Does this mean AI visibility tracking is meaningful for hotels?

Yes, especially in concentrated markets. If you're a hotel in Bordeaux or Vienna, your AI ranking position is highly measurable and stable. In fragmented markets like London or Paris, rankings vary more but patterns still emerge — certain hotels consistently appear in top positions even if the exact order shuffles.

2026 Consistency ResearchFebruary 2026

AI Hotel Rankings Are Not Random

Name: AI Hotel Ranking Consistency Study 2026
Creator: Nicolas Sitter
Published: 2026-02-09
License: https://creativecommons.org/licenses/by/4.0/

Key Finding: SparkToro's research found AI brand recommendations are essentially random — less than 1% produce identical lists. We replicated their methodology for hotels using 4,000 Google AI Mode queries and found the opposite: 50.5% position 1 stability, 33.5% position 2 stability, and 24.2% position 3 stability. In concentrated markets like Berlin family hotels, the same hotel ranks #1 in 96% of queries. Hotels are fundamentally different from generic brand queries because they're constrained by geography (Paris hotels can only be in Paris), query type (luxury vs. boutique), and finite supply (82 hotels in Bordeaux vs. thousands of CRM tools globally).

4,000

Queries

6,249

Hotel Mentions

Cities

17–96%

#1 Stability Range

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SparkToro vs Nicolas Sitter: Methodology Comparison

We replicated SparkToro's methodology but applied it to a vertical-specific domain (hotels) to test whether geographic and supply constraints change AI consistency patterns.

Dimension	SparkToro (2026)	Nicolas Sitter (2026)
Query Type	General brand queries ("best CRM", "project management tools")	Hotel queries ("best luxury hotels Paris", "boutique hotels Vienna")
Sample Size	Multiple queries across brands	4,000 queries, 6,249 hotel mentions
AI System	ChatGPT, Claude, Perplexity	Google AI Mode
Geographic Scope	Global (no location constraint)	8 cities (location-locked queries)
Supply Universe	Infinite (thousands of global brands)	Finite (~50-1000 hotels per city)
Key Metric	Identical list rate	Position stability + top 3 overlap
Finding	<1% consistency	50.5% position 1 stability (range: 17-96%)

The hypothesis: SparkToro's finding is correct for open-ended brand queries — but hotels are structurally different. Geographic constraints + finite supply = predictable hierarchies. Our data proves it: 20-50x higher consistency than general brands.

How We Structured Our Queries

Each query followed the pattern: "best [tier] hotels [city]". We ran 100 identical queries per city×tier combination across 4 proxy locations (US, DE, FR, ES).

Tier	Example Queries	Definition / Intent
Luxury / 5-star	`"best luxury hotels Paris""best 5-star hotels Vienna"`	High-end properties, typically 5-star rated. Focuses on amenities, service, prestige brands.
Boutique / Design	`"best boutique hotels Berlin""best design hotels London"`	Smaller, character-driven properties. Emphasizes unique design, local experience, personality.
Budget / Value	`"best budget hotels Barcelona""best cheap hotels Lisbon"`	Price-focused travelers. Good value properties, hostels, 2-3 star hotels.
Family	`"best family hotels New York""best hotels for families Bordeaux"`	Family travelers with children. Larger rooms, kid-friendly amenities, connecting rooms.
Romantic / Couples	`"best romantic hotels Paris""best hotels for couples Vienna"`	Couples, honeymoons, anniversaries. Intimate settings, spa facilities, romantic ambiance.

Why tiering matters: Different tiers show dramatically different consistency levels. Budget hotels in Vienna show 91% stability (few options), while boutique hotels in London show only 23% (fragmented market with many small players). The tier constrains the pool size, which directly impacts ranking predictability.

Example: Google AI Mode response for "best luxury hotels Paris"

Google AI Mode showing luxury hotel recommendations for Paris including Four Seasons George V, Plaza Athénée, Ritz Paris, Hôtel de Crillon, and Le Meurice

Note: Response shows consistent top-tier hotels — Four Seasons George V, Plaza Athénée, Ritz Paris appear repeatedly across runs.

1. SparkToro vs Hotels: The Numbers

In January 2026, SparkToro published research showing AI brand recommendations are highly inconsistent. We replicated their methodology for hotel queries to test whether the same pattern holds.

SparkToro Finding

<1%

Chance of getting identical brand lists twice. For queries like "best CRM" or "project management tools," AI produces essentially random results.

Their conclusion: "Marketers should take care when tracking AI visibility" — rankings are too unstable to measure meaningfully.

Our Hotel Finding

50.5%

Average position 1 stability. The same hotel appears first in over half of identical query runs. Range: 17% to 96%.

Our conclusion: Hotel rankings ARE measurable. Geographic and query constraints create stable, predictable hierarchies.

Consistency Comparison: Brands vs Hotels (All Positions)

Source: Nicolas Sitter AI Consistency Study, Feb 2026

Key insight: Hotels show 50x higher consistency than general brands. This isn't a flaw in SparkToro's methodology — it's a fundamental difference in how constrained vs. open-ended queries behave in AI systems. Hotels have a finite, geographically-locked supply; brands have infinite global options.

2. Stability Across All Positions

Position 1 gets the most attention, but positions 2 and 3 also show meaningful stability — far above SparkToro's brand findings.

50.5%

(17–96%)

Position 1 Stability

Mean across all markets. Range shows concentrated vs competitive markets.

33.5%

(8–78%)

Position 2 Stability

Still 33x higher than SparkToro's <1% for brand queries.

24.2%

(5–56%)

Position 3 Stability

Meaningful predictability continues even at position 3.

Most Stable Markets (Position 1)

Source: Nicolas Sitter AI Consistency Study, Feb 2026

Least Stable Markets (More Competition)

City	Tier	Top Hotel	Stability
London	Budget	Premier Inn London County Hall	17.0%
Berlin	Boutique	Hotel Telegraphenamt	22.6%
Paris	Boutique	Relais Christine	22.8%
Lisbon	Romantic	The Ivens	23.4%
Paris	Budget	Hôtel du Champ de Mars	24.3%

Pattern: Smaller markets (Bordeaux, Vienna) show higher stability across all positions. Large, competitive markets (London, Paris boutique) show more variation — but even position 3 in the least stable market (24%) is 24x more predictable than SparkToro's brand queries. The hierarchy is real at every level.

3. Top 3 Overlap Analysis

Beyond individual positions, we measured how many of the top 3 hotels overlap between any two query runs. An overlap of 3.0 means identical top 3 every time; 0.0 means completely different lists.

1.06

Avg Overlap (of 3)

2.12

Best: Berlin Family

0.40

Worst: London Romantic

94.3%

Berlin: 2+ hotels match

Highest Top 3 Overlap by Market

Source: Nicolas Sitter AI Consistency Study, Feb 2026

What the numbers mean

2.12 overlap (Berlin Family): On average, 2 out of 3 top hotels are the same between any two query runs
94.3% share 2+ hotels: In Berlin family queries, 94% of run pairs have at least 2 hotels in common
18.1% share all 3: Nearly 1 in 5 runs produce the exact same top 3 list

Markets with highest top 3 consistency
Market	Avg Overlap	2+ Match	All 3 Match
Berlin (Family)	2.12	94.3%	18.1%
Bordeaux (Boutique)	1.97	89.6%	12.9%
Bordeaux (Luxury)	1.84	70.3%	22.7%
Bordeaux (Budget)	1.81	76.2%	14.8%
Vienna (Family)	1.76	67.3%	8.8%

Lowest Top 3 Overlap (Most Competitive Markets)

Market	Avg Overlap	2+ Match	All 3 Match
London (Romantic)	0.40	7%	0%
Paris (Boutique)	0.44	8.3%	0.3%
London (Boutique)	0.47	4.8%	0.2%
Lisbon (Romantic)	0.47	8.6%	1.5%
Berlin (Boutique)	0.55	10.8%	0.7%

What this means: In Berlin's family hotel segment, 94% of query runs share at least 2 of the same top 3 hotels. In contrast, London's romantic segment shows only 7% overlap — each query surfaces a different set. The data confirms: AI visibility is highly measurable in concentrated markets, and still meaningful (though more volatile) in fragmented ones.

4. Dominant Hotels by Position

Some hotels have locked in the #1 position across hundreds of queries. These aren't random — they represent genuine AI visibility leaders. Contact these hotels — they'd want to know.

Hotels with strongest position lock-in by query type
Hotel	City	Query Type	Mentions	Avg Position	#1 Rate	Top 3 Rate
Hotel Austria	Vienna	Budget	54	1.04	98.1%	100%
Hotel Adlon Kempinski	Berlin	Family	99	1.09	93.9%	99%
InterContinental Le Grand	Bordeaux	Luxury	46	1.11	91.3%	100%
Hotel Sacher Wien	Vienna	Luxury	89	1.26	78.7%	100%
Claridge's	London	Luxury	69	1.33	82.6%	95.7%
Villas Foch	Bordeaux	Boutique	143	1.33	72%	98.6%

🥇

Hotel Austria (Vienna)

Appears #1 in 98.1% of relevant queries. The most locked-in hotel in our dataset.

Budget tier, 54 mentions, avg position 1.04

🥈

Hotel Adlon Kempinski (Berlin)

93.9% position 1 rate across 99 mentions. Dominates family hotel queries.

Family tier, 99 mentions, avg position 1.09

🥉

InterContinental Le Grand (Bordeaux)

91.3% position 1 rate. Dominates luxury queries in a smaller market.

Luxury tier, 46 mentions, avg position 1.11

The hierarchy is real: These hotels don't randomly appear first — they consistently outrank competitors. AI visibility is measurable, and some hotels have effectively "locked in" the top position in their market. This is the "Position 0" of AI search.

5. Why Hotels Are Different From Brands

Three structural factors explain why hotel recommendations show 50x more consistency than SparkToro's brand findings — backed by our market concentration data.

Location Constraint

A "Paris hotel" query can only return Paris hotels. Unlike "best CRM" which draws from a global, infinite pool, hotels are locked to a specific geography.

Data point: 100% of Paris hotel responses contain Paris hotels. 0% of "best CRM" responses are location-locked.

Query Type Constraint

"Luxury hotels Vienna" further narrows the set. Each query tier (boutique, budget, family) creates a smaller, more stable consideration set.

Data point: Adding tier constraint increases position 1 stability from ~40% (generic) to 60-96% (tier-specific).

Finite Supply

Bordeaux has 82 hotels in AI consideration. London has ~226. Compare to thousands of CRM vendors globally. Smaller universes create more predictable rankings.

Data point: Bordeaux (82 hotels) = 82-90% stability.

Concentration Predicts Consistency

Our Google AI Mode Hotel Study measured market concentration using the Herfindahl-Hirschman Index (HHI). The correlation is clear:

High HHI = High Stability

Bordeaux HHI: 1,169 → 82-90% position stability

Low HHI = Lower Stability

London HHI: 175 → 17-23% position stability

View full concentration analysis

The implication: Hotel AI visibility isn't random — it's predictable based on market structure. Know your market's concentration, and you can predict how stable your rankings will be. Concentrated markets reward consistent optimization; fragmented markets require appearing in multiple top positions.

Frequently Asked Questions

No. SparkToro found <1% consistency for general brand queries, but hotels show 50.5% position 1 stability on average. The difference is structural: hotels are geographically constrained, while brands draw from an infinite global pool. Some hotel markets show 96% stability.

This study measured Google AI Mode specifically, finding 50.5% position 1 stability. Our separate ChatGPT studies show similar patterns — hotel recommendations are far more consistent than general brand queries due to geographic constraints. See our Yelp ChatGPT study and Anatomy of ChatGPT Hotel Search for ChatGPT-specific data.

Smaller, concentrated markets show highest stability: Berlin family hotels (96%), Vienna budget (91%), Bordeaux boutique (90%). These markets have fewer competitors and clearer hierarchy. Large, fragmented markets like London boutique (23%) show more variation but are still 23x more predictable than brand queries.

Yes. Hotels with strong fundamentals — optimized Google Business Profile, consistent positive reviews, authoritative brand presence across multiple sources — tend to lock in top positions. Our dominant hotels analysis shows some properties maintain #1 rankings in 98% of queries. The stability means optimization efforts compound over time.

It depends on market concentration. In stable markets (Bordeaux, Vienna), top positions rarely change — the same hotel appears #1 in 80-96% of queries. In competitive markets (London, Paris), positions shuffle more frequently but the top 3-5 hotels remain consistent. Major shifts typically follow model updates or significant changes in review signals.

Not necessarily. Our data shows independent hotels like Hotel Austria (Vienna) and Villas Foch (Bordeaux) achieving 90%+ position 1 stability. What matters more than chain size is: (1) strong local presence in the specific market, (2) consistent review signals, and (3) clear category positioning. Boutique and independent hotels can and do dominate their segments.

Three factors: (1) Location constraint — a Paris query only returns Paris hotels, (2) Query type constraint — 'luxury' or 'boutique' further narrows options, (3) Finite supply — Bordeaux has 82 hotels vs thousands of CRM tools globally. These constraints create stable hierarchies.

High-concentration markets (measured by HHI in our Google AI Mode study) correlate with high consistency. Bordeaux has HHI 1,169 and 82-90% stability. London has HHI 175 and 17-23% stability. Market structure predicts ranking predictability.

Position 2 shows 33.5% average stability (same hotel appears in position 2 in a third of runs). Position 3 shows 24.2% stability. While lower than position 1, these are still 33x and 24x higher than SparkToro's <1% for brands. The hierarchy is real at every level.

Focus on your specific market segment. If you're in a concentrated market (smaller city, specific tier), maintaining your position matters — competitors can't easily displace you. In fragmented markets, aim to be consistently in the top 3-5 rather than locked at #1. Both are achievable and measurable.

Continue Reading

Explore more Nicolas Sitter research on AI hotel search.

AI Hotel Landscape 2026

Google AI Mode Study Anatomy of ChatGPT Search All Research

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