Big Potential and a Growing Problem
When the founder of a fast-growing travel platform reached out to us, he didn’t ask for AI recommender, or LLMs. He simply said:
We’re losing people. They come, they scroll, they leave. We need to help them find the right experience, and faster.
Their platform offered beautiful accommodations, curated activity bundles, even hidden gems that no one else had. But to users, it all looked the same:
- Overwhelming choices without personalization
- Generic and static experience
What they needed wasn’t more offers. They needed relevance.
That meant surfacing offers that matched a user’s behavior, location, preferences, and timing. Not through filters and endless menus, but automatically, intuitively, and in real time.
The Turning Point: Let’s Start Simple, but Smart
From experience, we knew that a lean, focused approach was the quickest way to deliver value and make an impact. In fact, the platform didn’t need an entirely new system; they needed smart solutions that could scale with them.
So, we proposed starting with a light integration of Amazon Personalize, a powerful AI recommendation engine. This strategy helped us quickly get things up and running.
No huge infrastructure overhaul. No six-month development cycle.
Just a quick, effective implementation of pre-built AI models that could learn from user behavior and start delivering personalized recommendations, smarter and faster.
Within three weeks, we had:
- Connected their website to Amazon Personalize
- Integrated real-time event tracking (using PutEvents API)
- Activated smart blocks like “You might also like”, “Trending now in your area”, and “Frequently booked together”
It wasn’t flashy. But it was powerful.
And the results started showing really fast.
What Happened Next: The Metrics Spoke for Themselves
After just 30 days, we checked in. The data didn’t whisper: it shouted.
Indeed, users were staying longer, booking faster, and engaging more. And most importantly: they were finding exactly what they wanted.
That’s when the conversation shifted. “Okay,” the founder said. “What’s next?”
From Pre-Built to Fully Customized AI Recommender
Once the client saw what AI could do with out-of-the-box models, we started building beyond the box.
Initially, we expanded the system with custom models using AWS SageMaker and integrated LLM-based tools to supercharge personalization.
Location-Specific Branding: Same Product, Different Story
For users from the UK (perhaps traveling to a sunny beach destination like the Maldives):
- Product Name: “Seaside Escape: Luxurious Oceanfront Villa with English Breakfast”
- Why It Works: British tourists often enjoy a traditional breakfast, and the idea of a relaxing, quiet escape resonates well with their travel preferences.
For users from the US (especially the west coast, visiting the same Maldives destination):
- Product Name: “Beachfront Adventure: Oceanfront Villa with Surfing & Sunset Views”
- Why It Works: American travelers, particularly from the West Coast, often seek active yet relaxing vacations, making surfing and stunning sunsets an attractive combination.
Seasonality & Weather-Aware Offers
For a user from New York, heading to a tropical destination in winter (like Bali or the Maldives):
- Offer: “Warmth Awaits: Beachfront Villas with Private Pools & Sunset Views”
- Why It Works: The AI recognizes the user wants a winter break and offers a warm, relaxing tropical escape.
For a user from Berlin , looking for winter activities in Switzerland:
- Offer: “Alpine Adventure: Ski Chalet with Guided Skiing & Spa Access”
- Why It Works: German travelers prefer active winter experiences, so the AI recommends a skiing-focused activity based on their winter sports interests.
The system automatically adjusted based on weather APIs and regional seasons.
Bundled Offer Optimization
Users didn’t just see hotels; instead, they encountered experiences.
For example, “Tropical Bliss: Oceanfront Villa + Thai Massage + Cooking Class + Excursion to Temples,” custom-crafted specifically for them.
Chat-based Discovery (LLM Assistant)
A conversational AI assistant, powered by GPT, helped users find what they didn’t even know they wanted.
What You Can Learn From This
- Start simple: You don’t need to reinvent your tech stack. Amazon Personalize offers a fast, low-risk entry point.
- Let the data guide you: Once you see results, scale with custom AI and LLM features.
- Personalization isn’t just about relevance: it’s about connection.
By implementing a simple AI recommender system, we provided personalized, real-time suggestions that guided users to the right experiences.
This not only improved conversions but also boosted customer satisfaction, with users spending more time on the site and making more bookings.
With this system in place, there’s always room for growth, particularly through deeper personalization and advanced AI models. Moreover, this system offers the opportunity to evolve with new features that adapt to changing customer needs.
If you’re facing similar challenges, we can help you build an AI recommender system that simplifies the customer journey and drives results. Feel free to reach out to us to get started.
