In my previous article, I zoomed in on one specific lever: scarcity messages, those “Only 1 room left” badges that reliably nudge users toward certain actions.


We saw something that every product team eventually learns the hard way:


But scarcity is just one tile in a much bigger mosaic. Travel UX is essentially a conveyor belt of uncertainty: Where should we go? Is this safe? Is this overpriced? Will I regret it? What if plans change? And uncertainty is where cognitive biases thrive.


Together with my colleague, Boris Yuzefpolsky, Head of UX Research at Ostrovok, who has been doing research for 10 years, we've decided to dig deeply into the top 12 cognitive biases that affect human decision-making, and explain how to use them correctly in your product

Why travel is the perfect storm for behavioral bias

Almost every industry has biases. Travel has all of them, amplified:


In theory, the user compares alternatives and chooses rationally. In reality, users:


Biases directly affect business outcomes: CTR, conversion, AOV/ARPU, cancellations, support load, CSAT/NPS, and retention.

And the line between ethical nudges and dark patterns is thinner than most teams admit, so let’s widen the lens

A practical mental model: biases appear where uncertainty spikes

Across the travel journey, uncertainty spikes at predictable points:

  1. Inspiration (Where should we go?)
  2. Search/listing (Too many options)
  3. Details (Can I trust this?)
  4. Checkout (Am I making a mistake?)
  5. Post-purchase (Will I regret it?)
  6. Trip execution (Stress + distractions)


Biases cluster around those spikes. So instead of treating biases like trivia, treat them like systemic forces you can map, measure, and design for.

The field guide: 12 cognitive effects you meet in travel UX

1) Anchoring

What it is: The first number you see becomes a reference point, even if it’s arbitrary


How it shows up in travel:

Strikethrough prices, “Was/Now”, “Average price”, "From X”, “Only today” etc. Once the brain latches onto $200, $140 feels like a win, regardless of the market


How to diagnose:


What it moves:


Ethical use:


2) Social proof

What it is: When unsure, we copy others, especially people like me


How it shows up:

“Popular with families” “Booked 27 times today”, "Rates 1 in this area”


How to diagnose:


What it moves:


Ethical use:


3) Confirmation bias

What it is: We seek evidence that confirms our belief and discount contradictions


How it shows up:

Users firstly decide whether a service is good or bad, and only then skim only positive reviews, ignoring recent complaints


How to diagnose:


What it moves:


Ethical use:


4) Probability bias

What it is: People overweight vivid rare risks and underweight boring common risks


How it shows up:

One turbulence story results in a conclusion that planes are unsafe, while a dangerous mountain drive feels normal in checkout, anxious users cling to cancellation policies and insurance


How to diagnose:


What it moves:


Ethical use:


5) Framing + the “Zero price” effect

What it is: Wording changes perceived value. The word "Free” is disproportionately attractive even when economically equivalent


How it shows up:

Breakfast for €7 feels like a loss, but breakfast included for free — feels like a win!


How to diagnose:


What it moves:


Ethical use:

6) Price = quality (price–quality heuristic)

What it is: When unsure, people treat higher price as a proxy for higher quality


How it shows up:

Users pick a slightly more expensive hotel to avoid risk, even when reviews are similar. Price becomes a shortcut for trust


How to diagnose:


What it moves:


Ethical use:


7) Authority effect

What it is: Badges and expert picks create trust


How it shows up:

“Traveler’s Choice”, “Hotel of the Year”, “Recommended”, “Best in district”


How to diagnose:


What it moves:


Ethical use:

8) Survivorship bias

What it is: We learn from success stories and ignore failures


How it shows up:

Users ask friends only about the trips that were amazing, not about what went wrong. Products do the same: highlight happy paths, hide failure modes


How to diagnose:


What it moves:


Ethical use:

9) Dunning–Kruger effect

What it is: Low experience can create overconfidence; users underestimate complexity


How it shows up:

First-time flyers with kids think they’re prepare, and then get hit by sleep/food/noise/stress realities. First-time bookers assume they get it, then make avoidable mistakes


How to diagnose:


What it moves:


Ethical use:


10) Distraction / cognitive overload

What it is: Competing stimuli reduce attention and increase errors.


How it shows up:

In airports: kids, bags, documents, noise. In mobile UX: notifications, small screens, dense UI. Users misclick, rage-click, backtrack


How to diagnose:


What it moves:


Ethical use:


11) Choice overload

What it is: Too many similar options paralyze decision-making


How it shows up:

Listings with hundreds of near-identical hotels create research spiral, which leads to abandonment


How to diagnose:


What it moves:


Ethical use:


12) Compromise effect

What it is: With three options, people often pick the middle to avoid extremes. 


How it shows up:

“Optimal plan” outsells basic and premium. Users choose “middle insurance” without deep reading because it feels safest.


How to diagnose:


What it moves:


Ethical use:


Working with bias: a product checklist

Step 1: Define user segments first

Bias impact is not uniform. New users, anxious travelers, experts, families—react differently. If you analyze average user, you will misread reality

Step 2: Map the whole journey

Biases are not isolated UI widgets, they compound. Scarcity + anchoring + social proof + choice overload can create either helpful clarity, or stress + distrust + churn

Step 3: Prioritize a handful of biases and design experiments

Turn the guide above into hypotheses. A/B tests, surveys, interviews, event analytics—each reveals different truth. And remember: interviews reveal narratives, experiments reveal behavior

Step 4: Measure more than just conversion

Complete picture assembles only when you look on it through a bunch of metrics, not just one

Step 5: Add explicit ethical constraints

Examples:

A quick look at Ostrovok: where we see these effects today

Important: the goal is not to eliminate biases (impossible). The goal is to understand where they help users vs where they harm users, and build a plan around experiments + guardrails


1) Search / start screen

This is where users often begin, and the sense of abundance here can be motivating. But there’s a risk: too much marketing optimism reduces trust. We want to test calmer, more credible wording. Still positive, but less hyperbolic (for ex. “Thousands of verified hotels and apartments worldwide”)


2) Recommendation blocks

Here behavioral patterns can work with users:


3) Hotel listing

This is where choice overload and cognitive fatigue peak. Ratings, badges, and price cues can help orientation: Free cancellation / Pay at hotel framing can reduce stress


But density is dangerous: too many icons, filters, and info blocks increase overload

So the work is:

Conclusion: we don’t design for robots

We design for humans—predictably irrational ones. And we’re not exempt. You can grow conversion by leaning into bias. The easy path is to turn every screen into a pressure machine. The harder path is the one worth building: use behavioral insights to reduce uncertainty, clarify tradeoffs, and support good decisions, without deception. Because in travel, and the same applies to other marketplace as well, the trust is not a metric: trust is the product itself.