Travel Marketers: how to avoid flops in your creator marketing campaigns

When creator marketing campaigns flop, it is embarrassing for everyone. It can be loaded with authenticity but when the results don't drop, accountability can be seen ducking to the toilet just before the campaign check-in meeting....

But sorry, this is a strategic and execution choice you've made.

Travel brands are betting bigger on influencers. According to this article, U.S. travel companies spent about US$1.5 billion on social media marketing in 2025, with that forecast to hit US$2 billion by 2027.

But the grown-up question is not “did people like the content?” It is: did the attention become anything useful? Expedia’s IShowSpeed campaign reportedly drove searches up as much as 70% for Saint Maarten and 50% for Guadeloupe. Virgin Voyages’ Creator Voyage with TikTok generated more than 20,000 pieces of content and over 200 million views.

Those are big numbers. But for every iShowSpeed winning campaign there are scores more with a big outlay of flights, accommodation and creator payments sitting at 3182 impressions and negative impact to the business bottom line. Every marketer has been there and lived that one.

The recent reload of Friends of Singapore 2.0 stated that "49% of all engagement came from just 3.7% of the assets we posted." The problem is that no-one knows before or during the campaign which pieces of content will be the winners.

Of course, the standard answer of a flop is a shoulder shrug and mumbling something about the algorithm which has the CFO reaching for their blood pressure tablets and CMO dreading the next management team meeting. But actually, this is just a failure of both strategy and execution.

If you are still cherry-picking one or two creators and hoping for your algo lottery numbers to come up, then that is on you.

At Videreo we sell the outcome. You don't pay for the flops. Your budget goes towards views by your target audience and nothing else. Not creators travel expenses. Not agency fees. We guarantee 5M views for your $50K budget. That’s a guaranteed $10 CPM. That is better than most paid SEM performance campaigns now in travel. The guessing game is now optional.

What will you choose?

This content is provided by the newsletter sponsor videreo.com - don’t pay for creator marketing that flops

You can now book hotels without leaving the chat in Google’s AI mode

Google has moved AI Mode a lot closer to the travel transaction. Google says AI Mode can now track flight prices inside the conversation, show points and miles rates for flights and hotels, and start rolling out hotel booking in the U.S. with partners including Booking.com, Expedia, Hilton, IHG, Marriott, Priceline, Trip.com and Wyndham.

The traveller is being untrained to visit ten tabs, compare twenty hotel pages and then start again on an OTA. They are being trained to describe what they want once, keep the context alive, and let the interface pull flights, rewards, hotel options, reviews, cancellation policy and payment into the same flow.

When I spoke with Oliver Green from Tourstack on the Everything AI in Travel podcast, his advice for operators was very practical: build a company brain. Connect the systems. Make sure AI can understand your products, prices, availability, policies, meeting points, reviews, FAQs and booking logic.

This Google update is a reminder why that matters. If AI Mode becomes one of the places travellers plan, compare and book, then the brands that win will not just be the ones with the best website. They will be the ones whose inventory, policies and content are easiest for the AI layer to understand and act on.

So the question for travel businesses is probably not “will Google send me less traffic?” - that is now a given for most travel businesses. 

It is: if Google, ChatGPT, Perplexity or an OTA assistant had to sell your product tomorrow, would it actually understand enough to do it well? Because at Google at least, tomorrow has become today.

Where in the loop is valuable for the human to sit?

Human-in-the-loop sounds sensible in travel. It may also become the next bottleneck.

That is the useful tension in a new PhocusWire piece from Wenrix. The argument is not that humans stop mattering. It is that travel should stop treating “a person checks every complex case” as the final version of AI servicing.

Travel has made human review feel like the responsible answer because the underlying infrastructure is so messy. Refunds, exchanges, fare rules, penalties, waivers, ancillaries, multiple passengers, multiple payment types, different airline systems, different content channels. At a certain point the human became the integration layer.

But if AI can read the rules, find the right policy, interpret the context and recommend the action in seconds, then forcing every decision back through a human queue does not remove the problem. It just moves the bottleneck.

When I spoke with Faisal Murtaza on the Everything AI in Travel podcast, we talked about this through the lens of hotel revenue management. His point was not that AI makes the commercial person irrelevant. It was that too much of the role is still data cleaning, checking systems, reconciling spreadsheets and spotting obvious leakage.

AI should move people up the stack. Less “can someone validate what the system already knows?” More “where does human judgment actually improve the outcome?”

That distinction matters. A disruption passenger may need empathy. A high-value corporate traveller may need reassurance. A weird edge case may need judgment. But a routine exchange, refund or pricing correction probably does not need to sit in a queue just because the industry is nervous about letting the system act.

The next serious AI question for travel companies may not be whether to keep humans in the loop. It may be where the loop is still earning its place.

Travel's AI Fork: Airbnb Builds a Lab, Expedia Builds a Team

Travel’s two biggest consumer platforms are making very different bets on AI, and the contrast is getting harder to ignore.

In this great piece of analysis by Christian H we see on one side, Airbnb appears to be inching toward frontier-model capability. In January 2026 it hired Meta’s Llama lead as CTO. In April, PriceLabs flagged Airbnb terms that seemed to allow training on host pricing decisions and guest interactions.

In June, Bloomberg reported that Brian Chesky was funding a frontier lab with his own money alongside outside investors, and in August Chesky confirmed the lab existed while refusing to say much more. That matters because only three weeks before Bloomberg’s report, Chesky had told investors Airbnb would not build frontier models or buy GPUs.

Airbnb has not said whether anything has actually been trained, but it has shifted from openly discussing use of models such as Alibaba’s Qwen to calling its stack competitively sensitive.

On the other side, Expedia Group has taken the infrastructure-and-talent route. It hired a Chief AI and Data Officer from Google in December 2025, published three papers at KDD, and opened a San Jose office close to Google to recruit aggressively. That is a different thesis: not that Expedia needs its own frontier model, but that the advantage sits in the harness around the model, the ranking systems, workflows, and proprietary travel logic that turn generic intelligence into commercial outcomes.

The next 12 months will show whether either approach is more than theatre. Expedia’s technology spend has declined for three straight years, from $1.358 billion to $1.314 billion to $1.277 billion, so if that line falls again while the company expands its AI profile, the programme may look more like recruitment marketing than product transformation. Airbnb, meanwhile, faces the opposite test: whether proprietary data such as its 200 million verified identities and deep review history can become a real learning advantage rather than just a story.

That is the fork now facing every vertical, and travel is just getting there first in public. A rented model may know the world, but it does not know your business. If years of search, pricing, booking and service behaviour are not turned into a system that learns and improves decisions, then even the best data is just an expensive filing cabinet.

Not all who wander are lost

Booking (rightly) is getting all the AI attention but exploration is also an interesting battleground. Afterall, by the time a traveller departs, they still have more than 50% of their travel decisions to still make on the fly.

Arvindh Yuvaraj at Web in Travel has a good piece on iWander, the London startup building AI-guided walking tours. The headline numbers are useful. Co-founder Marius Nigond says only 3% of travellers joined a tour on their last trip, iWander has curated 6.5 million points of interest, and 27% of partner app downloads engage with the iWander layer once it is embedded.

That 3% number is critical. I recorded a podcast this week for Tourpreneur where host Mitch Bach and I discussed this exact point. This is not a “will AI replace human travel guides question”. Nigmond himself explains that going with a guide is the better experience, but most just aren’t and won’t. Building for them is smart. It’s not a threat to anyone.

Everyone is racing to help travellers search, compare and book faster. Fair enough. Google, OTAs, everyone. But most travel decisions still happen after you land. Where should I go next? What is actually worth seeing? What is nearby right now? What fits the mood, the weather, the kids, the energy level, the budget?

That is where travel gets messy and where a lot of the real experience still lives.

What I like here is that iWander did not just throw an LLM at a city map and hope for the best. Nigond says the product narrates from a curated database and benchmarks AI tours against human-curated ones. That is the bit. Not the AI voice. Not the cute character guides. The product discipline underneath it.

There is also a useful B2B lesson in the piece. iWander started inside partner apps, then launched direct because it needed a playground to ship faster. New AI models, onboarding changes, interface experiments and guide personalities were apparently getting stuck in partner release cycles for months.

That feels like a broader travel problem. A lot of companies want AI upside without AI-speed operating models. If every test takes six months, the market has moved before you learn anything. And yet startups need funding and funding requires traction. Often the only way to get it is through partnerships like these.

For tours, attractions, DMOs and city platforms, this is one to watch. AI in travel is not just about getting the booking. It is also about owning more of the in-destination moment, where recommendations, context and transactions can all blur together.

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