AEO 101Single source of truth on AEO

For hotels, airlines, OTAs, and destinations

Be the destination AI recommends when travelers ask where to go.

Trip planning moved from Google flights and TripAdvisor to the AI chat window. The properties named in the answer get the booking.

By Subia Peerzada/Updated May 14, 2026

§01 How does the new buying funnel actually work?

A traveler types one prompt. AI pulls from editorial guides, OTAs, and specialist aggregators. Three properties land in the answer.

01The buyer prompt
>_
Traveler, best boutique hotels in Lisbon under €400 a night
02Retrieval fanout
guidecntraveler.com
guidetravelandleisure.com
aggregatorbooking.com
specialistmr & mrs smith
forumreddit.com/r/portugal
03The named answer
Boutique hotels in Lisbon under €400/night:
Memmo Alfama — view, design, Travelers Choice
Santiago de Alfama — Mr & Mrs Smith pick
Bairro Alto Hotel — design, central location

Editorial guides and specialist aggregators decide the cast. Your own site is rarely the deciding cite.

Your direct booking site is downstream of citation. The work is in the surfaces that decide the answer.

§02 What happened to the old buying funnel?

Trip-planning research collapsed. AI does the destination compare for the traveler.

Pre-AI funnel2023
  1. Traveler Googles 'boutique hotels Lisbon'
  2. Reads a Condé Nast or T+L guide
  3. Checks 5 properties on Booking
  4. Reads TripAdvisor reviews
  5. Compares on price and dates
  6. Books one

6 steps

AI-answer funnel2026
  1. Traveler asks ChatGPT or Perplexity for boutique hotels
  2. AI names 3 properties with rationale
  3. Traveler books the named property

3 steps

The browse-and-compare middle of the trip-planning funnel collapsed. The named property gets the booking.

Top-of-funnel discovery now compresses into a single named answer. Editorial placements and aggregator presence decide who is in it.

§03 Which sources does AI actually read from?

AI travel-recommendation answers come from a specific publisher and aggregator pool. Direct booking sites are not in Tier 1.

The source pool AI reads from

What we influence, tier by tier

01 · Tier 1Editorial travel guidescntraveler.com · travelandleisure.com · afar.com · thepointsguy.comHighest citation weight on best-of and destination prompts. Inclusion in a CNT Gold List or T+L Worlds Best is one of the strongest signals AI can read.
02 · Tier 1OTA property pagesbooking.com · expedia.com · hotels.comAI's primary source for attribute, amenity, and review data. The OTA listing is an AI source-pool input, not a neutral channel.
03 · Tier 2Meta-review aggregatorstripadvisor.com · google.com (reviews) · trustpilotCorroborated traveler sentiment. Cited on prompts that ask about real-stay experience.
04 · Tier 2Specialist publishers and category listsmr & mrs smith · forbes travel guide · michelin keysSub-category authority. Strongest on luxury, boutique, and design-led travel prompts.
05 · Tier 3Owned site, social, and Reddityourbrand.com · reddit destination subs · instagramLower individual weight, compounding when refreshed. Reddit specifically carries weight on practical traveler prompts.

Editorial guides decide the cast. OTAs decide the spec. Aggregators decide the rank. Reddit decides the edge cases.

The work is in the guides and the OTAs. The hotel website matters for verification, not for the AI shortlist.

§04 What metric actually decides the category?

Shortlist composition on one destination prompt, by AI surface.

Citation share visualisation

Prompt: best boutique hotels in Lisbon under €400 a night

Category defaultChallengerLong tail
ChatGPT39% · 25% · 36%
Memmo Alfama
Santiago Alfama
Claude35% · 27% · 38%
Memmo Alfama
Bairro Alto
Perplexity32% · 26% · 42%
Memmo Alfama
Santiago Alfama
Gemini31% · 25% · 44%
Memmo Alfama
Bairro Alto
AI Overviews34% · 23% · 43%
Memmo Alfama
Santiago Alfama

Illustrative shares for one destination prompt. Real engagements track 60 to 150 prompts weekly per destination or property cluster.

Seasonal queries move quickly. Awards from CNT, T+L, or Michelin Keys can shift the bars within a single editorial cycle.

§05 What do we actually ship?

Six lines of work, run weekly, owned by us.

Each block describes the actual work, not a tool we hand over. We carry editorial relationships, OTA-page hygiene, and weekly platform monitoring.

01

Destination prompt citation work

Travelers ask AI where to go: best beach destinations in March, where to ski in April, which Italian cities to visit on a first trip. The answer pool for each destination prompt is decided by a small set of cited guides and editorial sources. We map the destination prompts that matter and engineer presence.

02

Hotel and airline recommendation surfacing

Brand-direct queries (best hotels in Lisbon, best business-class airlines for Asia, top boutique hotels in Tokyo) draw from a recurring set of review sources. We engineer the citation patterns that move recommendation rate for named properties and routes.

03

TripAdvisor, Booking, and aggregator source-pool positioning

Travel aggregators carry disproportionate weight in AI citations. We work the surfaces that feed into TripAdvisor reviews, Booking and Expedia property pages, and the smaller specialist aggregators that AI cites by category.

04

Comparison citation for versus queries

Travelers ask AI to compare properties, airlines, and destinations head to head. We engineer the comparison content and third-party validation that gets cited when a traveler runs your brand against a category peer.

05

Weekly booking-funnel monitoring

Travel queries are seasonal and volatile. A new flight route, a hotel renovation, a destination travel advisory, all of these shift the AI answer pool within days. We monitor the curated prompt set every week and surface movement on the queries that decide bookings.

06

Editorial guide placement work

Condé Nast Traveler, Travel + Leisure, The Points Guy, and AFAR decide most luxury and points-driven travel queries. We work editorial relationships, structured content, and pitch angles that get a property cited inside the guides AI reads most.

§06 The methodology is public

One framework, applied weekly. Research, playbook, and engineering ledger all open.

The methodology

CITE framework

Comprehend, Influence, Track, Evolve

The living playbook

AEO 101

Refreshed daily from our research library

The research

AI Visibility Index

Open library of operator-grade briefs

§07 Questions buyers ask before they engage

The questions hospitality and travel leaders ask before they engage.

How does AI decide which hotels to recommend?
Models pull from a recurring set of travel sources: the major editorial guides (Condé Nast Traveler, Travel + Leisure, The Points Guy, AFAR), the OTA property pages (Booking, Expedia, Hotels.com), the meta-review aggregators (TripAdvisor, Tripadvisor Travelers Choice lists), specialist publications by category (Mr & Mrs Smith for boutique, Forbes Travel Guide for luxury), and Reddit threads in destination subs. AI weighs review recency, named-property specificity, awards from cited guides, and the structure of cited pages. A hotel wins recommendation when it appears across multiple cited sources with consistent positioning, not when the property's own site is rebuilt.
Do Google reviews still matter for AI?
Yes, but less than most travel teams assume. Google reviews feed local entity signals that decide near me queries and AI Overviews on local intent. For broader travel queries (best hotels in a destination, best airlines for a route), AI weighs the editorial press and OTA review aggregates more heavily than Google ratings. The work is to maintain a clean local entity through Google for funnel-bottom queries, while doing the editorial and aggregator work that moves top-of-funnel recommendation.
Why does AI favour OTAs over direct booking sites?
OTAs have higher domain authority, more structured content (rooms, rates, amenities, policies in machine-readable formats), and broader review density per property than most hotel-direct sites. When AI needs a source for a property's attributes, the OTA page is usually the cleaner and more cited option. The implication for direct booking is not to compete with the OTA page; it is to ensure your direct site is the cited source for things the OTA cannot answer (loyalty benefits, suite descriptions, in-house experiences) and to make sure your OTA pages themselves are accurate.
How do destination marketing organisations compete with private brands?
Destination marketing organisations (DMOs) have a structural advantage AI weighs heavily: they are the authoritative source for destination-level information, and AI cites them readily for queries about a region, city, or country. The work for a DMO is to make sure that authority converts into citation across the supporting surfaces (regional press, partner content, travel guides that AI reads), and to engineer the destination prompts that send travelers toward the region rather than to a private competitor. Private brands compete by becoming the cited specialist inside a category the DMO does not cover at depth.
Does AI use Booking.com or Expedia data?
Yes, both heavily. OTA property pages are among the most-cited sources in AI hotel-recommendation answers because they carry structured attribute data, review density, and verified content at scale. The implication for hoteliers is that the OTA listing is not a neutral channel; it is an AI source-pool input. Accurate property descriptions, current amenity lists, correct address and category tagging, and a strong review profile on the OTAs feed directly into how AI describes your property when a traveler asks. We work the OTA surfaces as part of the source-pool program, not as a parallel commercial conversation.

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