{"id":92517,"date":"2026-04-29T11:38:07","date_gmt":"2026-04-29T09:38:07","guid":{"rendered":"https:\/\/www.aleydasolis.com\/?p=92517"},"modified":"2026-04-29T11:38:07","modified_gmt":"2026-04-29T09:38:07","slug":"global-ai-search-strategy","status":"publish","type":"post","link":"https:\/\/www.aleydasolis.com\/en\/ai-search\/global-ai-search-strategy\/","title":{"rendered":"Where AI Search Sends Traffic: 10-Market Patterns for Your Global AI Search Strategy"},"content":{"rendered":"

There’s a comfortable narrative around AI search right now: AI assistants surface the biggest brands, ecommerce concentrates around marketplaces, finance flows to trusted institutions, travel goes to the major OTAs, and the playbook is to “be a recognized brand.”<\/span><\/p>\n

That story is partly true. But after going through the top click-producing AI search domains across ecommerce, finance, and travel & tourism in 10 international markets – the USA, UK, Spain, France, Germany, Italy, the Netherlands, Australia, Mexico, and Brazil – in March 2026, using Similarweb<\/a> data covering roughly 87.6M visits across 57,696 domain-market entries, what stands out isn\u2019t how the picture confirms that narrative. It\u2019s how often it doesn\u2019t.<\/span><\/p>\n

The most useful pattern I can pull from this data is that AI search isn’t one thing. The shape of the click-receiving layer changes radically by vertical and by country, and so does the strategy that actually works inside it.<\/b><\/p>\n

Before I go through what the data shows, one important caveat that should sit underneath everything below.<\/span><\/p>\n

What this data does and doesn’t show<\/b><\/h2>\n

These datasets capture click-producing AI search traffic: visits that landed on a domain after a user clicked a citation or link from an AI-generated answer. It doesn’t measure brand mentions or citations that didn’t generate a click, sentiment, or recommendation strength.<\/span><\/p>\n

The analysis is based on third-party AI search referral traffic estimates for March 2026 across ecommerce, finance, and travel & tourism across ten markets (USA, UK, Spain, France, Germany, Italy, Netherlands, Australia, Mexico and Brazil) from Similarweb<\/a>.<\/span><\/p>\n

It also blends across AI surfaces that behave very differently \u2014 ChatGPT, Perplexity, Google AI Mode, and other AI search experiences don’t feature links and generate clicks in the same way. So when I say a domain “wins” AI search in a market, I mean it wins within the measurable click-producing layer. That’s an important slice of the picture, but not the whole one.<\/span><\/p>\n

One methodological note: the domain counts below are domain-market entries, not necessarily unique domains. A domain can appear in more than one market or vertical. The industry categories are also based on the classification available in the dataset, so broad buckets such as generic finance or generic travel should be read as broad category groupings rather than precise user-intent labels.<\/span><\/p>\n

The dataset overall:<\/span><\/p>\n

\n\n\n\n\n\n\n\n\n
Vertical<\/b><\/td>\nTotal AI clicks (10 markets)<\/b><\/td>\nDomain-market entries in dataset<\/b><\/td>\n<\/tr>\n<\/thead>\n
Ecommerce<\/span><\/td>\n39.3M<\/span><\/td>\n14,454<\/span><\/td>\n<\/tr>\n
Finance<\/span><\/td>\n30.7M<\/span><\/td>\n22,313<\/span><\/td>\n<\/tr>\n
Travel & tourism<\/span><\/td>\n17.6M<\/span><\/td>\n20,929<\/span><\/td>\n<\/tr>\n
Total<\/b><\/td>\n87.6M<\/b><\/td>\n57,696<\/b><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

\u00a0<\/span><\/p>\n

With that on the table, here’s what the data actually shows.<\/span><\/p>\n

Pattern 1: AI search concentration is not the same across verticals, it’s an order of magnitude different<\/b><\/h2>\n

The most useful number here isn’t top 10 share. It’s how few domains it takes to capture the first 50% of clicks in a vertical.<\/span><\/p>\n

\"\"<\/p>\n

Figure 1. Vertical concentration gradient based on domains needed to capture 50% and 99% of AI search clicks in each vertical.<\/span><\/em><\/p>\n

\"\"<\/span><\/p>\n

Figure 1B. Market-level concentration: domains needed to reach 50% of AI clicks by vertical.<\/span><\/p>\n

 <\/p>\n

\n\n\n\n\n\n\n\n
Vertical<\/b><\/td>\nMedian domains for 50%<\/b><\/td>\nMedian for 99%<\/b><\/td>\nAvg #1 share<\/b><\/td>\nAvg top 10 share<\/b><\/td>\n<\/tr>\n<\/thead>\n
Ecommerce<\/span><\/td>\n5<\/b><\/td>\n650<\/span><\/td>\n26.9%<\/span><\/td>\n63.9%<\/span><\/td>\n<\/tr>\n
Finance<\/span><\/td>\n17<\/b><\/td>\n763<\/span><\/td>\n18.1%<\/span><\/td>\n44.2%<\/span><\/td>\n<\/tr>\n
Travel & tourism<\/span><\/td>\n47<\/b><\/td>\n1,077<\/span><\/td>\n6.9%<\/span><\/td>\n30.0%<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

\u00a0<\/span><\/p>\n

In Italian ecommerce, Amazon.it alone captures 46.2% of all AI ecommerce clicks, and adding Temu pushes it past half. In US ecommerce, three domains capture 50%. In UK travel, you need 129. That\u2019s not “ecommerce is more concentrated than travel.” That\u2019s a structural difference.<\/span><\/p>\n

The same shows up at every market level. Here are the domain counts needed for 50% of clicks per market and vertical:<\/span><\/p>\n

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Market<\/b><\/td>\nEcommerce<\/b><\/td>\nFinance<\/b><\/td>\nTravel<\/b><\/td>\n<\/tr>\n<\/thead>\n
USA<\/span><\/td>\n3<\/span><\/td>\n26<\/span><\/td>\n70<\/span><\/td>\n<\/tr>\n
UK<\/span><\/td>\n6<\/span><\/td>\n57<\/span><\/td>\n129<\/span><\/td>\n<\/tr>\n
Spain<\/span><\/td>\n7<\/span><\/td>\n19<\/span><\/td>\n47<\/span><\/td>\n<\/tr>\n
France<\/span><\/td>\n5<\/span><\/td>\n9<\/span><\/td>\n80<\/span><\/td>\n<\/tr>\n
Germany<\/span><\/td>\n4<\/span><\/td>\n16<\/span><\/td>\n60<\/span><\/td>\n<\/tr>\n
Italy<\/span><\/td>\n2<\/span><\/td>\n13<\/span><\/td>\n32<\/span><\/td>\n<\/tr>\n
Netherlands<\/span><\/td>\n11<\/span><\/td>\n8<\/span><\/td>\n46<\/span><\/td>\n<\/tr>\n
Australia<\/span><\/td>\n9<\/span><\/td>\n26<\/span><\/td>\n32<\/span><\/td>\n<\/tr>\n
Mexico<\/span><\/td>\n5<\/span><\/td>\n8<\/span><\/td>\n11<\/span><\/td>\n<\/tr>\n
Brazil<\/span><\/td>\n3<\/span><\/td>\n18<\/span><\/td>\n13<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

\u00a0<\/span><\/p>\n

The ecommerce column never exceeds 11. The travel column ranges from 11 to 129. Finance sits between them in nearly every market.<\/span><\/p>\n

This matters because it changes what AI search optimization actually looks like by vertical:<\/span><\/p>\n

In ecommerce, AI traffic strategy is largely marketplace strategy.<\/b><\/p>\n

The head is highly concentrated and, in many markets, very difficult to displace. The realistic question for most brands is not “how do I win the head” but “how do I show up correctly inside the marketplaces and resellers that own it.”<\/span><\/p>\n

In finance, there are clearer leaders, but there’s still meaningful room beneath them, and those leaders change materially by country.<\/b><\/p>\n

PayPal leads finance in Germany and Italy. Stripe leads in markets where the click mix appears to include a stronger payment-infrastructure, merchant, or developer component. Local banks hold strong positions in Brazil, Australia, and the Netherlands.<\/span><\/p>\n

In travel, the head is much more open.<\/b><\/p>\n

No single domain owns the user journey, which means specialist, local, and route-specific players still have a real path to visibility.<\/span><\/p>\n

Pattern 2: AI search traffic often go to local infrastructure, not global defaults<\/b><\/h2>\n

This is the pattern that surprised me most when I went through the data, and it’s the one I’d most like SEOs working internationally to internalize.<\/span><\/p>\n

In most non-US markets, locally trusted domains, classifieds, public-sector entities, and infrastructure operators are beating, or competing seriously with, the global defaults at the head.<\/span><\/p>\n

\"\"<\/p>\n

Figure 2. Examples where the leading AI-click domain is not the expected global default, based on the top domain by market and vertical.<\/span><\/em><\/p>\n

Here are the cases where what you’d expect to lead doesn’t, ranked roughly by how much they break the assumption:<\/span><\/p>\n

 <\/p>\n

\n\n\n\n\n\n\n\n\n\n\n\n\n
Market<\/b><\/td>\nVertical<\/b><\/td>\nExpected leader<\/b><\/td>\nActually leads<\/b><\/td>\n#1 share<\/b><\/td>\n<\/tr>\n<\/thead>\n
Netherlands<\/span><\/td>\nEcommerce<\/span><\/td>\namazon.nl<\/span><\/td>\nbol.com<\/b><\/td>\n17.9%<\/span><\/td>\n<\/tr>\n
Brazil<\/span><\/td>\nEcommerce<\/span><\/td>\namazon.com.br<\/span><\/td>\nmercadolivre.com.br<\/b><\/td>\n20.7%<\/span><\/td>\n<\/tr>\n
Germany<\/span><\/td>\nTravel<\/span><\/td>\nbooking.com<\/span><\/td>\nbahn.de<\/b><\/td>\n6.4%<\/span><\/td>\n<\/tr>\n
Italy<\/span><\/td>\nTravel<\/span><\/td>\nbooking.com<\/span><\/td>\nlefrecce.it<\/b><\/td>\n11.3%<\/span><\/td>\n<\/tr>\n
Germany<\/span><\/td>\nFinance<\/span><\/td>\nstripe.com<\/span><\/td>\npaypal.com<\/b><\/td>\n18.2%<\/span><\/td>\n<\/tr>\n
Italy<\/span><\/td>\nFinance<\/span><\/td>\nstripe.com<\/span><\/td>\npaypal.com<\/b><\/td>\n13.4%<\/span><\/td>\n<\/tr>\n
USA<\/span><\/td>\nTravel<\/span><\/td>\nbooking.com<\/span><\/td>\nexpedia.com<\/b><\/td>\n3.5%<\/span><\/td>\n<\/tr>\n
Australia<\/span><\/td>\nTravel<\/span><\/td>\nbooking.com<\/span><\/td>\ntravelandtourworld.com<\/b><\/td>\n9.7%<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

\u00a0<\/span><\/p>\n

And once you go beyond #1, the pattern intensifies. Local infrastructure carries serious weight in the second tier of nearly every non-US market:<\/span><\/p>\n

\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Market<\/b><\/td>\nNotable local players in second tier (across verticals)<\/b><\/td>\n<\/tr>\n<\/thead>\n
Spain<\/span><\/td>\nwallapop.com, chollometro.com, renfe.com, milanuncios.com, jcyl.es, barcelona.cat, civitatis.com<\/span><\/td>\n<\/tr>\n
Germany<\/span><\/td>\nkleinanzeigen.de, idealo.de, geizhals.de, mydealz.de, check24.de, handelsblatt.com, fewo-direkt.de, muenchen.de, hamburg.de<\/span><\/td>\n<\/tr>\n
France<\/span><\/td>\nleboncoin.fr, cdiscount.com, fnac.com, boursorama.com, ameli.fr, caf.fr, paris.fr, sncf-connect.com, iledefrance-mobilites.fr<\/span><\/td>\n<\/tr>\n
Netherlands<\/span><\/td>\nmarktplaats.nl, coolblue.nl, tweakers.net, hema.nl, belastingdienst.nl, abnamro.nl, ing.nl, ns.nl, amsterdam.nl, 9292.nl<\/span><\/td>\n<\/tr>\n
Italy<\/span><\/td>\nsubito.it, trovaprezzi.it, idealo.it, poste.it, ilsole24ore.com, trenitalia.com<\/span><\/td>\n<\/tr>\n
Brazil<\/span><\/td>\nshopee.com.br, magazineluiza.com.br, olx.com.br, casasbahia.com.br, caixa.gov.br, serasa.com.br, nubank.com.br, latamairlines.com, voeazul.com.br, 99app.com<\/span><\/td>\n<\/tr>\n
Mexico<\/span><\/td>\nliverpool.com.mx, walmart.com.mx, bodegaaurrera.com.mx, promodescuentos.com, bbva.mx, mercadopago.com.mx, volaris.com, vivaaerobus.com, aeromexico.com<\/span><\/td>\n<\/tr>\n
Australia<\/span><\/td>\nbigw.com.au, kmart.com.au, ozbargain.com.au, myer.com.au, commbank.com.au, westpac.com.au, qantas.com, velocityfrequentflyer.com<\/span><\/td>\n<\/tr>\n
UK<\/span><\/td>\nargos.co.uk, johnlewis.com, currys.co.uk, boots.com, gumtree.com, moneysavingexpert.com, tfl.gov.uk<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

\u00a0<\/span><\/p>\n

The pattern doesn’t appear to be explained by translation quality alone. It’s that those local domains often hold the structured inventory, operational data, or institutional trust that AI systems can rely on to answer the task in that market.<\/span>\u00a0For “Milan to Rome by train”, Lefrecce holds the timetables and routes. Booking.com doesn’t. <\/span>For “best price in the Netherlands”, Bol.com has the local inventory and pricing. For “tax obligations as a freelancer in Spain”, the Spanish public-sector sites hold the answer no commercial intermediary can match.<\/span><\/p>\n

The strategic implication: your global competitor set is not your AI search competitor set in each market.<\/b><\/p>\n

In Italian travel, your real competitor for rail intent isn’t Booking, it’s Trenitalia. In Dutch ecommerce, it’s not Amazon, it’s Bol.com. In French finance, it might be Boursorama or even Ameli. <\/span>International SEO frameworks built around global authority don’t capture this cleanly.<\/span><\/p>\n

Pattern 3: “AI search is growing” is true, but masks meaningful churn<\/b><\/h2>\n

A common reading of month-over-month data is that AI search is in a uniform growth phase. The medians can make it look that way at first:<\/span><\/p>\n

\n\n\n\n\n\n\n\n
Vertical<\/b><\/td>\nAvg median MoM (top 50)<\/b><\/td>\nAvg weighted MoM (top 50)<\/b><\/td>\n<\/tr>\n<\/thead>\n
Ecommerce<\/span><\/td>\n+20.0%<\/span><\/td>\n+49.1%<\/span><\/td>\n<\/tr>\n
Finance<\/span><\/td>\n+25.0%<\/span><\/td>\n+72.7%<\/span><\/td>\n<\/tr>\n
Travel<\/span><\/td>\n+29.1%<\/span><\/td>\n+101.1%<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

\u00a0<\/span><\/p>\n

But the medians hide an important reality. In market after market, 30\u201340% of the top 50 domains are declining month-over-month, not growing.<\/span><\/p>\n

\"\"<\/p>\n

Figure 3. Declining domains among each market\/vertical top 50, showing churn beneath aggregate AI search growth.<\/span><\/p>\n

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Market<\/b><\/td>\nEcommerce decliners<\/b><\/td>\nFinance decliners<\/b><\/td>\nTravel decliners<\/b><\/td>\n<\/tr>\n<\/thead>\n
USA<\/span><\/td>\n9 of 50 (18%)<\/span><\/td>\n10 of 50 (20%)<\/span><\/td>\n11 of 48 (23%)<\/span><\/td>\n<\/tr>\n
UK<\/span><\/td>\n16 of 50 (32%)<\/span><\/td>\n13 of 50 (26%)<\/span><\/td>\n19 of 49 (39%)<\/span><\/td>\n<\/tr>\n
Spain<\/span><\/td>\n21 of 49 (43%)<\/span><\/td>\n17 of 50 (34%)<\/span><\/td>\n17 of 50 (34%)<\/span><\/td>\n<\/tr>\n
France<\/span><\/td>\n17 of 50 (34%)<\/span><\/td>\n22 of 50 (44%)<\/span><\/td>\n18 of 50 (36%)<\/span><\/td>\n<\/tr>\n
Germany<\/span><\/td>\n20 of 50 (40%)<\/span><\/td>\n16 of 50 (32%)<\/span><\/td>\n14 of 50 (28%)<\/span><\/td>\n<\/tr>\n
Italy<\/span><\/td>\n14 of 50 (28%)<\/span><\/td>\n5 of 50 (10%)<\/span><\/td>\n15 of 49 (31%)<\/span><\/td>\n<\/tr>\n
Netherlands<\/span><\/td>\n15 of 50 (30%)<\/span><\/td>\n11 of 50 (22%)<\/span><\/td>\n11 of 44 (25%)<\/span><\/td>\n<\/tr>\n
Australia<\/span><\/td>\n18 of 50 (36%)<\/span><\/td>\n17 of 50 (34%)<\/span><\/td>\n18 of 50 (36%)<\/span><\/td>\n<\/tr>\n
Mexico<\/span><\/td>\n15 of 49 (31%)<\/span><\/td>\n10 of 47 (21%)<\/span><\/td>\n11 of 48 (23%)<\/span><\/td>\n<\/tr>\n
Brazil<\/span><\/td>\n13 of 49 (27%)<\/span><\/td>\n9 of 50 (18%)<\/span><\/td>\n12 of 49 (24%)<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

\u00a0<\/span><\/p>\n

The weighted averages are also distorted by individual outliers, a few domains growing +800% or +4,500% from a small base inflate the mean without reflecting what most domains are actually experiencing. Some examples worth looking at carefully:<\/span><\/p>\n

\n\n\n\n\n\n\n\n\n\n
Market<\/b><\/td>\nDomain<\/b><\/td>\nMoM change<\/b><\/td>\nWhat to question<\/b><\/td>\n<\/tr>\n<\/thead>\n
Brazil<\/span><\/td>\nazulviagens.com.br<\/span><\/td>\n+4,506%<\/span><\/td>\nSingle-month spike, small previous base<\/span><\/td>\n<\/tr>\n
Mexico<\/span><\/td>\ninnovasport.com<\/span><\/td>\n+9,019%<\/span><\/td>\nLikely seasonal sports retail spike<\/span><\/td>\n<\/tr>\n
Italy<\/span><\/td>\njoom.com<\/span><\/td>\n+778%<\/span><\/td>\nCross-border marketplace \u2014 could be platform-side change<\/span><\/td>\n<\/tr>\n
Spain<\/span><\/td>\nflightaware.com<\/span><\/td>\n+2,464%<\/span><\/td>\nNiche tracker, low absolute base<\/span><\/td>\n<\/tr>\n
Australia<\/span><\/td>\nqueensland.com<\/span><\/td>\n+2,267%<\/span><\/td>\nSeasonal\/destination intent shift<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

\u00a0<\/span><\/p>\n

These movers are interesting and worth investigating, but a +4,500% single-month change on a small base doesn’t establish a structural trend. It establishes an investigation lead.<\/span><\/p>\n

So the more accurate framing isn’t “AI search is growing.” It’s:<\/span><\/p>\n

AI search is changing who receives traffic. The ecosystem of traffic receiving domains is expanding, but there is significant churn inside it. Current winners are not automatically safe, and baseline growth at the head is usually much more modest than the long-tail outliers suggest.<\/b><\/p>\n

That changes the strategic implication. Position snapshots on their own aren’t enough. <\/span>Momentum tracking matters more than position.<\/b> The brands that need urgent attention are not the ones at #50 today, they’re the ones at #15 with a -25% MoM trend, because that’s where the position is actually being lost.<\/span><\/p>\n

Pattern 4: “Trust-led finance” is real, but the headline is misread<\/b><\/h2>\n

Stripe ranks #1 in seven of ten finance markets in this dataset. That’s a real and important pattern, but it’s easy to misread it as “AI search rewards consumer trust in financial brands.”<\/span><\/p>\n

A more careful reading is that Stripe’s prominence likely reflects B2B, developer, merchant, and payment-infrastructure demand rather than purely consumer financial decision making. The domain-level data does not let us fully separate those intents, so this should not be read as a simple consumer trust signal.<\/span><\/p>\n

Here’s where Stripe and PayPal sit across all 10 finance markets:<\/span><\/p>\n

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Market<\/b><\/td>\n#1 finance domain<\/b><\/td>\n#1 share<\/b><\/td>\nStripe rank<\/b><\/td>\nPayPal rank<\/b><\/td>\n<\/tr>\n<\/thead>\n
USA<\/span><\/td>\nciti.com<\/span><\/td>\n11.9%<\/span><\/td>\n#2<\/span><\/td>\n#5<\/span><\/td>\n<\/tr>\n
UK<\/span><\/td>\nstripe.com<\/span><\/td>\n13.9%<\/span><\/td>\n#1<\/span><\/td>\n#2<\/span><\/td>\n<\/tr>\n
Spain<\/span><\/td>\nstripe.com<\/span><\/td>\n15.4%<\/span><\/td>\n#1<\/span><\/td>\n#2<\/span><\/td>\n<\/tr>\n
France<\/span><\/td>\nstripe.com<\/span><\/td>\n17.6%<\/span><\/td>\n#1<\/span><\/td>\n#2<\/span><\/td>\n<\/tr>\n
Germany<\/span><\/td>\npaypal.com<\/span><\/td>\n18.2%<\/span><\/td>\n#2<\/span><\/td>\n#1<\/span><\/td>\n<\/tr>\n
Italy<\/span><\/td>\npaypal.com<\/span><\/td>\n13.4%<\/span><\/td>\n#2<\/span><\/td>\n#1<\/span><\/td>\n<\/tr>\n
Netherlands<\/span><\/td>\nstripe.com<\/span><\/td>\n26.8%<\/span><\/td>\n#1<\/span><\/td>\n#3<\/span><\/td>\n<\/tr>\n
Australia<\/span><\/td>\nstripe.com<\/span><\/td>\n16.9%<\/span><\/td>\n#1<\/span><\/td>\n#9<\/span><\/td>\n<\/tr>\n
Mexico<\/span><\/td>\nstripe.com<\/span><\/td>\n29.1%<\/span><\/td>\n#1<\/span><\/td>\n#14<\/span><\/td>\n<\/tr>\n
Brazil<\/span><\/td>\nstripe.com<\/span><\/td>\n17.5%<\/span><\/td>\n#1<\/span><\/td>\n#41<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

\u00a0<\/span><\/p>\n

The “Stripe wins finance” headline is technically correct but too broad. PayPal, which is more consumer-facing, leads in Germany and Italy, while Stripe’s leadership in several other markets likely reflects the weight of payment-infrastructure, merchant, and developer oriented demand inside the finance dataset, not just consumer finance behavior.<\/span><\/p>\n

Once you separate B2B infrastructure from consumer finance, the pattern becomes clearer:<\/span><\/p>\n

\"\"<\/p>\n

Figure 4. Finance sub-category breakdown by share of AI search clicks across the 10 markets.<\/span><\/em><\/p>\n

\n\n\n\n\n\n\n\n\n\n\n
Sub-category<\/b><\/td>\nShare of finance clicks<\/b><\/td>\n<\/tr>\n<\/thead>\n
Generic \/ broad finance<\/span><\/td>\n30.7%<\/span><\/td>\n<\/tr>\n
Banking, credit & lending<\/span><\/td>\n25.1%<\/span><\/td>\n<\/tr>\n
Investing<\/span><\/td>\n22.4%<\/span><\/td>\n<\/tr>\n
Financial planning & management<\/span><\/td>\n11.9%<\/span><\/td>\n<\/tr>\n
Insurance<\/span><\/td>\n7.2%<\/span><\/td>\n<\/tr>\n
Accounting & auditing<\/td>\n2.7%<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

\u00a0<\/span><\/p>\n

One of the most interesting findings in the entire dataset is buried in this breakdown: investing at 22.4% of finance clicks. <\/span><\/p>\n

AI platforms are sending substantial click volume to investing tools, charts, broker platforms, and market-data environments. <\/span><\/p>\n

A reasonable reading is that users may be relying on AI for market explainers and lookups, then clicking through when they need charts, prices, broker environments, or deeper market data.<\/span><\/p>\n

Notable investing\/market-data players in the dataset include TradingView (top 20 across all 10 markets), Investing.com (top 20 in 9 markets), Schwab and Fidelity (US), DEGIRO (Netherlands), Boursorama (France), Finecobank (Italy), and the broker platforms inside Mexico and Brazil. <\/span><\/p>\n

For investing platforms, AI search is already a meaningful click producing discovery channel, and one of the finance categories where the click-through is high enough to matter.<\/b><\/p>\n

Pattern 5: Travel is fragmented at the discovery layer, but more concentrated at the booking and infrastructure layer<\/b><\/h2>\n

The headline that “travel is fragmented” is right at the aggregate level, but it hides a useful split inside the vertical.<\/span><\/p>\n

\"\"<\/p>\n

Figure 5. Travel sub-category breakdown by share of AI search clicks across the 10 markets.<\/span><\/em><\/p>\n

\n\n\n\n\n\n\n\n\n\n\n\n
Sub-category<\/b><\/td>\nShare of travel clicks<\/b><\/td>\n<\/tr>\n<\/thead>\n
Generic travel content<\/span><\/td>\n44.3%<\/span><\/td>\n<\/tr>\n
Accommodation & hotels<\/span><\/td>\n26.6%<\/span><\/td>\n<\/tr>\n
Air travel<\/span><\/td>\n16.3%<\/span><\/td>\n<\/tr>\n
Tourist attractions<\/span><\/td>\n3.8%<\/span><\/td>\n<\/tr>\n
Ground transportation<\/span><\/td>\n3.6%<\/span><\/td>\n<\/tr>\n
Car rentals<\/span><\/td>\n3.5%<\/span><\/td>\n<\/tr>\n
Other \/ unclassified travel<\/span><\/td>\n1.8%<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

\u00a0<\/span><\/p>\n

The 44.3% “generic travel” bucket does most of the fragmentation work: it’s destination guides, travel publishers, and broader tourism content. Once you strip it out, the underlying booking and infrastructure patterns become much clearer.<\/span><\/p>\n

Accommodation is more concentrated than the headline numbers suggest.<\/b><\/p>\n

Booking, Airbnb, Expedia, and a small number of hotel chains carry most of it. Booking.com appears in the top 20 across all 10 travel markets and is #1 in five (UK, Spain, France, Netherlands, Brazil).<\/span><\/p>\n

Air travel is dominated by airlines and a handful of metasearch sites.<\/b><\/p>\n

Skyscanner appears across nearly every market. National airlines carry significant share where they have route concentration; Qantas in Australia, Volaris\/Viva Aerobus\/Aeromexico in Mexico (three airlines in the top 5), LATAM\/Azul in Brazil, EasyJet\/Ryanair in Europe.<\/span><\/p>\n

Ground transportation is hyper-local and infrastructure-led,<\/b>\u00a0and this is the single most country-specific layer of any vertical:<\/span><\/p>\n

\n\n\n\n\n\n\n\n\n\n\n
Market<\/b><\/span><\/td>\nTop ground transport domain in travel top 20<\/b><\/span><\/td>\n<\/tr>\n<\/thead>\n
Germany<\/span><\/span><\/td>\nbahn.de (#1 in travel)<\/span><\/span><\/td>\n<\/tr>\n
Italy<\/span><\/span><\/td>\nlefrecce.it (#1) + trenitalia.com (top 3)<\/span><\/span><\/td>\n<\/tr>\n
France<\/span><\/span><\/td>\nsncf-connect.com + iledefrance-mobilites.fr<\/span><\/span><\/td>\n<\/tr>\n
UK<\/span><\/span><\/td>\ntfl.gov.uk<\/span><\/span><\/td>\n<\/tr>\n
Netherlands<\/span><\/span><\/td>\nns.nl + 9292.nl<\/span><\/span><\/td>\n<\/tr>\n
Spain<\/span><\/span><\/td>\nrenfe.com<\/span><\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

\u00a0<\/span><\/p>\n

So a more precise statement in this case is: <\/span>travel is fragmented across discovery, planning, and content, but more concentrated in accommodation, air travel, and especially transportation infrastructure, and that concentration runs along national rather than global lines.<\/b><\/p>\n

So what does this actually mean for AI search strategy?<\/b><\/h2>\n

Here are a few practical implications from these AI search traffic trends, especially for brands operating across multiple markets and verticals:\u00a0<\/span><\/p>\n

1. Stop treating AI search as a single optimization problem.<\/b><\/h4>\n

The right strategy in Italian ecommerce (where two domains capture more than half of the head) is structurally different from the right strategy in UK travel (where 129 domains share the same cumulative share). The first one is about marketplace presence and reseller control. The second is about content authority and citation density.<\/span><\/p>\n

2. Audit local champions before investing in any non-US market.<\/b><\/h4>\n

The biggest mistake I keep seeing is brands assuming their global domain authority will carry into a market where a local champion already holds the structured-inventory position. <\/span><\/p>\n

The audit question isn’t “what’s our domain authority”; it’s “who currently holds the operational data, structured inventory, or institutional trust that AI needs to answer my category’s tasks in this market.” <\/span><\/p>\n

If the answer is a local infrastructure player, your strategy needs to be built around that fact.<\/span><\/p>\n

3. Localization needs to extend into entity presence and structured data, not just translation.<\/b><\/h4>\n

Translating product pages won’t help you beat a national rail operator at route queries. The work that does help: making sure your entity is correctly represented in local knowledge graphs, your structured data covers the operational signals that matter (inventory, pricing, availability, hours, locations), and you have local third-party citation density in the publications and directories AI surfaces actually draw from.<\/span><\/p>\n

4. Measure AI search movement, not static snapshots.<\/h4>\n

A top 15 domain trending -25% MoM is in more strategic trouble than a top 50 domain trending flat. <\/span><\/p>\n

The volatility inside the top 50 is where positions are actively being lost, and a quarterly snapshot won’t catch it.<\/span><\/p>\n

5. For brands inside concentrated heads, accept the structural reality and play your actual game.<\/b><\/h4>\n

If you’re an OTA in Germany, you’re not going to outrank Bahn for rail intent. That’s fine, Bahn doesn’t compete with you for accommodation intent either. <\/span><\/p>\n

The play is to win cleanly on the queries where your structured offering is genuinely the strongest, not to fight the operator on its own ground.<\/span><\/p>\n

6. Separate AI visibility from traffic ownership and business impact<\/h4>\n

One of the clearest implications of this data is that AI visibility and AI referral ownership are not always the same thing.<\/p>\n

AI assistants can mention or recommend your brand while sending the measurable click to Amazon, Bol.com, MercadoLibre, Booking, a comparison site, a reseller, a public-sector site, a transportation operator, or a publisher. That is especially important for ecommerce and travel, where the domain receiving the click may not be the brand the user ultimately evaluates or buys from.<\/p>\n

This means AI search measurement needs to go beyond \u201care we mentioned?\u201d and separate three questions:<\/p>\n