The travel industry, worth an estimated $9.5 trillion in 2023, has long been a prime target for tech giants like Google and Meta. With AI, they aim to capture a larger share of the lucrative travel market.
AI has the potential to significantly influence how consumers research and plan their trips, steering them away from traditional discovery platforms and reshaping the entire user journey.
This article explores the rise of AI assistants in the travel space, their capabilities and how they could impact travel brands’ visibility and marketing strategies moving forward.
Google has previously worked to capture as much of the travel market as possible through SERP features, including the Hotels and Flights search and booking features.
AI Overviews (formerly SGE) have also been focused on the travel sector, with Google featuring them in their March 2024 The Keyword Blog.
The launch of AI Overviews has had a rocky start (to say the least), but we need to accept that AI is a large part of Google’s future in Search and other parts of their product ecosystem.
While much attention is paid to AI Overviews affecting website traffic, I think the bigger issue is how Google and Meta’s AI products change the way users discover and research online.
Meta AI is currently in beta in selected countries, but based on testing, it can rival Google’s AI offerings.
Meta also plans to launch the product on Facebook, Messenger, WhatsApp, and Instagram, meaning market penetration will instantly increase to two billion monthly active users with access to Meta AI.
On the Meta AI website, they intend Meta AI to play an active part in group chats (across the various platforms) to let people plan and prepare outings, meet-ups and trip itineraries – but in a group experience, so rather than Googling and sending links within chats, the group can be involved in the discovery process.
Why is this important?
While we have speculated and looked at how AI Overviews may impact traffic, Meta AI has the potential to steer users away from what might have previously been a Google Search and keep them within the Meta ecosystem.
When users eventually leave the Meta ecosystem and move to Google, they may already be much further along their journey.
For example, if I engage with Gemini and ask it to show me “family-friendly European vacations,” I get destination recommendations. If I continue the conversation and narrow my search for family-friendly, all-inclusive resorts in Greece, I get further recommendations.
Historically, without the AI intervention, I might have clicked on the Tripadvisor link that ranked first, a list of the top 10 resorts that match the query.
Outside of this now being a zero-click search, the wider implication is that the top 10 listed by Tripadvisor differ from the recommendations provided by Google’s AI.
This means the user journey has already moved onto a different path and resorts and destinations would have gained any visibility or traction from being visible and having the brand touchpoint by investing in their Tripadvisor reviews and profiles.
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Understanding how users search and stack queries
Users rarely go to Google and perform a single search and are likely query stacking.
Query stacking is the process of refining search queries over multiple iterations to reach the information the user seeks.
It often involves starting with a broad, vague search and then adjusting and specifying the search terms based on the initial results.
This sequence of increasingly specific queries helps users zero in on the exact information they need, leveraging search engines’ understanding of context and intent to provide more relevant results.
Broad searches have been something Google has been working to address for a number of years. During their 20th birthday week in 2018, they showed how they used neural matching and synonyms to address 30% of queries (at the time) to improve the user Search journey.
In travel, this journey is already becoming more multi-modal, with travel consumers following and being swayed by travel influencers on platforms like TikTok and then using the platform to consume more content about destinations they’re researching.
The influence on users is now greater than ever and has longer-lasting effects. Game of Thrones’s effect on Northern Ireland’s tourism is a good example.
Historically, television series would have aired and then been resigned to a VHS box set. However, in today’s era, they live on through streaming services, with extensions being commissioned.
Understanding the potential impact AI can have on the travel user journey
The influence that AI has on a user journey depends on the user type. User type will change and evolve depending on the purpose for which the user is performing a search.
On a top level, we categorize these user types as Learner, Shopper, Participator and Purchaser.
In the context of a user researching their next excursion:
- A learner would be a user at the start of their journey. They are the user performing broad and relatively vague informational searches and are looking to narrow down options. AI can steer this stage of the journey in different directions from what previous search results may have.
- A participator would be the user looking to engage in and read user-generated content and reviews on known and reputable platforms. AI has minimal influence here but can summarize content and potentially surface different sources for the user.
- The shopper and purchaser user types are much further down the funnel in the context of a travel consumer and a lot of these user needs will only be satisfied on websites directly as the consumer is shopping around for the best deal, before making a purchase.
Measuring and monitoring the impact of AI on the travel research journey
Although we can only infer the impact of users staying within the Meta AI ecosystem over time by observing clicks and impressions for stable search term rankings, there are more reliable methods to measure the impact of AI Overviews on user behavior and performance.
You will be able to see these trends in Google Search Console, although there are no plans to distinguish AI Overview clicks and impressions from “regular” clicks and impressions.
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