Article

7min read

The Digital Upgrade: How Experimentation Drives Airline Revenue

Booking a flight is an exercise in high-stakes decision-making. For the customer, it’s a significant purchase filled with dozens of micro-decisions, from dates and times to seat selection and baggage allowances. For an airline, it’s a complex, multi-stage transaction where the smallest point of friction can lead to an abandoned booking and a substantial loss of revenue. Unlike a simple e-commerce purchase, the path from searching for a flight to completing a booking is a long-haul journey in itself.

In this environment, relying on assumptions is a recipe for failure. The color of a CTA button, the order of ancillary services, or the way fees are presented can have an outsized impact on conversion rates. This is why a culture of systematic experimentation isn’t just a “nice-to-have” for airlines; it’s the most effective way to navigate the complexities of the user journey, de-risk critical design decisions, and build a digital experience that turns lookers into bookers, and bookers into loyal customers. It’s about replacing guesswork with the certainty of data, ensuring every change is a step toward a smoother, more profitable customer experience.

The high-friction world of airline UX

An airline website is not a typical e-commerce store. It’s a sophisticated platform balancing user needs, complex business rules, and ancillary revenue goals. A seamless User Experience (UX) here requires a deep understanding of the unique pressures and priorities of the travel booker. Key considerations include:

  • Clarity in search and filtering: The journey begins with a search. Users need to effortlessly filter by dates, stops, airlines, and times. As Spanish travel agency Iberojet discovered, even the initial presentation of search options can have a major impact. They questioned the order of their homepage tabs: “Holiday Packages” vs. “Travel Circuits and Long-Distance Trips.” By running a simple A/B test that swapped the order based on user browsing history, they increased clicks on the “Search” button by a staggering 25%. This shows that getting the very first interaction right is critical.
  • Transparency in pricing: Nothing erodes trust faster than hidden fees. A modern airline UX presents all costs—from baggage fees to seat selection charges—in a clear and upfront manner. The goal isn’t to hide the costs, but to integrate them so seamlessly into the flow that the user feels informed, not ambushed.
  • A mobile-first imperative: More and more travelers are booking complex trips entirely on their mobile devices. This demands a responsive, thumb-friendly design where every step, from entering passenger details to selecting a seat on a detailed map, is intuitive on a small screen.
  • Intuitive ancillary upsells: Baggage options, seat upgrades, and travel insurance are crucial revenue drivers. However, if presented aggressively or confusingly, they become a major point of friction. The best experiences integrate these upsells as helpful, well-timed suggestions rather than mandatory hurdles. A cluttered page that forces users to opt-out of multiple insurance offers feels frustrating, whereas a clean interface that clearly explains baggage options at the right moment feels helpful.

De-risking design with systematic experimentation

Every proposed change to a booking flow is a hypothesis. Does this new layout simplify seat selection? Does this revised copy clarify baggage rules? Experimentation is the process of testing these hypotheses with real users before committing to a full rollout.

A/B testing

This is the workhorse of experimentation. It involves testing one change at a time (e.g., a green “Book Now” button vs. a blue one) to see which performs better against a specific goal, like booking completion rate. It’s simple, direct, and provides clear answers to specific questions. A great example from the vacation package industry comes from Smartbox. They hypothesized that a more prominent “Add to Cart” button would drive more sales. By testing a bright pink CTA against their original aqua one, they saw a 16% increase in clicks. The principle is the same for airlines: small visual changes can yield significant results.

Multivariate testing

This approach allows you to test multiple changes at once. For example, you could simultaneously test two different headlines, three different banner images, and two different CTA buttons to see which combination performs best. This is ideal for redesigning a complex section, like the ancillary services page, where multiple elements interact. Its power lies in not only identifying the best-performing individual elements but also understanding how they influence one another.

Personalization experiments

Not all travelers are the same. A frequent flyer logged into their loyalty account has different needs than a first-time visitor booking a family vacation. Personalization involves tailoring the experience to different user segments. For example, Best Western Hotels & Resorts ran a personalization campaign targeting anonymous visitors looking for a multi-night stay. By showing them a pop-up with a special offer available only to loyalty members, they increased program sign-ups by 12%. Airlines can use the same logic to offer targeted promotions to frequent flyers, pre-fill information for logged-in users, or simplify the interface for new customers.

Navigating the challenges of airline experimentation

While incredibly valuable, running experiments on a high-traffic airline website comes with its own set of challenges:

  • Minimizing disruption: A poorly implemented test can introduce bugs or slow down the site, directly impacting revenue. Rigorous quality assurance and phased rollouts are essential to avoid disrupting the booking process for thousands of users.
  • Complex technical environment: Airline websites are often a web of internal systems, third-party APIs (for everything from payment to loyalty programs), and global distribution systems. Implementing a test that touches multiple systems requires careful planning and deep technical expertise. A test on the seat selection page, for instance, might rely on an external API for the seat map; if that API is slow, it could invalidate the test results.
  • Measuring long-term impact: While it’s easy to measure the immediate impact of a test on bookings, measuring its effect on long-term loyalty or repeat business is more difficult. This requires a mature analytics setup and a commitment to tracking user cohorts over time to see if a winning variation today leads to more valuable customers tomorrow.

Recommendations: Building a culture of continuous improvement

To successfully navigate the turbulence of the online travel market, airlines should treat their website not as a static brochure, but as a dynamic product that is always evolving.

  1. Embrace an ongoing process: Experimentation should not be a one-off project. It’s an iterative, continuous loop of hypothesizing, testing, learning, and improving. The insights from one test should fuel the ideas for the next, creating a powerful engine for growth.
  2. Reduce guesswork with data: Use data-driven insights to inform every UX decision, from the grand redesigns down to the smallest copy change. A powerful example of this comes from Evolve Vacation Rental. By analyzing user intent from different traffic sources, they tested changing a CTA from “Start for Free” to “See if You Qualify.” This simple, intent-aligned copy change drove a 161% increase in conversions, demonstrating the immense impact of data-driven copywriting.
  3. Balance optimization with brand: While optimizing for conversion is critical, it must be balanced with the airline’s brand promise and regulatory requirements. The goal is a journey that is not only efficient but also reassuring, trustworthy, and compliant.

By adopting a disciplined, data-driven approach to UX and experimentation, airlines can move beyond simply selling tickets. They can design digital journeys that are smoother, more intuitive, and build the kind of trust that keeps passengers coming back.

Ready to find your better? If you’re looking to build a data-driven experimentation program that drives revenue and builds customer trust, we’re here to help. Talk to one of our experts today to start your journey.

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Article

6min read

From Clicks to Conversations: The AI Search Era’s Impact on E-Commerce & Travel

For two decades, the digital playbook has been clear: get clicks. Whether you’re selling sneakers or flights, success has been a game of climbing search rankings, optimizing landing pages, and guiding users through a funnel you meticulously built on your own website. That predictable path from a Google search to your checkout page is now being fundamentally rerouted.

The era of AI-driven discovery is here. Tools like Perplexity, Google’s AI Overviews, and ChatGPT are shifting user behavior from searching to asking. Instead of a list of blue links, users get a direct answer, a curated summary, or a complete travel itinerary. Now, with AI models integrating “buy” functionality, the journey is being short-circuited entirely. The conversation itself is becoming the point of sale.

This isn’t just another channel to manage; it’s a paradigm shift that challenges the core assumptions of digital marketing. For e-commerce and travel brands, the question isn’t just “how do we adapt?” but “what are we adapting to?” The truth is, nobody has all the answers yet. What follows isn’t a playbook, because a playbook doesn’t exist. It’s a pragmatic look at the shifts we’re seeing, how we might start to measure this new world, and why a culture of experimentation has never been more critical.

The new reality: From search to answers 

The fundamental change is the introduction of a powerful new middle layer between a user’s intent and a brand’s website. Large language models (LLMs) are becoming expert synthesizers. A user asking, “What are the best running shoes for marathon training under $150?” no longer gets ten articles to read. They get a direct, compiled answer listing three specific models with summarized reviews and maybe a link.

This is the great unbundling of the search results page. The user gets their answer without ever needing to visit multiple sites to compare and contrast. And with platforms like ChatGPT embedding purchasing capabilities, that final step—the transaction—can happen right there in the chat interface. The website, once the center of the customer journey, risks becoming a simple fulfillment endpoint or, in some cases, being skipped entirely.

E-Commerce impact: When the storefront shrinks to a chat window

For e-commerce brands, this shift feels personal. The product detail page (PDP) is sacred ground. It’s a carefully crafted space for storytelling, cross-sells, and brand building. When discovery and comparison happen inside an AI, that ground vanishes.

The immediate impacts are clear:

  • A drop in direct traffic: Fewer users will land directly on product or category pages, making it harder to guide them through a curated experience.
  • The conversion conundrum: If a sale is initiated in a chat and fulfilled on your site (or via an API), how do you attribute it? Traditional last-click models become obsolete.
  • Lost opportunities: The spontaneous cross-sell (“Customers also bought…”) or the carefully placed upsell becomes much harder when you don’t own the interface.

Success in this new ecosystem may hinge on a brand’s ability to be “AI-friendly.” This isn’t about keywords; it’s about data. The brands most likely to be recommended by an LLM will be those with impeccable, highly structured product data that the AI can easily parse and trust. Your product catalog becomes your new landing page.

Travel’s new tour guide: The AI agent

The travel industry is perhaps even more exposed to this disruption. An LLM is, in effect, the ultimate travel agent. A single prompt like, “Plan a 5-day family-friendly trip to Lisbon in May, staying near the city center with a budget of $2,000,” can generate a complete itinerary with hotel options, flight suggestions, and activity booking links.

Brands risk being reduced to a single line item in an AI-generated plan. The key challenges are:

  • Disintermediation: If the AI presents three hotel options that all meet the user’s criteria, the brand’s own marketing and website become secondary to the AI’s curation.
  • Data accuracy is everything: Travel is time-sensitive. An AI won’t recommend a hotel or flight if it can’t confidently access real-time availability, accurate pricing, and clear policies. Outdated or poorly structured data is a death sentence.
  • Commoditization: Without the ability to showcase a unique brand experience on their own site, hotels and airlines risk being chosen on price and basic features alone.

For travel, the path forward requires a radical focus on the quality and accessibility of data. Think rich, structured, and instantly available information that makes your offering the easiest and most reliable choice for an AI to recommend.

Attribution in the age of answers

So, how do we measure success when clicks and rankings no longer tell the whole story? This is where the uncertainty is most palpable. The new platforms are largely opaque, and a new set of metrics is still emerging.

The conversation is shifting from “how did they find our site?” to “are we part of the AI’s conversation?” Potential new measures might include:

  • Mentions and citations: Tracking how often your brand or products are cited as answers to relevant queries.
  • Branded query lift: An increase in users asking for your brand by name (“Find me Nike running shoes”) becomes a powerful indicator of success.
  • Referral attribution: As partnerships form, tracking referrals directly from AI platforms will be crucial, though likely limited to their chosen partners.

For now, tracking remains experimental, but some signals are becoming clearer. We can now see referral traffic from sources like chat.openai.com and perplexity.ai in analytics. However, traffic from Google’s AI Overviews is currently blended with traditional organic search, making it difficult to isolate. This means a complete picture is still impossible, requiring a combination of brand monitoring and deep analysis of the referral data we can get.

Can brands catch up? The case for test-and-learn

This new search paradigm is full of unknowns, but waiting for a settled playbook isn’t a strategy. The only viable posture is a disciplined, test-and-learn mindset. The goal is to make your brand as legible, authoritative, and accessible to AI as possible, preparing you for whatever comes next.

Potential strategies include:

  • Mastering structured data: Implementing comprehensive schema markup across your site is no longer optional. It’s the cost of entry.
  • Creating AI-friendly content: Develop clear, factual, and easily digestible content that directly answers common customer questions, making it prime material for an LLM to cite.
  • Investing in brand and loyalty: When users are overwhelmed with AI-curated choices, a trusted brand name becomes a powerful shortcut. Loyalty programs and excellent customer experiences will be more important than ever.
  • Exploring API integrations: For larger brands, pursuing direct API integrations with major chat platforms could be a way to ensure your inventory and data are seamlessly included in their results.

The honest truth is that this ecosystem is still being built, and the rules are changing in real-time. The brands best positioned to navigate this shift won’t be the ones who guess the future correctly, but those who build a culture of rapid experimentation. The only question is, what will you try next?