# Ryanair

Booking flow design for millions of customers.

## The impact

**Reduced drop-off at the flight selection step by eliminating forced back-navigation, and improved higher fare conversion through a controlled experiment that challenged a standard UX assumption — both measured against live traffic at one of Europe's highest-volume purchase funnels.**

## The context

Ryanair's booking flow is one of the highest-volume purchase funnels in European travel. Hundreds of millions of passengers a year pass through it. At that scale, a 1% improvement in any conversion step isn't a minor win — it's material. A 1% drop is equally serious. The design environment doesn't allow for intuition-led changes.

The challenge of working in a funnel like this isn't creativity — it's discipline. Every hypothesis has to be testable, every direction has to be grounded in data before it reaches prod, and every experiment has to be scoped carefully enough to produce a clean signal.

## My role

**UI Product Designer**, embedded in a cross-functional squad. I owned design end-to-end: discovery, exploration, prototyping, stakeholder reviews, and developer handoff, alongside a UX designer, a PM, PO and a researcher. Funnel analytics drove every direction before it moved to design. I worked closely with product and engineering to scope experiments that would hold up at the volume Ryanair operates at.

## The approach

The squad used funnel analytics as the primary signal source — drop-off rates, return navigation rates, time-on-step — to identify where friction was concentrated. Usability testing and user interviews were brought in to diagnose root causes once the data flagged a pattern. The loop was: analytics surfaces the problem, qualitative research explains it, design proposes a solution, and where possible we test in a controlled experiment before full rollout.

Two problems had the clearest signal and the most addressable root causes.

## The problems

### Flight selection: trapped users

Session data flagged high return navigation at the flight selection step — users were moving forward into the funnel, then navigating back to search repeatedly before completing or abandoning. The pattern was pronounced enough to show up clearly in funnel analysis, and consistent enough that it was one of the most significant friction signals across the entire booking flow.

Cross-referencing with usability testing and user interviews made the root cause clear. When users arrived at the flight selection page, they wanted to compare alternative dates and prices before committing to a specific outbound flight. The design gave them no way to do that without exiting the step entirely — losing context, resetting the search, and starting again. So they went back, looked around, came forward again, or didn't come back at all.

**The design decision** was an edit flight capability: a way to adjust the search — dates, departure point, destination — directly from within the flight selection step, without losing context or resetting progress. It surfaced on demand rather than cluttering the interface for users who'd already made their minds up.

Before settling on that direction, I explored two alternatives. The first was a date picker embedded in the results list itself, letting users slide across dates without leaving the step. It kept users even more anchored in context but added visual complexity to an already dense results page — and the risk of accidentally triggering a date change while scrolling was non-trivial. The second was a persistent summary bar at the top showing the current search parameters, tappable to edit. This was cleaner but required users to understand that the summary was interactive, which usability testing suggested they wouldn't assume.

The edit flight approach — a dedicated, clearly labelled entry point — tested better on both discoverability and user confidence. It was obvious what it did, it was out of the way when not needed, and it removed the forced exit entirely.

Removing the forced exit cut back-and-forth navigation sharply and produced a measurable improvement in flow success rates at that step.

### Higher fare conversion: a counterintuitive result

Ryanair's higher fare tiers include flexibility and perks with real monetary value for frequent travellers — but conversion on those tiers was underperforming what the pricing suggested it should be. The initial hypothesis was a value clarity problem: users didn't understand what they were actually getting, or didn't trust that the described benefits would apply when it mattered.

The design work focused on making value concrete rather than abstract — showing what a flexible ticket means at the moment of a flight disruption, rather than listing "flexibility" as a row in a comparison table. Specificity changed how users evaluated the upgrade.

But the more interesting finding came from a controlled experiment on the structure of the fare display itself.

**The hypothesis going in was conventional:** fewer choices reduce cognitive load and drop-off. Hick's Law is a standard reference in conversion work — the more options presented, the longer it takes to decide, and the higher the abandonment risk. The control showed three fare options. The test variant showed four.

**The result went the other way.** In a tightly scoped experiment — limited to a specific geography and a defined user segment with particular package combinations — four fare options converted better on higher tiers than three. Users in the variant were more likely to select an upgraded fare, not less.

The experiment was designed to isolate the effect: the geography and segment were chosen to control for variables that could confound the result, ensuring the comparison was clean. The finding held within that scope.

The likely mechanism wasn't random. With three options, the middle tier absorbs most of the decision. With four, the distribution shifts — a new reference point changes how users weigh the tiers above it. What looks like the "safe middle choice" at three options becomes a different anchor at four. Hick's Law describes decision time and drop-off risk accurately; it doesn't account for how adding a tier can reframe the perceived value of the options already there.

The experiment didn't overturn the principle — it revealed a context where the interaction between choice architecture and tier anchoring outweighed the cognitive load effect. That's the kind of result you don't get from assumption. You get it from running the test.

## Key learnings

**Data tells you where. Research tells you why.** The return navigation pattern was visible in analytics. But the reason — users wanting to compare dates without losing context — came from usability testing. Neither source alone would have produced the right solution. The loop between them is what made the design defensible.

**Standard UX principles are starting points, not conclusions.** Hick's Law is a reliable heuristic in most contexts. In a tiered pricing environment, it gave us the wrong prediction. The principle didn't fail — the context changed what it meant. Running the experiment before assuming was the right call.

**Scoping experiments carefully is a design skill.** The fare experiment worked because the geography and user segment were controlled tightly enough to isolate the variable. A poorly scoped test at Ryanair's volume would have produced noise that looked like signal. Getting the experimental design right was as important as getting the product design right.

**At this scale, "good enough to ship" is expensive.** Every direction had to be defensible before it reached prototype stage. That discipline isn't a constraint on creative work — it's what makes creative work credible in a data-driven environment.

## What the scale teaches you

Designing for a funnel at this volume changes how you think about decisions. You stop asking "does this feel right?" and start asking "what would prove this is right?" The data-driven framework wasn't a constraint on creative work. It was the thing that made the work legible — to the team, to stakeholders, and to me.

The most valuable thing scale gives you is honest feedback. At Ryanair's volume, reality arrives fast and at sufficient sample size to be believed. That makes it one of the hardest environments to work in and one of the most instructive.