{"@context":"https://schema.org","@type":"BlogPosting","headline":"Best Western France: Does ChatGPT Know the Network? (2026)","description":"A brand AI-visibility audit of Best Western France's 322-hotel/242-city network, unaffiliated with Best Western. Methodology: French/FR-proxy prompts across all 242 reconciled cities (matching Best Western France's own press-release disclosure that Corporate revenue dwarfs Leisure and foreign clientele concentrates in Paris + PACA), plus an English/US-proxy add-on scoped to those 37 Paris+PACA cities. 8,370 unbranded prompts (no \"Best Western\" in any prompt text), 23,220 of 25,110 planned ChatGPT calls landed, despite a live Bright Data SSE-stream outage (since ~2026-08-17) that nulled raw-stream/map data in 98.2% of captures — the core citation/mention signal was unaffected. HEADLINE: Best Western shows up organically 64.8% of the time (14,023/21,624 captures with a resolved mention), average best rank 2.12, #1 pick 46.7% of the time. A 28-point single-vs-multi-hotel-city gap (69.2% vs 41.4%) — multi-property cities are disproportionately the bigger, more competitive markets (Paris, Nice, Marseille, Cannes). Budget framing is the weak spot: 33.8% mention rate vs 64.9% for 4-star. Business-traveler persona (80.7%) far outpaces couples (56.2%), consistent with the network's own Corporate-vs-Leisure revenue split. French/FR-proxy vs English/US-proxy in Paris+PACA: 55.7% vs 50.1%, a real but modest 5.6-point gap. Soft-brand naming (Sure Hotel, BW Premier/Signature Collection) is recognized far less reliably (85.2% exact-match on flagship names vs fuzzy/alias paths for soft-brands). Two cities with 20+ captures never surfaced Best Western: Honfleur and Lyon (AI recommends InterContinental, MGallery and Vieux-Lyon boutique properties instead). Top competitors named instead: Hôtel Le Nouveau Monde, InterContinental Marseille–Hôtel Dieu, Golden Tulip Saint-Malo. Disclosure: no affiliation to Best Western.","datePublished":"2026-08-31","dateModified":"2026-08-31","url":"https://nicolassitter.com/research/best-western-france-ai-visibility-2026","category":"research","keywords":["Best Western France AI visibility","Best Western ChatGPT","hotel brand AI audit","AI search hotel brand","unbranded prompt study","GEO hotel brands","AI local search study"],"articleSection":"Research","wordCount":2600,"readTime":"11 min","articleBody":"Brand AI-visibility audit · August 2026Hotels · Brand Visibility · GEO\n\n# Best Western France:Does ChatGPT know the network?\n\n**TL;DR:** We asked ChatGPT 8,370 unbranded questions — never once naming “Best Western” — across every one of the network’s 242 French cities. Best Western surfaced organically in **64.8% of answers** where the query never named it. But that headline hides a 28-point gap between towns where it’s the only real option and cities where it has to compete, a near-disappearance on budget-framed searches, and two cities where it never showed up at all.\n\nNS\n\nNicolas Sitter\n\nPublished August 2026 · third-party study, no affiliation to Best Western\n\n64.8%\n\nOrganic mention rate\n\n23,220\n\nAI captures\n\n322\n\nHotels tracked\n\n2.12\n\nAvg. best rank when mentioned\n\n[Read the Report](#intro)\n\n[Overview](#intro)[Methodology](#methodology)[Where it’s visible](#map)[Explore the data](#explore)[City competition gap](#city-gap)[Tiers & personas](#tiers-personas)[French vs English](#language)[Soft-brand naming](#naming)[Zero-mention cities](#zero-cities)[Top competitors](#competitors)[Recommendations](#recommendations)[FAQ](#faq)\n\n## An interactive brand AI-visibility audit\n\nBest Western France operates 322 reconciled hotels across 242 cities — mostly 3-4 star, provincial, roadside-and-business inventory, not the palace-and-boutique segment most AI hotel coverage focuses on. This page asks a narrower, more useful question than “is Best Western good at AI SEO”: when someone asks a completely generic hotel question about a town where Best Western operates — never naming the brand — does the AI mention it anyway?\n\nIt’s both a project and an article: the map and explorer below are interactive, backed by the same live snapshot tables that power the citable [data feed](/api/landscape/best-western-france). The prose sections around them walk through what we found, in the order the questions get more specific: does it show up at all, where does it stop showing up, and who takes its place when it doesn’t.\n\nMethodology\n\n## French + FR-proxy is the primary pass, not English + US\n\nMost of this site’s cross-vertical AI-search field tests default to English-only, US-proxy-only prompts, matching an international-traveler assumption. That convention doesn’t transfer to a brand study of Best Western France specifically, because most of its clientele isn’t that. The evidence comes directly from Best Western France’s own 2025/2026 press releases (pressroom.bestwestern.fr):\n\nCorporate revenue dwarfs Leisure — €44.3M (+9% YoY) vs €2.95M (+54% YoY, off a tiny base). On the Leisure segment’s growth, Best Western France’s own words: “_le retour du tourisme international_” (the return of international tourism) — framing international leisure as a recovering, smaller segment, not the norm. And critically: “_une présence plus marquée de la clientèle étrangère à Paris et en région PACA_” — foreign clientele is explicitly called out as concentrated in Paris and PACA (Nice, Cannes, Antibes, Marseille, Toulon, Avignon…), not spread evenly across the network.\n\nNational context backs this up directionally: INSEE puts France’s 3-star hotel clientele at roughly 40-47% foreign depending on season — but that figure is itself Paris/Riviera-weighted at the national level, consistent with the other ~76% of this network (240 of 242 reconciled cities, everywhere outside Paris+PACA) sitting well below it, toward domestic French.\n\n**Design consequence:** two passes, not a uniform cross-product. A primary pass (French, FR-proxy) across **all 242 reconciled cities**, matching the real, majority-domestic, majority-business clientele across the network; and an add-on pass (English, US-proxy) scoped to **Paris + all 36 PACA cities (37 total)** — exactly where Best Western’s own reporting says foreign leisure clientele concentrates. 8,370 distinct prompts, every one unbranded (no “Best Western” anywhere in the text): the question is whether the brand surfaces when someone asks a generic hotel question about a destination where it has a property, not whether the AI can describe a hotel the user already named.\n\n**Disclosed confound: a live Bright Data outage.** Bright Data’s ChatGPT crawler had an unresolved SSE-stream capture regression starting ~2026-08-17 (worsening intraday): the raw stream and structured map/business-location data came back null in **98.2% of captures** (411/23,220 unaffected). This study’s core signal — mentions, ranking, citation domains — runs off the 2-step JSON follow-up (`additional_answer_text` plus `citations`), which populated cleanly and was unaffected by the outage.\n\n8,370\n\nUnbranded prompts\n\n23,220\n\nCaptures landed\n\nof 25,110 planned\n\n242\n\nCities (FR pass)\n\n37\n\nParis+PACA cities (EN add-on)\n\nWhere it’s visible\n\n## Every tracked property, colored by mention rate\n\nHover any dot for its city’s mention rate. Larger, white-ringed dots are flagship-named properties; smaller, dark-ringed dots are soft-brand properties (Sure Hotel, BW Premier/Signature Collection) — which, as the naming section below shows, get recognized far less reliably.\n\n#### 267 Best Western France properties — colored by AI mention rate\n\nCity mention rate: 0% → 100%No mention-rate data yetFlagship-named (larger, white ring) — 203Soft-brand (smaller, dark ring) — 64\n\nExplore the data\n\n## Filter by dimension, search by city\n\nThe same three cuts driving the findings below, live: mention rate by query dimension (archetype, star/budget tier, persona, amenity, language), a searchable/sortable city leaderboard, and the competitor leaderboard.\n\nMention rate by dimension\n\nCity leaderboard\n\nCity\n\nRegion\n\nHotels\n\nCaptures\n\nMention rate\n\nAvg rank\n\nPort Jerome Sur Seine\n\nHaute-Normandie\n\n1\n\n90\n\n100%\n\n1.1\n\nLe Poinconnet\n\nCentre\n\n1\n\n90\n\n100%\n\n1.2\n\nFeytiat\n\nLimousin\n\n1\n\n89\n\n100%\n\n2.1\n\nPerignat Les Sarlieve\n\nAuvergne\n\n1\n\n89\n\n100%\n\n1.3\n\nLa Tour De Salvagny\n\nRhone-Alpes\n\n1\n\n89\n\n100%\n\n1.4\n\nFresnes-Les-Montauban\n\nNord-Pas-de-Calais\n\n1\n\n89\n\n100%\n\n1.4\n\nSaint-Brice-Courcelles\n\nChampagne-Ardenne\n\n1\n\n89\n\n100%\n\n1.4\n\nVertus Blancs Coteaux\n\nChampagne-Ardenne\n\n1\n\n89\n\n100%\n\n1.4\n\nVarennes-Jarcy\n\nIle-de-France\n\n1\n\n89\n\n100%\n\n1.2\n\nBaldersheim\n\nAlsace\n\n1\n\n88\n\n100%\n\n1.3\n\nDorlisheim\n\nAlsace\n\n1\n\n88\n\n100%\n\n1.5\n\nRang Du Fliers\n\nNord-Pas-de-Calais\n\n1\n\n87\n\n100%\n\n1.4\n\nPort-Saint-Pere\n\nPays-de-la-Loire\n\n1\n\n86\n\n100%\n\n1.1\n\nMeung Sur Loire\n\nCentre\n\n1\n\n86\n\n100%\n\n1.5\n\nFournels\n\nLanguedoc-Roussillon\n\n1\n\n60\n\n100%\n\n1.3\n\nTalant\n\nBourgogne\n\n1\n\n60\n\n100%\n\n1.2\n\nSaulx Les Chartreux\n\nIle-de-France\n\n1\n\n59\n\n100%\n\n1.4\n\nSaint-Pierre-Du-Mont\n\nAquitaine\n\n1\n\n59\n\n100%\n\n1.5\n\nFontaine Le Comte\n\nPoitou-Charentes\n\n1\n\n30\n\n100%\n\n1.5\n\nLe Passage\n\nAquitaine\n\n1\n\n10\n\n100%\n\n1.7\n\nPlouescat\n\nBretagne\n\n1\n\n90\n\n98.9%\n\n1.6\n\nCouiza\n\nLanguedoc-Roussillon\n\n1\n\n89\n\n98.9%\n\n1.2\n\nAnzin-Saint-Aubin\n\nNord-Pas-de-Calais\n\n1\n\n88\n\n98.9%\n\n1.2\n\nMargny-Les-Compiegne\n\nPicardie\n\n1\n\n88\n\n98.9%\n\n1.1\n\nMarguerittes\n\nLanguedoc-Roussillon\n\n1\n\n87\n\n98.9%\n\n1.4\n\nMaisons Laffitte\n\nIle-de-France\n\n1\n\n70\n\n98.6%\n\n1.3\n\nDucey\n\nBasse-Normandie\n\n1\n\n60\n\n98.3%\n\n1.8\n\nJons\n\nRhone-Alpes\n\n1\n\n59\n\n98.3%\n\n1.3\n\nLescar\n\nAquitaine\n\n1\n\n90\n\n97.8%\n\n1.4\n\nBuc\n\nIle-de-France\n\n1\n\n89\n\n97.8%\n\n1.5\n\nRis-orangis\n\nIle-de-France\n\n1\n\n89\n\n97.8%\n\n1.4\n\nDourdan\n\nIle-de-France\n\n1\n\n46\n\n97.8%\n\n1.3\n\nMaubeuge\n\nNord-Pas-de-Calais\n\n1\n\n88\n\n97.7%\n\n1.3\n\nPauillac\n\nAquitaine\n\n1\n\n86\n\n97.7%\n\n1.9\n\nBressols\n\nMidi-Pyrenees\n\n1\n\n90\n\n96.7%\n\n1.4\n\nBois-colombes\n\nIle-de-France\n\n1\n\n90\n\n96.7%\n\n1.4\n\nMarquise\n\nNord-Pas-de-Calais\n\n1\n\n90\n\n96.7%\n\n1.4\n\nQuetigny\n\nBourgogne\n\n1\n\n90\n\n96.7%\n\n1.3\n\nArmbouts-Cappel\n\nNord-Pas-de-Calais\n\n1\n\n60\n\n96.7%\n\n1.3\n\nMoissac Bellevue\n\nPACA\n\n1\n\n119\n\n96.6%\n\n1.1\n\nGuyancourt\n\nIle-de-France\n\n1\n\n87\n\n96.6%\n\n2.4\n\nLoos\n\nNord-Pas-de-Calais\n\n1\n\n87\n\n96.6%\n\n1.2\n\nAndrezieux-Boutheon\n\nRhone-Alpes\n\n1\n\n90\n\n95.6%\n\n1.7\n\nEysines\n\nAquitaine\n\n1\n\n89\n\n95.5%\n\n1.2\n\nPontorson\n\nBasse-Normandie\n\n1\n\n89\n\n95.5%\n\n1.7\n\nBoujan-Sur-Libron\n\nLanguedoc-Roussillon\n\n1\n\n88\n\n95.5%\n\n1.4\n\nMoissac\n\nMidi-Pyrenees\n\n1\n\n86\n\n95.3%\n\n2.3\n\nVendôme\n\nCentre\n\n1\n\n60\n\n95%\n\n1.6\n\nChanteloup-en-Brie\n\nIle-de-France\n\n1\n\n53\n\n94.3%\n\n1.7\n\nPeronne\n\nPicardie\n\n1\n\n90\n\n93.3%\n\n2.1\n\nArgentan\n\nBasse-Normandie\n\n1\n\n88\n\n93.2%\n\n2.3\n\nRemiremont\n\nLorraine\n\n1\n\n59\n\n93.2%\n\n1.6\n\nThoirymulti-hotel\n\nIle-de-France\n\n2\n\n59\n\n93.2%\n\n2.0\n\nSoissons\n\nPicardie\n\n1\n\n66\n\n92.4%\n\n1.6\n\nKaysersberg\n\nAlsace\n\n1\n\n90\n\n92.2%\n\n1.8\n\nGuerandemulti-hotel\n\nPays-de-la-Loire\n\n2\n\n60\n\n91.7%\n\n2.1\n\nBois Guillaume\n\nHaute-Normandie\n\n1\n\n59\n\n91.5%\n\n1.1\n\nFlers\n\nBasse-Normandie\n\n1\n\n59\n\n91.5%\n\n1.9\n\nLoches\n\nCentre\n\n1\n\n58\n\n91.4%\n\n1.6\n\nThionville\n\nLorraine\n\n1\n\n57\n\n91.2%\n\n2.1\n\nEpagny Metz-Tessy\n\nRhone-Alpes\n\n1\n\n90\n\n91.1%\n\n1.5\n\nLarmor-plage\n\nBretagne\n\n1\n\n90\n\n91.1%\n\n1.7\n\nMarsac-Sur-L'Isle\n\nAquitaine\n\n1\n\n90\n\n91.1%\n\n1.4\n\nRouvignies\n\nNord-Pas-de-Calais\n\n1\n\n89\n\n91%\n\n1.7\n\nChantilly\n\nPicardie\n\n1\n\n88\n\n90.9%\n\n1.6\n\nRambouillet\n\nIle-de-France\n\n1\n\n90\n\n90%\n\n1.8\n\nHolnon\n\nPicardie\n\n1\n\n90\n\n90%\n\n1.4\n\nTrignac\n\nPays-de-la-Loire\n\n1\n\n90\n\n90%\n\n2.0\n\nSaint Quentin\n\nPicardie\n\n1\n\n30\n\n90%\n\n2.4\n\nTonnay-Charente\n\nPoitou-Charentes\n\n1\n\n88\n\n89.8%\n\n1.7\n\nClisson\n\nPays-de-la-Loire\n\n1\n\n89\n\n88.8%\n\n1.7\n\nRamonville-saint-agne\n\nMidi-Pyrenees\n\n1\n\n89\n\n88.8%\n\n1.5\n\nLectoure\n\nMidi-Pyrenees\n\n1\n\n88\n\n88.6%\n\n1.4\n\nSausset-Les-pins\n\nPACA\n\n1\n\n174\n\n88.5%\n\n1.6\n\nChassieu\n\nRhone-Alpes\n\n1\n\n60\n\n88.3%\n\n1.2\n\nBondues\n\nNord-Pas-de-Calais\n\n1\n\n60\n\n88.3%\n\n2.3\n\nMeudon-la-foret\n\nIle-de-France\n\n1\n\n60\n\n88.3%\n\n1.7\n\nCastelnau Le Lez\n\nLanguedoc-Roussillon\n\n1\n\n59\n\n88.1%\n\n1.9\n\nRiquewihr\n\nAlsace\n\n1\n\n90\n\n87.8%\n\n1.4\n\nLaguiole\n\nMidi-Pyrenees\n\n1\n\n87\n\n87.4%\n\n1.6\n\nPrades\n\nLanguedoc-Roussillon\n\n1\n\n90\n\n86.7%\n\n2.5\n\nVincennes\n\nIle-de-France\n\n1\n\n90\n\n86.7%\n\n2.3\n\nLorientmulti-hotel\n\nBretagne\n\n2\n\n60\n\n86.7%\n\n2.0\n\nPertuis\n\nPACA\n\n1\n\n146\n\n85.6%\n\n1.7\n\nColomiers\n\nMidi-Pyrenees\n\n1\n\n88\n\n85.2%\n\n1.7\n\nOrléans\n\nCentre\n\n1\n\n90\n\n84.4%\n\n2.4\n\nSaclay\n\nIle-de-France\n\n1\n\n89\n\n84.3%\n\n1.9\n\nCahors\n\nMidi-Pyrenees\n\n1\n\n60\n\n83.3%\n\n1.6\n\nEmbrun\n\nPACA\n\n1\n\n178\n\n82.6%\n\n1.6\n\nLe Cannet Des Maures\n\nPACA\n\n1\n\n90\n\n82.2%\n\n2.7\n\nAuray\n\nBretagne\n\n1\n\n89\n\n82%\n\n2.7\n\nSaint-laurent-du-var\n\nPACA\n\n1\n\n176\n\n81.8%\n\n2.8\n\nCanet Plage\n\nLanguedoc-Roussillon\n\n1\n\n88\n\n81.8%\n\n2.0\n\nLes Ponts De Ce\n\nPays-de-la-Loire\n\n1\n\n89\n\n80.9%\n\n2.2\n\nSainte-maximemulti-hotel\n\nPACA\n\n2\n\n146\n\n80.8%\n\n2.9\n\nBourgoin-Jallieu\n\nRhone-Alpes\n\n1\n\n30\n\n80%\n\n2.8\n\nFougères\n\nBretagne\n\n1\n\n89\n\n79.8%\n\n2.3\n\nSartene\n\nCorse\n\n1\n\n89\n\n79.8%\n\n2.5\n\nLa Ciotat\n\nPACA\n\n1\n\n148\n\n79.1%\n\n1.5\n\nBesse-Et-Saint-Anastaise\n\nAuvergne\n\n1\n\n88\n\n78.4%\n\n1.7\n\nAurillac\n\nAuvergne\n\n1\n\n60\n\n78.3%\n\n2.0\n\nGradignan\n\nAquitaine\n\n1\n\n60\n\n78.3%\n\n2.1\n\nSaint Amand Les Eaux\n\nNord-Pas-de-Calais\n\n1\n\n89\n\n77.5%\n\n2.0\n\nSaint-Herblainmulti-hotel\n\nPays-de-la-Loire\n\n2\n\n88\n\n77.3%\n\n2.4\n\nBourg-Les-Valence\n\nRhone-Alpes\n\n1\n\n88\n\n77.3%\n\n3.1\n\nVillefranche-sur-Saône\n\nRhone-Alpes\n\n1\n\n88\n\n77.3%\n\n1.6\n\nSaint-Cyr-Sur-Mer\n\nPACA\n\n1\n\n148\n\n77%\n\n2.6\n\nBesanconmulti-hotel\n\nFranche-Comte\n\n2\n\n60\n\n76.7%\n\n2.3\n\nSaint Jean De Maurienne\n\nRhone-Alpes\n\n1\n\n60\n\n76.7%\n\n2.5\n\nSari-Solenzara\n\nCorse\n\n1\n\n60\n\n76.7%\n\n1.4\n\nReimsmulti-hotel\n\nChampagne-Ardenne\n\n2\n\n89\n\n76.4%\n\n2.5\n\nVelizy Villacoublay\n\nIle-de-France\n\n1\n\n89\n\n76.4%\n\n2.9\n\nDole\n\nFranche-Comte\n\n1\n\n59\n\n76.3%\n\n1.8\n\nMandelieu La Napoule\n\nPACA\n\n1\n\n147\n\n76.2%\n\n1.9\n\nChenove\n\nBourgogne\n\n1\n\n88\n\n76.1%\n\n2.3\n\nEpinal\n\nLorraine\n\n1\n\n88\n\n76.1%\n\n1.7\n\nChâteauroux\n\nCentre\n\n1\n\n89\n\n75.3%\n\n2.3\n\nBalaruc-les-bains\n\nLanguedoc-Roussillon\n\n1\n\n87\n\n74.7%\n\n3.0\n\nAubagne\n\nPACA\n\n1\n\n118\n\n74.6%\n\n2.2\n\nValence\n\nRhone-Alpes\n\n1\n\n88\n\n73.9%\n\n2.6\n\nChateau-thierry\n\nPicardie\n\n1\n\n90\n\n73.3%\n\n2.2\n\nSenlis\n\nPicardie\n\n1\n\n90\n\n73.3%\n\n2.3\n\nBedee\n\nBretagne\n\n1\n\n89\n\n73%\n\n1.1\n\nOrleans\n\nCentre\n\n1\n\n89\n\n73%\n\n2.3\n\nVitrolles\n\nPACA\n\n1\n\n179\n\n72.1%\n\n1.7\n\nTournus\n\nBourgogne\n\n1\n\n74\n\n71.6%\n\n2.2\n\nSaint-paul-les-dax\n\nAquitaine\n\n1\n\n62\n\n71%\n\n2.4\n\nMaussane-les-alpilles\n\nPACA\n\n1\n\n174\n\n70.7%\n\n3.0\n\nLimoges\n\nLimousin\n\n1\n\n57\n\n70.2%\n\n1.9\n\nSelestat\n\nAlsace\n\n1\n\n90\n\n70%\n\n2.0\n\nVillard-de-lans\n\nRhone-Alpes\n\n1\n\n59\n\n69.5%\n\n2.6\n\nBormes-les-Mimosas\n\nPACA\n\n1\n\n176\n\n69.3%\n\n1.6\n\nPloubazlanec\n\nBretagne\n\n1\n\n90\n\n68.9%\n\n2.5\n\nGemenos\n\nPACA\n\n1\n\n147\n\n68.7%\n\n2.6\n\nLe Grand-bornand\n\nRhone-Alpes\n\n1\n\n88\n\n68.2%\n\n2.4\n\nMontreuil-sur-Mer\n\nNord-Pas-de-Calais\n\n1\n\n89\n\n67.4%\n\n2.9\n\nBrive-La-Gaillarde\n\nLimousin\n\n1\n\n88\n\n67%\n\n2.2\n\nOuistreham\n\nBasse-Normandie\n\n1\n\n90\n\n66.7%\n\n2.6\n\nBelfort\n\nFranche-Comte\n\n1\n\n59\n\n66.1%\n\n3.4\n\nAuxerre\n\nBourgogne\n\n1\n\n58\n\n65.5%\n\n2.2\n\nClermont Ferrandmulti-hotel\n\nAuvergne\n\n2\n\n29\n\n65.5%\n\n2.6\n\nNancy\n\nLorraine\n\n1\n\n89\n\n65.2%\n\n3.0\n\nTrouville-sur-mer\n\nBasse-Normandie\n\n1\n\n89\n\n65.2%\n\n2.7\n\nNiort\n\nPoitou-Charentes\n\n1\n\n88\n\n64.8%\n\n2.1\n\nEvreux\n\nHaute-Normandie\n\n1\n\n86\n\n64%\n\n2.1\n\nCaenmulti-hotel\n\nBasse-Normandie\n\n4\n\n57\n\n63.2%\n\n3.0\n\nChinon\n\nCentre\n\n1\n\n90\n\n62.2%\n\n2.9\n\nIle-Rousse\n\nCorse\n\n1\n\n89\n\n61.8%\n\n2.9\n\nSaint-brieuc\n\nBretagne\n\n1\n\n89\n\n61.8%\n\n2.9\n\nQuiberon\n\nBretagne\n\n1\n\n60\n\n61.7%\n\n2.7\n\nBastia\n\nCorse\n\n1\n\n88\n\n61.4%\n\n2.4\n\nNantesmulti-hotel\n\nPays-de-la-Loire\n\n3\n\n88\n\n61.4%\n\n2.4\n\nJuan-les-pins\n\nPACA\n\n1\n\n147\n\n61.2%\n\n3.2\n\nBourges\n\nCentre\n\n1\n\n87\n\n60.9%\n\n1.8\n\nBourg-en-bresse\n\nRhone-Alpes\n\n1\n\n87\n\n60.9%\n\n3.1\n\nSaint-Ouen-Sur-Seine\n\nIle-de-France\n\n1\n\n87\n\n60.9%\n\n3.0\n\nManosque\n\nPACA\n\n1\n\n88\n\n60.2%\n\n2.9\n\nMacon\n\nBourgogne\n\n1\n\n89\n\n59.6%\n\n2.3\n\nTroyes\n\nChampagne-Ardenne\n\n1\n\n86\n\n59.3%\n\n2.6\n\nSanary-Sur-mer\n\nPACA\n\n1\n\n175\n\n58.9%\n\n2.7\n\nCholet\n\nPays-de-la-Loire\n\n1\n\n90\n\n58.9%\n\n2.5\n\nFigeac\n\nMidi-Pyrenees\n\n1\n\n29\n\n58.6%\n\n2.9\n\nMulhousemulti-hotel\n\nAlsace\n\n2\n\n86\n\n58.1%\n\n3.2\n\nVannesmulti-hotel\n\nBretagne\n\n2\n\n88\n\n58%\n\n2.6\n\nPloermel\n\nBretagne\n\n1\n\n59\n\n57.6%\n\n2.3\n\nPlaisir\n\nIle-de-France\n\n1\n\n60\n\n56.7%\n\n2.6\n\nDunkerque\n\nNord-Pas-de-Calais\n\n1\n\n87\n\n56.3%\n\n3.2\n\nCastres\n\nMidi-Pyrenees\n\n1\n\n86\n\n55.8%\n\n3.0\n\nQuimper\n\nBretagne\n\n1\n\n90\n\n55.6%\n\n2.4\n\nAlbert\n\nPicardie\n\n1\n\n89\n\n55.1%\n\n2.6\n\nRouenmulti-hotel\n\nHaute-Normandie\n\n2\n\n89\n\n55.1%\n\n3.1\n\nSaint-nazaire\n\nPays-de-la-Loire\n\n1\n\n88\n\n54.5%\n\n2.8\n\nCoquelles\n\nNord-Pas-de-Calais\n\n1\n\n59\n\n54.2%\n\n3.6\n\nStrasbourgmulti-hotel\n\nAlsace\n\n4\n\n88\n\n53.4%\n\n3.0\n\nGrasse\n\nPACA\n\n1\n\n149\n\n53%\n\n2.7\n\nToulon\n\nPACA\n\n1\n\n119\n\n52.9%\n\n3.0\n\nOrange\n\nPACA\n\n1\n\n146\n\n52.1%\n\n3.1\n\nAvignonmulti-hotel\n\nPACA\n\n2\n\n118\n\n51.7%\n\n2.7\n\nLe Mans\n\nPays-de-la-Loire\n\n1\n\n89\n\n50.6%\n\n1.6\n\nRoyan\n\nPoitou-Charentes\n\n1\n\n89\n\n50.6%\n\n3.2\n\nMentonmulti-hotel\n\nPACA\n\n2\n\n149\n\n50.3%\n\n2.6\n\nFontvieille\n\nPACA\n\n1\n\n146\n\n49.3%\n\n2.0\n\nSaint-raphael\n\nPACA\n\n1\n\n145\n\n47.6%\n\n3.3\n\nCassismulti-hotel\n\nPACA\n\n2\n\n176\n\n47.2%\n\n2.9\n\nAix-les-bainsmulti-hotel\n\nRhone-Alpes\n\n2\n\n89\n\n46.1%\n\n3.4\n\nLattes\n\nLanguedoc-Roussillon\n\n1\n\n89\n\n46.1%\n\n3.2\n\nLourdes\n\nMidi-Pyrenees\n\n1\n\n86\n\n44.2%\n\n3.5\n\nLe Havremulti-hotel\n\nHaute-Normandie\n\n2\n\n87\n\n43.7%\n\n3.3\n\nNevers\n\nBourgogne\n\n1\n\n89\n\n42.7%\n\n3.3\n\nCalvi\n\nCorse\n\n1\n\n88\n\n39.8%\n\n3.5\n\nMetzmulti-hotel\n\nLorraine\n\n2\n\n89\n\n39.3%\n\n3.7\n\nLa Baulemulti-hotel\n\nPays-de-la-Loire\n\n2\n\n59\n\n39%\n\n3.8\n\nArcachon\n\nAquitaine\n\n1\n\n88\n\n38.6%\n\n4.0\n\nMontpelliermulti-hotel\n\nLanguedoc-Roussillon\n\n2\n\n58\n\n37.9%\n\n2.9\n\nHyeres\n\nPACA\n\n1\n\n175\n\n37.1%\n\n2.8\n\nPerros-guirec\n\nBretagne\n\n1\n\n89\n\n37.1%\n\n4.0\n\nSaint Etienne\n\nRhone-Alpes\n\n1\n\n90\n\n36.7%\n\n3.4\n\nPassymulti-hotel\n\nRhone-Alpes\n\n2\n\n89\n\n34.8%\n\n1.6\n\nBoulogne-billancourt\n\nIle-de-France\n\n1\n\n90\n\n34.4%\n\n3.8\n\nAsnieres-Sur-Seine\n\nIle-de-France\n\n1\n\n89\n\n33.7%\n\n3.3\n\nBonifacio\n\nCorse\n\n1\n\n89\n\n33.7%\n\n3.2\n\nPoitiers\n\nPoitou-Charentes\n\n1\n\n58\n\n32.8%\n\n3.2\n\nDinan\n\nBretagne\n\n1\n\n90\n\n32.2%\n\n3.0\n\nLa Rochellemulti-hotel\n\nPoitou-Charentes\n\n3\n\n88\n\n31.8%\n\n3.7\n\nSarlat-la-caneda\n\nAquitaine\n\n1\n\n88\n\n31.8%\n\n2.9\n\nAjaccio\n\nCorse\n\n1\n\n86\n\n31.4%\n\n3.1\n\nSalon De Provence\n\nPACA\n\n1\n\n147\n\n31.3%\n\n3.9\n\nLe Puy En Velay\n\nAuvergne\n\n1\n\n90\n\n31.1%\n\n3.6\n\nNicemulti-hotel\n\nPACA\n\n8\n\n171\n\n30.4%\n\n3.1\n\nAmboise\n\nCentre\n\n1\n\n88\n\n27.3%\n\n3.5\n\nRennesmulti-hotel\n\nBretagne\n\n3\n\n81\n\n27.2%\n\n2.7\n\nAntibesmulti-hotel\n\nPACA\n\n2\n\n146\n\n26%\n\n3.5\n\nSaumur\n\nPays-de-la-Loire\n\n1\n\n63\n\n25.4%\n\n3.6\n\nToursmulti-hotel\n\nCentre\n\n3\n\n59\n\n23.7%\n\n3.6\n\nPau\n\nAquitaine\n\n1\n\n87\n\n23%\n\n3.2\n\nBeaune\n\nBourgogne\n\n1\n\n88\n\n22.7%\n\n3.8\n\nIssy-les-moulineaux\n\nIle-de-France\n\n1\n\n88\n\n22.7%\n\n3.4\n\nLillemulti-hotel\n\nNord-Pas-de-Calais\n\n3\n\n89\n\n22.5%\n\n4.0\n\nToulouse\n\nMidi-Pyrenees\n\n1\n\n86\n\n20.9%\n\n2.8\n\nCannesmulti-hotel\n\nPACA\n\n4\n\n145\n\n20%\n\n4.1\n\nPuteaux\n\nIle-de-France\n\n1\n\n90\n\n20%\n\n3.9\n\nBiarritz\n\nAquitaine\n\n1\n\n86\n\n18.6%\n\n2.9\n\nBlois\n\nCentre\n\n1\n\n89\n\n18%\n\n3.8\n\nAngouleme\n\nPoitou-Charentes\n\n1\n\n57\n\n17.5%\n\n3.2\n\nAnnecymulti-hotel\n\nRhone-Alpes\n\n3\n\n88\n\n15.9%\n\n4.3\n\nColmar\n\nAlsace\n\n1\n\n87\n\n14.9%\n\n4.2\n\nNîmes\n\nLanguedoc-Roussillon\n\n1\n\n90\n\n14.4%\n\n4.1\n\nBordeauxmulti-hotel\n\nAquitaine\n\n2\n\n59\n\n11.9%\n\n3.6\n\nMerignac\n\nAquitaine\n\n1\n\n59\n\n10.2%\n\n2.3\n\nCourbevoie\n\nIle-de-France\n\n1\n\n60\n\n10%\n\n4.0\n\nParismulti-hotel\n\nIle-de-France\n\n26\n\n169\n\n7.7%\n\n3.9\n\nChartres\n\nCentre\n\n1\n\n89\n\n5.6%\n\n3.0\n\nMazan\n\nPACA\n\n1\n\n148\n\n5.4%\n\n2.6\n\nMougins\n\nPACA\n\n1\n\n150\n\n4.7%\n\n3.0\n\nBrest\n\nBretagne\n\n1\n\n59\n\n3.4%\n\n2.5\n\nDijon\n\nBourgogne\n\n1\n\n60\n\n3.3%\n\n3.0\n\nAix-en-Provence\n\nPACA\n\n1\n\n175\n\n2.9%\n\n6.0\n\nSaint-malo\n\nBretagne\n\n1\n\n85\n\n1.2%\n\n5.0\n\nMarseillemulti-hotel\n\nPACA\n\n2\n\n171\n\n0.6%\n\n3.0\n\nHonfleurmulti-hotel\n\nBasse-Normandie\n\n2\n\n87\n\n0%\n\n—\n\nLyonmulti-hotel\n\nRhone-Alpes\n\n4\n\n60\n\n0%\n\n—\n\nTop competitors named instead\n\n-   1Hôtel Le Nouveau Monde204\n-   2InterContinental Marseille - Hotel Dieu by IHG185\n-   3Golden Tulip Saint Malo– Le Grand Bé180\n-   4Hotel Le Pigonnet170\n-   5Hôtel Rotonde Aix-en-Provence centre169\n-   6Château de Mazan - Handwritten Collection168\n-   7Grand Hôtel Des Thermes168\n-   8Logis Hôtel Synaya162\n-   9Hôtel Le Siècle161\n-   10Le Negresco Nice155\n\nFindings\n\n## Single-hotel cities vs. multi-hotel cities: a 28-point gap\n\nCounterintuitive at first, but it makes sense once you separate “is there a hotel worth recommending” from “is it a Best Western”: multi-property cities are disproportionately the bigger, more competitive markets (Paris, Nice, Marseille, Cannes) where AI answers have many strong non-BW options to choose from. In a single-hotel small/mid-size city, the local BW property is often simply the most prominent hotel search-indexed for that town.\n\nBest Western organic mention rate, single- vs multi-hotel cities\n\nScope\n\nMention rate\n\nHits / captures\n\nSingle-hotel cities (1 BW property)\n\n69.2%\n\n12,608 / 18,210\n\nMulti-hotel cities (2+ BW properties)\n\n41.4%\n\n1,415 / 3,414\n\n### By query framing (archetype)\n\nComparison and open-ended framings surface Best Western the most; transactional and star/budget-tier framings the least.\n\nMention rate by query archetype\n\nArchetype\n\nMention rate\n\ncomparison (\"compare the top hotels in X\")\n\n79.2%\n\noccasion (\"hotel in X for a weekend break\")\n\n74.4%\n\namenity\n\n71.1%\n\nopen (\"where to stay in X\")\n\n71%\n\nnear (train station / city center)\n\n68.8%\n\npersona\n\n68.3%\n\ncontrol (\"best hotel in X\")\n\n67.6%\n\ntransactional (book/cheapest/best value)\n\n52.6%\n\nstar\\_tier\n\n52.1%\n\n## Star/budget tier and persona: where the brand nearly disappears\n\nBest Western France’s real inventory clusters at 3-4 star. “Luxury hotel in X” scoring higher than “3-star” is a real, if mildly counterintuitive, AI-behavior finding: a small/mid-size town often has no genuine luxury option, and the AI falls back to the most prominent hotel it has — frequently the local Best Western. “Cheap hotel” queries, by contrast, pull in budget chains (Ibis Budget, Premiere Classe, F1) that undercut Best Western on price positioning even in towns where it’s the only real full-service option.\n\nMention rate by star/budget tier\n\nTier\n\nMention rate\n\nbudget (\"cheap hotel in X\")\n\n33.8%\n\n3-star\n\n54%\n\nluxury\n\n55.1%\n\n4-star\n\n64.9%\n\n### Persona: business travelers see it far more than couples\n\nConsistent with the methodology section’s finding from Best Western France’s own press data (Corporate revenue 15x Leisure) — the AI models’ associative sense of the brand skews business/roadside too, not just the real booking mix.\n\nMention rate by traveler persona\n\nPersona\n\nMention rate\n\nbusiness\\_traveler\n\n80.7%\n\nsolo\\_leisure\n\n69.1%\n\nfamily\n\n67.7%\n\nroad\\_trip\\_overnight\n\n67.6%\n\ncouple\n\n56.2%\n\nBudget framing is the single weakest spot (33.8%) despite Best Western having real budget-adjacent inventory — the brand is losing “cheap hotel” mindshare to dedicated budget chains even in towns where it’s the only full-service option.\n\n## French vs. English (Paris + PACA only): language matters less than expected\n\nA real but modest gap — French-language, FR-proxy queries surface Best Western somewhat more often in the international-clientele cities, consistent with the brand being better-known to the domestic search behavior the methodology identified. But the gap is far smaller than the corporate-vs-leisure revenue skew (15x) would predict: foreign tourists searching in English from a US IP still see Best Western about half the time in Paris/PACA — the brand isn’t invisible to that segment, just modestly less visible.\n\nMention rate by language/proxy, Paris + PACA cities only\n\nLanguage / proxy\n\nMention rate\n\nHits / captures\n\nFrench / FR proxy\n\n55.7%\n\n1,447 / 2,598\n\nEnglish / US proxy\n\n50.1%\n\n1,371 / 2,735\n\n## Soft-brand recognition: the flagship name dominates\n\n85% of the time an AI answer names a Best Western property, it uses a form close enough to the flagship “Best Western \\[Plus/Premier/Signature\\] …” naming to match exactly. Soft-brand properties (Sure Hotel, BW Premier/Signature Collection) are recognized far less reliably — even after fixing the resolver’s brand-token list, ~9% of hits still needed the fuzzy/alias/review-flagged paths rather than an exact match.\n\nNaming match tier, share of all Best Western hits\n\nMatch tier\n\nShare of hits\n\ntier\\_1\\_exact (exact \"Best Western ...\" name match)\n\n85.2%\n\ntier\\_fuzzy\\_same\\_city\n\n5.6%\n\ntier\\_fuzzy\\_cross\\_city (city mismatch, needs review)\n\n5.2%\n\nbrand\\_token\\_only (soft-brand token, no registry match)\n\n3.1%\n\nalias (hand-curated bare soft-brand name)\n\n0.8%\n\nSoft-brand naming is a real visibility tax: these properties get recognized as Best Western far less reliably than flagship-named ones, both by this study’s own resolver and, potentially, by the AI models themselves conflating the property’s identity. Best Western France’s own naming conventions may be actively working against AI discoverability for these specific properties.\n\nZero-mention cities\n\n## Two cities where Best Western never showed up\n\nTwo cities with 20+ captures never surfaced a Best Western property at all: **Honfleur** (87 captures) and **Lyon** (60 captures, spot-checked — real competitors named instead: InterContinental Lyon–Hôtel Dieu, Carlton Lyon MGallery, Pullman Lyon, Cour des Loges Radisson Collection, several boutique Vieux-Lyon properties). Lyon has 8 Best Western-affiliated hotels in the reconciled directory — the AI’s mental model of “best hotel in Lyon” runs entirely through upscale/boutique/historic properties in Vieux Lyon and Presqu’île, a segment Best Western’s Lyon portfolio doesn’t compete in.\n\n## Top competitors named instead\n\nWhen Best Western is absent, a mix of independent boutique properties, other franchise chains (IHG, Louvre Hotels/Golden Tulip, Accor MGallery/Sofitel), and — notably — full sibling-brand competitors in the exact same “3-4 star provincial hotel” positioning Best Western occupies (Logis, The Originals) take its place.\n\nTop competitors named when Best Western is absent\n\nRank\n\nCompetitor\n\nMentions\n\n1\n\nHôtel Le Nouveau Monde\n\n204\n\n2\n\nInterContinental Marseille - Hotel Dieu by IHG\n\n185\n\n3\n\nGolden Tulip Saint Malo– Le Grand Bé\n\n180\n\n4\n\nHotel Le Pigonnet\n\n170\n\n5\n\nHôtel Rotonde Aix-en-Provence centre\n\n169\n\n6\n\nChâteau de Mazan - Handwritten Collection\n\n168\n\n7\n\nGrand Hôtel Des Thermes\n\n168\n\n8\n\nLogis Hôtel Synaya\n\n162\n\n9\n\nHôtel Le Siècle\n\n161\n\n10\n\nLe Negresco Nice\n\n155\n\n## What this means\n\n-   **The biggest AI-visibility gap is in multi-property, competitive cities** — Paris, Nice, Marseille, Cannes, Lyon — not the small-town roadside market where the brand already dominates organically. Any AI-visibility investment should prioritize these specific markets rather than the network broadly.\n-   **Budget-framed queries are the weakest spot** (33.8%) despite Best Western having real budget-adjacent inventory — the brand is losing “cheap hotel” mindshare to dedicated budget chains even in towns where it’s the only full-service option.\n-   **Soft-brand naming is a real visibility tax** — Sure Hotel and BW Premier/Signature Collection properties get recognized as Best Western far less reliably than flagship-named ones. Best Western France’s own naming conventions may be actively working against AI discoverability for these properties.\n-   **The French/English gap (55.7% vs 50.1%) is smaller than expected** given the network’s business-travel skew — international leisure travelers searching in English aren’t being systematically excluded, just modestly underserved relative to the domestic market.\n\n## FAQ\n\nYes, organically and often: across 21,624 unbranded-prompt captures with a resolved hotel mention, **64.8%** (14,023) mention a Best Western property even though the query never named the brand. When it appears, its average best rank is 2.12, and it is the AI’s #1 recommendation 46.7% of the time.\n\n**Disclosure:** this is an independent, third-party AI-visibility study. Nicolas Sitter has no affiliation with Best Western, its France operating company, or any hotel named in this piece.\n\n### Summarize with AI\n\n### More hotel-brand and AI-search research\n\n[AI Cross-Platform Consensus 2026](/research/ai-cross-platform-consensus-2026)[AI Hotel Volatility 2026](/research/ai-hotel-volatility-2026)[Best Western France — data feed (JSON/CSV)](/api/landscape/best-western-france)[All research](/research)","author":{"@type":"Person","name":"Nicolas Sitter","url":"https://nicolassitter.com/about","sameAs":["https://www.linkedin.com/in/nicolassitternolleau/","https://github.com/Nicositter88","https://hotelrank.ai"]},"publisher":{"@type":"Person","name":"Nicolas Sitter","url":"https://nicolassitter.com"},"image":"https://nicolassitter.com/api/og/best-western-france-ai-visibility-2026","mainEntityOfPage":{"@type":"WebPage","@id":"https://nicolassitter.com/research/best-western-france-ai-visibility-2026"},"tags":["AI Search","Hotels","Best Western","Brand Visibility","GEO"],"sameAs":["https://hotelrank.ai/research/best-western-france-ai-visibility-2026"],"alternateFormat":{"html":"https://nicolassitter.com/research/best-western-france-ai-visibility-2026","json":"https://nicolassitter.com/api/post/best-western-france-ai-visibility-2026","rss":"https://nicolassitter.com/rss.xml"},"datasets":[{"name":"summary","contentUrl":"https://nicolassitter.com/data/best-western-france-ai-visibility-2026/summary.csv","encodingFormat":"text/csv"}]}