--- title: "GEO vs AEO — how generative engines pick a hotel" description: "An explainer on the two disciplines behind AI visibility: GEO (the brand narrative) and AEO (the hard facts), and why hotels need both to be recommended and booked." url: "https://sigtrip.com/geo-vs-aeo/" --- # How AI decides which hotel to recommend. Generative engines like ChatGPT and Perplexity don't rank links anymore — they synthesise an answer. Two disciplines decide whether your property makes that answer: GEO for the brand narrative, AEO for the hard facts. [Run a free analysis →](https://sigtrip.com/ai-visibility-scan/) [Talk to the team](https://sigtrip.com/contact/) ## One tracks narrative. The other tracks facts. They sound similar and often share tooling, but GEO and AEO solve different problems for an AI model. Most properties need a strategy for both. GEO · Generative Engine Optimization ### Works on the brand narrative The answer to: “when an AI describes this hotel, how does it describe it?” Built from press, reviews, social mentions, partner content and editorial coverage — the unstructured layer. Win condition the model associates your entity with the right adjectives — exclusive, design-forward, family-run, eco-led — and cites you when those qualities are asked for. AEO · Answer Engine Optimization ### Works on the structured facts The facts a model can extract and answer directly — Schema.org markup, FAQ pages, amenity tags, policy data — content that maps cleanly to a question's literal terms. Win condition when a traveler asks a yes-or-no or numeric question, the model gives the correct answer with confidence — and cites you as the source. ## Watch the model think — five hops, two ways. The same query runs a different gauntlet depending on what it asks for. Here is where your property is won — or quietly dropped. GEO pipeline ### From review corpus to citation, in five hops. The model is asking: “who is this property, in their own words?” - 01Intent· Traveler query “Best boutique hotel in Lisbon under €250” - 02Lookup· Entity match Find candidate properties from the model’s knowledge graph - 03Context· Narrative pull Editorial reviews, press, social — what tone wins? - 04Score· Adjective alignment Match the property’s narrative against the query’s intent words - 05Cite· Answer + citation Up to 3 properties recommended — the rest are invisible Watchingthe model spends most of its compute on step 03 — pulling unstructured narrative from across the web. If your property’s narrative is thin, generic, or contradictory, you don’t survive the score. AEO pipeline ### From structured fact to direct answer, in five hops. The model is asking: “do they meet these specific constraints?” - 01Intent· Constraint query “Pet-friendly · free parking · under $200 · near JFK” - 02Parse· Decompose constraints Translate the query into structured field filters - 03Retrieve· Fact lookup Schema.org, FAQ pairs, amenity tags from indexed pages - 04Verify· Cross-check Multiple sources must agree — contradictions disqualify - 05Answer· Direct snippet A short, confident answer with your name as the source Watchingstep 04 is where most properties die. The model finds your hotel says “pets allowed” on one page and “no pets” on a Booking listing — it can’t reconcile, so it picks a competitor whose facts agree with themselves. ## Same goal. Different muscles. DimensionGEOAEO Optimises for“Best for X” intent — qualitative, descriptive“Does it have Y” intent — factual, constrained Lives inPress, editorial, reviews, social — unstructuredSchema.org, FAQ pages, amenity feeds — structured Refresh cadenceSlow — months to shift narrativeFast — days to update facts Failure modeWrong adjectives stick — “good value” when you want “exclusive”Contradictions across sources — the model picks a competitor Spend tilts towardDigital PR, content, brand citationsSchema engineering, data hygiene, FAQ programs Wins for segmentPremium & luxury — intent is qualitativeEconomy & mid-scale — intent is utility ## What it takes to manually improve each. None of this is impossible. The list ticks itself off as you read — these are the operational items you'd assign to a marketing lead, a content team, or an external agency. Per property. Per month. GEO ### Improving narrative 7 items - Audit every public mention of the property — every source the model can ingest. ~ 200–2,000 mentions per established hotel - Define the target narrative — three to five adjectives the brand must own. requires a GM & brand alignment workshop - Commission digital PR in the publications models actually weight. ~ 4–8 placements / quarter, $3–15k each - Update partner copy — DMOs, OTA descriptions, GDS content — to match the target adjectives. 15+ partners per property, cyclical - Surface guest stories that reinforce the narrative — long-form reviews, video. - Test how each engine describes the property — weekly, by hand, via prompt. 6 engines × ~12 query types = 72 prompts / wk - Track the sentiment gap — what the model says vs. what you want it to say. no consumer tool does this; a spreadsheet today AEO ### Improving facts 7 items - Implement Hotel + LodgingBusiness schema across every public page. ~ 60–120 schema fields per property - Build FAQ pairs for the top 50 traveler intents per market. refresh quarterly as intents drift - Reconcile facts across every channel — your site, OTA listings, GDS, partner pages. ~ 12–20 surfaces per property - Update the moment a fact changes — pet policy, parking, hours, kid age limits. a 24h lag is enough to lose the citation - Verify each engine’s answer to a constraint query — by hand, with prompts. 6 engines × ~80 query types = 480 prompts / wk - Catch drifting facts before a model trains on them — before they harden. window: roughly the model’s training cadence - Track the answer-accuracy gap — when a model is wrong, document and dispute it. no native dispute channel exists for most engines ### You can do all of it. Once. For one property. For one week. 0 Engines × 0 Traveler personas × 0 Query types × 0 Refresh / day =0 data points · per property · per day A single hotel needs roughly 138,240 lookups a day to keep GEO and AEO healthy across the engines that matter. Now multiply by your portfolio. This is what we automate. ## Don't pick one. Do both. One tells you where you stand today. The other tells you where the budget should go next quarter. [### Run the AI Visibility scan. See your property's actual visibility score, mention velocity, and the sentiment gap between what the AI says and what you want it to say.Run a free analysis →](https://sigtrip.com/ai-visibility-scan/) [### Plan the GEO & AEO spend shift. Three sliders — ADR, star rating, location intensity — and a recommended channel mix. The starting allocation you take to your CFO.Open the calculator →](https://sigtrip.com/geo-spend-calculator/) ## You can fight 138,000 lookups a day. Or we can. Sigtrip runs the GEO and AEO loop continuously — across AI engines, every persona, every market — and tells you exactly what to fix, in priority order. [See done-for-you GEO →](https://sigtrip.com/geo/) [Run a free analysis](https://sigtrip.com/ai-visibility-scan/)