuine 'best X tools' roundups and editorial comparisons.
- Build presence on credible third-party review and directory sites.
- Cultivate authentic customer reviews and case studies.
- Participate honestly in the communities where your buyers actually discuss the category.
Step 3 — Structure your content for extraction
Even with strong authority, you make ChatGPT's job easier — and your inclusion more likely — when your own content is built to be quoted. Models extract claims best from clear, declarative writing: state plainly what you do, who you're for, what you cost, and how you differ, rather than burying it in vague marketing prose.
Concretely, that means leading pages with an unambiguous one-line description, using comparison tables and feature lists that a model can parse, and adding FAQ sections that answer the exact questions buyers ask. These formats aren't just human-friendly; they hand the model pre-packaged, extractable answers it can lift directly into a recommendation.
Keep the facts current and consistent across every page. Conflicting claims — different pricing here, a different positioning there — lower a model's confidence and make it more likely to recommend a competitor whose story is coherent. Treat your core facts as a single source of truth and make sure every surface tells the same story.
Step 4 — Measure, fix the gaps, and repeat
GEO is a loop, not a launch. Once your baseline is in place, focus on the gaps it reveals: the high-intent questions where a competitor gets named and you don't. For each gap, ask why — is it missing third-party corroboration, unclear positioning, or simply that the model hasn't seen enough about you in that context? That diagnosis points to the fix.
Then act, and re-measure. Publish the missing comparison, earn the missing review, clarify the muddy page — and check the same prompts again later to see whether the answer moved. Over time you're not chasing a single recommendation but raising your overall share of voice: the proportion of relevant questions where your brand shows up at all.
This is the core workflow GetNamed automates end to end — weekly tracking across all eight engines, share-of-voice and competitor comparison, gap analysis explaining why you're skipped, and on-brand content suggestions to close those gaps — so the loop runs continuously instead of depending on you remembering to re-check by hand.