The power of intelligent personalization: How AI will transform the customer journey

In this article:

• Why the complex travel funnel demands intelligent personalization
• The difference between traditional SVD and modern deep learning recommendation engines
• The multiplier effect: how a better customer experience leads to sustainable growth
• The future: AI agents and real-time guidance throughout the complete customer journey

The travel industry is known for its complex customer journey. Unlike many other sectors where customers make a purchase within minutes or hours, the average time to booking in travel can stretch to weeks or even months. This unique dynamic creates both challenges and opportunities for (travel) organizations aiming for the best possible customer experience and conversion.

Over the past years, I've experienced up close how technology can transform this industry. What started as a fascination with the complexity of traveler behavior grew into a mission to help companies create genuinely relevant, personal experiences.

In this article, I share my vision on where this technology stands today, where it's heading, and why I believe we're on the eve of a revolution in customer experience, driven by connecting data properly. I'll use the travel industry as my example, but this applies to any industry with a meaningful customer journey.

The complexity of the travel funnel: a challenge of its own

The booking process for a trip is fundamentally different from other online purchases. Where consumers often buy a book or clothing on impulse, travelers go through an extensive orientation phase spanning multiple touchpoints, channels, and decision moments.

Think about the typical journey of a potential vacationer: it might start with an Instagram photo of a friend on a tropical beach. This triggers the first research: which destinations are out there? What's the best season? Then the comparing begins: accommodations, flights, car rental. In between, reviews are read, travel blogs consulted, and price comparison sites visited. There are discussions with travel companions: can the kids come along? Does it fit everyone's calendar? Does it fit the budget?

📸 Instagram inspiration
   ↓
🌍 Destination research
   ↓
🏨 Comparing hotels → ✈️ Checking flights → 🚗 Car rental
   ↓
⭐ Reading reviews → 💰 Comparing prices
   ↓
👨‍👩‍👧‍👦 Discussing with travel companions
   ↓
📅 Checking dates → 💳 Booking

This complexity is amplified by external factors:

• 🎄 Seasonal booking patterns: Long-haul trips are booked months in advance, city breaks often last-minute
• 📈 Dynamic availability: Hotels fill up, flights get more expensive as the date approaches
• 💝 Emotional decision-making: A vacation is often the biggest expense of the year
• 👥 Multi-stakeholder decisions: Different family members with different wishes and preferences

💡 "A customer journey that stretches over weeks or months, with dozens of interaction moments spread across different channels."

The result? A customer journey that stretches over weeks or months, with dozens of interaction moments spread across different channels. And this is exactly where the problem lies: most travel organizations treat every contact moment as an isolated event. The visitor looking at Italy today still gets generic offers for every possible destination tomorrow.

We analyzed this challenge together with Bookit, known for brands like Weekendjeweg.nl and Traveldeal. We started with a simple hypothesis: what if we simply adapted content and visuals to broad, well-known audience segments? Not even one-to-one personalization, just different messages for different groups.

We identified three broad audiences, inspiration seekers, practical planners, and luxury lovers, and created tailored content and imagery for each segment. Where inspiration seekers saw dreamy images of undiscovered destinations, practical planners got concrete information about facilities and prices. Luxury lovers saw exclusive experiences and premium accommodations.

👨‍👩‍👧 Families → 🎠 Kids' activities + 🏊 All-inclusive
💑 Couples → 🍷 Romantic dinners + 🌅 Boutique hotels
👥 Friends → 🎉 Nightlife + 🏄 Adventure

The results were already excellent. This simple form of segmentation alone led to significant improvements in conversion and engagement. No surprise, of course: anyone can figure out that something converts better when it matches your exact wishes.

The really interesting question is how to take this segmentation to true one-to-one personalization. Not by creating audience segments upfront and trying to fit people into a box, but by presenting tailored offers across channels based on data points from the entire customer journey. (Think website, email, social media, and so on.)

📱 Mobile → 💻 Desktop → 📧 Email → 📱 Mobile → 🛒 Booking

This is the problem I've spent the past years solving: truly personalizing with good recommendations, and being present throughout the entire journey with relevant contact moments.

We started with the "standard route": implementing a Customer Data Platform to make some simple changes to the website and email communications based on recognition. We later expanded this with a recommendation engine that could make suggestions based on machine learning. That already delivered very good results. But we ran into its limits, both in intelligence and in the cost of running the model as data volumes grew.

That was the moment, partly driven by the rapid developments in AI, to build our own recommendation engine. The starting points: it had to be self-learning, fast, and cost-efficient. I've written about it before, but I enjoy sharing what makes it different from traditional models, the practical applications (and results achieved), and how it's now woven into the entire customer journey, from searching for a trip, to the experience during the trip, to inspiration for the next destination afterwards.

From linear models to deep learning: the evolution of recommendation engines

Traditional recommendation engines in travel often work with Singular Value Decomposition (SVD) models. SVD is essentially a mathematical technique that reduces large matrices of user preferences to smaller, manageable dimensions. Think of it as an enormous spreadsheet with millions of rows (users) and columns (trips) being "compressed" into a workable model.

Although SVD models were the standard for years, they suffer from fundamental limitations (for this article I'm deliberately simplifying some concepts, the reality is of course more nuanced):

• ❌ Linear assumptions: SVD assumes preferences are linear. If you rate Spain an 8 and France a 6, then Portugal should be a 7. But human behavior doesn't work that way.
• ❌ Cold start problem: New users without history are a black box for SVD
• ❌ Static representations: Once trained, SVD models don't adapt to changing preferences
• ❌ No understanding of context: A romantic city trip is treated the same as a weekend away with friends

The deep learning recommendation engine we developed takes advantage of what's now possible and approaches the math from a different angle. Instead of linear mathematical reductions, it uses Neural Collaborative Filtering, an architecture that can actually model the complexity of human behavior.

The advantages sit mainly in:

• ✅ Self-learning and adaptive: Where SVD models need to be fully retrained periodically (a (very) costly and time-consuming process), the deep learning model learns continuously. Every interaction makes the system smarter, without recalculating the entire model.
• ✅ Intelligent pattern recognition: The system spots subtle connections that SVD misses. It understands, for example, that someone who first looks at budget destinations and then suddenly browses luxury resorts is probably saving up for something special. Not random behavior, but a pattern with meaning.
• ✅ Cost-efficient: Because the model learns incrementally instead of being fully retrained, operational costs are significantly lower. On top of that, better targeting means you waste less expensive marketing money.
• ✅ Real-time performance: While SVD models are often updated in overnight batches, this engine generates real-time recommendations that respond directly to the user's current behavior, immediately applicable across channels.

The crucial difference? Where SVD models forcibly try to squeeze complex human preferences into a linear mathematical model, deep learning embraces the complexity. The system understands that the journey from inspiration to booking isn't linear, but a complex web of emotions, practical considerations, and external factors.

Another crucial advantage of this deep learning approach is its self-learning character. Every interaction, from website visit to booking, feeds the model with new data. This creates a positive feedback loop where:

• 📈 Predictions become increasingly accurate
• 🔍 New trends are identified automatically
• 🗓️ Seasonal patterns refine themselves year after year
• 👤 Individual customer profiles get richer

The system also adapts to changing market conditions. Certain destinations or trip types can suddenly be on the rise. Where you'd normally spot this in after-the-fact analysis, the model notices increased demand from interactions (even before the booking) and can adjust recommendations in real time. This makes it possible to predict not only what a customer wants, but also why, and more importantly (and for me the holy grail): what the next step in their journey will be. This self-learning aspect reinforces the multiplier effect: the longer a customer interacts with the platform, the better the recommendations get, and the higher the chance of repeat purchases.

💡 The multiplier effect: the longer a customer interacts with the platform, the better the recommendations get, the higher the chance of repeat purchases.

Intelligent business rules: domain knowledge combined with AI

What makes this recommendation engine unique is the integration of domain-specific knowledge into the AI model. The system takes into account factors that are crucial for the travel industry. Some examples:

🌤️ Weather-dependent recommendations

🌧️ Raining in NL → 🤖 AI detects → ☀️ Promote sunny destinations

When it rains in the Netherlands, the system automatically promotes sunny destinations. When the weather at home is beautiful, recommendations shift toward domestic weekend getaways. This contextual intelligence not only increases relevance, but also creates a feeling of "they get me" with the customer, the foundation for long-term customer relationships.

📅 Seasonal predictions

🎄 December: Christmas markets booked (in July)
☀️ Summer: Last-minute city trips
🎿 Winter: Planning long-haul trips
🌸 Spring: Locking in summer holidays

The system "knows" that Christmas market trips are booked as early as July, that families travel during school holidays, and that last-minute deals mainly attract solo travelers. These insights are automatically weighed into the recommendations, so customers get the right suggestions at the right moment.

⏰ Availability and urgency

⏰ "2 days left!" → 2️⃣x boost in ranking
🔥 "Almost sold out" → 📈 Higher priority
✅ Available → 👁️ Show
❌ Full → 🚫 Auto-filter

Offers that are about to expire get a 2x boost factor in the ranking. The system automatically filters out unavailable options and takes the minimum booking window per trip into account. This prevents frustration and increases the chance of actual bookings.

🎨 Diversity in recommendations

To prevent monotony, the system limits the number of recommendations per city. For thematic recommendations, such as Formula 1 trips, overlap between similar items is intelligently minimized. This keeps the offering varied and keeps inspiring customers for future trips.

Practical examples: personalization in action

The power of the recommendation engine comes to life in concrete applications across all the channels where the consumer actually is:

💻 Website personalization

On product detail pages (PDPs), visitors see contextual recommendations that match their interest. A family looking at an all-inclusive resort in Turkey gets similar family-friendly options, while a couple browsing luxury city trips is shown other romantic destinations. The copy is dynamically rewritten based on the travel party profile, which at Bookit resulted in a 20% higher PDP-to-checkout conversion, for example.

📧 Intelligent newsletters

Instead of generic newsletters, subscribers receive personalized content based on their position in the customer journey. "Browsers" get inspiring content featuring multiple destinations, while "ready-to-book" profiles receive concrete deals with booking incentives. The system prevents saturation by excluding previously sent recommendations for X days and boosting recently viewed deals for X days. This intelligent approach makes customers look forward to the next newsletter instead of unsubscribing. A nice side effect: your deliverability becomes (and stays) excellent.

📱 Social media targeting

The recommendation engine also drives social campaigns. Instead of advertising broadly, the system creates targeted campaigns per audience and funnel stage. Videos with "3-city inspiration" reach upper-funnel prospects, while concrete deals with AI-generated USPs are shown to visitors further along in their journey. As an example, this orchestrated approach resulted in 23% more add-to-cart actions and lower acquisition costs. Part of that freed-up budget can then be reinvested in attracting new customers. (More on that later.)

💬 Conversational commerce

An innovative module that works hand in hand with the recommendation engine is a travel inspiration wizard: an interactive tool that generates personalized travel brochures based on the user's answers. By connecting the recommendation engine to conversational interfaces like this, travelers can discover their perfect trip through natural interaction. Meanwhile, every interaction enriches the customer profile, making future recommendations even more relevant.

📊 Data collection → 🧠 AI processing → 🎯 Personalization → 📱 Multi-channel delivery

The Bookit business case: numbers and results

An important aspect of a recommendation engine is that it lets you identify profiles. Once you can do that, you have a group of profiles with already elevated interest. That nuance needs to be made, but even so, the results for people who received personalized experiences are impressive:

• 📈 Newsletter: 5.7x higher conversion compared to non-personalized visitors
• 💰 Newsletter: 6.3x higher revenue per user
• 🛒 23% more add-to-cart actions through intelligent multi-channel targeting
• 🎯 41% higher conversion on specific A/B tests for homepage personalization
• 👆 12.5% higher book-now click rate (73% vs. 65% in the control group)

These numbers are obviously great, but the real power lies in what happens next: the multiplier effect that amplifies these initial successes.

The multiplier effect: from conversion to customer value

The impact of intelligent personalization reaches far beyond direct conversion. By offering customers a more relevant experience, a powerful flywheel effect emerges that can transform the entire business. (Or put simply: your LTV:CAC ratio becomes better than everyone else's.)

📈 Better experience
   ↓
💚 Higher loyalty → 🔄 More frequent returns
   ↓                      ↓
💰 Higher CLV      →   📢 Word-of-mouth
   ↓                      ↓
🚀 More budget for acquisition ← 👥 New customers

💚 Increased customer loyalty

When travelers notice that a platform truly "gets" them and makes relevant suggestions, trust grows. They come back sooner for their next trip, which significantly increases customer lifetime value. At Bookit, for example, we saw that personalized customers returned 2.4x more often within 90 days.

💸 More efficient marketing spend

With higher customer value, travel organizations can invest more in acquiring new customers. Improved targeting also ensures marketing budgets are spent more efficiently (simple example: no more ad budget flowing to existing customers who would have booked anyway). That freed-up budget can be invested directly in growth.

🏆 Competitive advantage

In a market dominated by price comparison sites, where loyalty is scarce, superior personalization offers a sustainable competitive advantage. It becomes the reason customers come back, even when they could be a few euros cheaper elsewhere. The emotional bond created by relevant, personal experiences is hard for competitors to copy.

🌐 Network effects

Satisfied customers share their positive experiences, which leads to organic growth. Every new customer who arrives through a recommendation already has positive expectations and is more likely to become a loyal customer themselves. This amplifies the multiplier effect exponentially.

The future: AI agents and presence throughout the complete journey 🚀

The next step is combining the recommendation engine with AI agents. This integration creates unprecedented possibilities across the complete customer journey. This is exactly the part that gets me genuinely excited. :) A few examples of what's being built right now:

🤖 Intelligent chatbots with context

Imagine: a customer chats with an AI assistant about possible destinations. Instead of generic answers, the chatbot has direct access to the full customer profile through the recommendation engine. The bot "knows" this customer has been to Spain before, prefers family vacations, and usually books during the May school break. The result? Hyper-personalized advice that feels like a conversation with a personal travel advisor, and lays the foundation for a long-term customer relationship.

🎯 Proactive customer service

When a customer contacts customer service, the AI agent already has context. Is the customer leaving within a week? Then the agent can proactively help with last-minute questions. Has the customer recently looked at similar destinations? The agent can immediately offer relevant alternatives in case of changes or cancellations.

💬 Real-time travel guidance via WhatsApp

An exciting development is WhatsApp journey technology, which brings personalization to the missing moment: during the trip itself.

This kind of assistant elevates the customer experience with, for example:

• 🏖️ Proactive tips at the right moment: "The weather is perfect for a beach visit today. Here are three beach clubs that match your preferences."
• 🎫 Personalized vouchers and tickets: Specific discounts or personalized suggestions for entrance tickets, for a particular museum, for example.
• 🎯 Smart activity suggestions: Based on the travel party, combined with that day's weather and historical behavior.
• 📲 Direct ticket purchases: Bookable directly via WhatsApp, without leaving the app.

📍 Location check → 🌤️ Weather analysis → 💡 Suggestion → 🎟️ Directly bookable

The interesting part is that all these interactions contribute to one central customer profile. Every WhatsApp conversation, every voucher used, every activity booked enriches the profile. When the customer returns for the next booking, the system has become even smarter. The multiplier effect in action: every action makes the next experience better.

The flywheel effect of integrated AI

This combination of a recommendation engine and AI agents creates an incredibly powerful flywheel:

Pre-trip: 💬 Chatbots → inspiration & planning
   ↓
✈️ Booking: 🎯 Personalized suggestions
   ↓
🏖️ On the trip: 💬 WhatsApp assistant → relevant tips
   ↓
🏠 After the trip: 🤖 Agents → preparing the next trip
   ↓
🔄 Repeat, with even better personalization

• Before the trip: Chatbots help with inspiration and planning, fed by recommendation data
• During booking: Personalized suggestions and dynamic content increase conversion
• On the trip: A WhatsApp assistant keeps customers engaged with relevant tips and offers
• After the trip: Follow-up via agents that are already preparing the next trip

Every phase strengthens the next, every interaction makes the system smarter, and every satisfied customer increases the chance of repeat purchases and recommendations.

Conclusion: the journey toward truly great customer experience through personalization is underway

The travel industry is at a crossroads. On one side, technological developments make unprecedented personalization possible. On the other, consumers expect ever more relevance and convenience. Organizations that seize this opportunity and invest in intelligent recommendation engines position themselves well for the long term. Especially in an era of rising advertising costs, shrinking visibility, and heavy competition.

🚦 CROSSROADS
                        |
       Old road 🛤️ ←────┼────→ 🚀 New road
                        |
   ❌ Generic          ✅ Personalized
   ❌ Reactive         ✅ Proactive
   ❌ Fragmented       ✅ Integrated

In an industry where margins are under pressure and acquisition costs keep rising, intelligent personalization can be the difference between surviving and thriving. It's no longer about optimizing individual touchpoints, but about creating an integrated ecosystem where every interaction adds value.

For travel organizations ready to take the next step, it seems clear to me: the technology exists, the business case is proven, and the time to act is now. Because in the world of travel, it's ultimately about creating unforgettable experiences, and that starts at the very first digital contact.

But let's be honest: these questions are not limited to the travel industry. Whether you work in retail, financial services, education, or healthcare, wherever customers go through a journey, wherever choices are complex, and wherever personalization can make the difference, these principles apply. The technology developed for travel keeps proving valuable in sectors nobody initially thought of.

And that brings me to what I find most fascinating about these developments: the conversations with people facing similar challenges. Whether you're a tech enthusiast working on similar solutions, or an entrepreneur wrestling with customer experience in your own industry, I get energized by exchanging ideas on these topics. How do you translate complex data into human experiences? Where is the line between personalization and privacy? How do you build systems that truly learn and adapt?

I don't have all the answers, and probably even AI doesn't ;). The technology is developing at breakneck speed, customer behavior keeps evolving, and what works today can be outdated tomorrow. But that's exactly why the dialogue is so valuable. By sharing experiences, learning from each other, and innovating together, we get further than we would alone.

Consider this article an invitation. Curious how these concepts could work in your industry? Working on innovative customer experience solutions yourself? Or simply in the mood to exchange thoughts about the future of personalization? I'd love to hear from you. Because the best ideas often emerge from the cross-pollination between different worlds. 💡

-- Bram Versteegh

Bram Versteegh is the founder of MartechNext, covering the business of AI in marketing: who's building it, who's funding it, and how industries put it to work.

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