All case studies

All case studies

All case studies

Airbnb hackathon

Airbnb hackathon

Reimagining Airbnb’s booking flow to help adventurous travelers discover curated experience bundles.

UI.UX

Hackathon

Role:

Lead UX Designer, Research, High Fidelity

Scope:

24 hours

Toolkit:

Figma, FigJam

Building better experience bundles with data.
Building better experience bundles with data.

In a winter hackathon sprint, our team explored how Airbnb might better serve returning users—specifically adventurous travelers. We focused on designing a smarter, more intuitive recommendation engine that suggests experience bundles tailored to users’ past behaviour, preferences, and seasonal travel trends.

In a winter hackathon sprint, our team explored how Airbnb might better serve returning users—specifically adventurous travelers. We focused on designing a smarter, more intuitive recommendation engine that suggests experience bundles tailored to users’ past behaviour, preferences, and seasonal travel trends.

In a winter hackathon sprint, our team explored how Airbnb might better serve returning users—specifically adventurous travelers. We focused on designing a smarter, more intuitive recommendation engine that suggests experience bundles tailored to users’ past behaviour, preferences, and seasonal travel trends.

In a winter hackathon sprint, our team explored how Airbnb might better serve returning users—specifically adventurous travelers. We focused on designing a smarter, more intuitive recommendation engine that suggests experience bundles tailored to users’ past behaviour, preferences, and seasonal travel trends.


Seamless discovery of experience bundles.
Scenario

Scenario

Our use case centered on Whistler, BC—a premier winter destination and the most sought-after Canadian spot for American travelers.

With the hackathon taking place during peak ski season, this scenario allowed us to ground our concept in a high-demand, seasonal travel context, highlighting how our proposed feature could meaningfully enhance the experience for returning users planning popular, adventure-driven trips.


Constraints

Constraints

For design consistency, we drew from Airbnb’s existing branding and UI structure, to ensure our solution felt intuitive and integrated, rather than disruptive. We worked within Airbnb’s Design Language System (DLS), using existing UI patterns to maintain a familiar user experience.

We also prioritized a modular design approach, allowing our solution to be easily integrated into Airbnb’s current platform. This strategy helped us balance a more novel solution with feasibility, building a feature in a short amount of time that felt native to the product while showcasing clear added value.

For design consistency, we drew from Airbnb’s existing branding and UI structure, ensuring our solution felt intuitive and integrated, rather than disruptive. We worked within Airbnb’s Design Language System (DLS), using existing UI patterns to maintain a familiar user experience.

We also prioritized a modular design approach, allowing our solution to be easily integrated into Airbnb’s current platform. This strategy helped us balance a more novel solution with feasibility, building a feature in a short amount of time that felt native to the product while showcasing clear added value.


Research insights

Research insights

We focused our research on Airbnb’s core demographic of adventurous travelers aged 25–35, analyzing usage patterns and preferences within this group. In particular, we identified women as the platform’s largest user segment, alongside a growing number of young adults and small families seeking short, memorable getaways.

Our secondary research revealed a common thread across these audiences: a desire for a more personalized, streamlined booking experience that offers curated recommendations without requiring extensive filtering or manual planning.

We focused our research on Airbnb’s core demographic of adventurous travelers aged 25–35, analyzing usage patterns and preferences within this group. In particular, we identified women as the platform’s largest user segment, alongside a growing number of young adults and small families seeking short, memorable getaways.

Our secondary research revealed a common thread across these audiences: a desire for a more personalized, streamlined booking experience that offers curated recommendations without requiring extensive filtering or manual planning.


Millennials

Over 50% of users are between 25-44

Female

56% of users identify as female

Young adults

Young adults / small families are main groups

Unique

Prefer a more personalized travel experience

Proto-persona

Proto-persona

These insights shaped our decision to focus on the Adventurous Traveler as our proto-persona, guiding both our design strategy and feature prioritization within the time constraints.

These insights shaped our decision to focus on the Adventurous Traveler as our proto-persona, guiding both our design strategy and feature prioritization within the time constraints.


Challenge

Challenge

How might we leverage user data to create a more personalized recommendation engine that suggests relevant travel packages for the Adventurous Traveler?


Seamless discovery of experience bundles.

Seamless discovery of experience bundles.


Our solution

Our solution

We designed a lightweight, data-powered feature for users to discover and book experience bundles—without disrupting Airbnb’s familiar interface. The simple solution was a top-level "Experience Bundles" button placed at the top of the search results page to sort options for qualifying stays.

We designed a lightweight, data-powered feature for users to discover and book experience bundles—without disrupting Airbnb’s familiar interface. The simple solution was a top-level "Experience Bundles" button placed at the top of the search results page to sort options for qualifying stays.

We designed a lightweight, data-powered feature for users to discover and book experience bundles—without disrupting Airbnb’s familiar interface. The simple solution was a top-level "Experience Bundles" button placed at the top of the search results page to sort options for qualifying stays.


Home
Home
Home
Home
Search by regular stays
Search by regular stays
Search by regular stays
Search by regular stays
Search by experience bundles
Search by experience bundles
Search by experience bundles
Search by experience bundles
Listing for bundled stays
Listing for bundled stays
Listing for bundled stays
Listing for bundled stays
Travel bundles

Travel bundles

When clicked, users are shown properties with bundled experiences, prioritized by geographic relevance and availability based on their selected travel dates. Within the listing and reservation flow, a clear “Add Bundle” button appears before checkout, encouraging users to explore curated add-ons like local excursions, rentals, or tours.

We retained Airbnb’s existing experience layout for consistency, but enhanced it with prominent CTA buttons that:

  • Clearly explain what’s included

  • Display additional costs upfront

  • Allow users to opt into bundles with confidence


Option to add on experiences
Option to add on experiences
Option to add on experiences
Option to add on experiences
Bundle and save CTA
Bundle and save CTA
Bundle and save CTA
Bundle and save CTA
Why this works

Why this works

This design both streamlines the booking process for experience-seeking users and supports Airbnb’s revenue growth by increasing add-on bookings, while respecting user expectations and interface familiarity.


Minimal interface disruption—returning users don’t have to re-learn the existing UI.

Context-aware travel recommendations—travel suggestions that actually make sense (seasonally, experientially, behaviourally).

Stronger value perception—bundles feel like premium curation, not simply another listing.


Experience booking page
Experience booking page
Experience booking page
Experience booking page
Option to add bundle
Option to add bundle
Option to add bundle
Option to add bundle
Bundled cost savings
Bundled cost savings
Bundled cost savings
Bundled cost savings
Total cost breakdown
Total cost breakdown
Total cost breakdown
Data analysis

Data analysis

Our data science team designed a lean architecture that pulls from user search history, saved stays, and stated interests. This framework leverages dynamic, real-time suggestions that feel timely, relevant, and tailored to each traveler’s preferences and behaviour.

By matching these signals with seasonal trends and location data, the platform highlights curated travel bundles that make sense not only for where users want to go, but for how they like to explore.

After 24 hours, our team developed a streamlined solution for smarter travel planning and higher user engagement to introduce Airbnb’s bundled offerings.


Reflections

Reflections

To kick off the hackathon, our team prioritized storytelling as a foundation for ideation, crafting a scenario around a week-long getaway to a Whistler ski chalet.

This narrative helped shape our UI direction—with relevant visual design elements and seasonal context—while also guiding our data scientists in building a personalized search recommendation engine tailored to adventure-driven travel.

To kick off the hackathon, our team prioritized storytelling as a foundation for ideation, crafting a scenario around a week-long getaway to a Whistler ski chalet.

This narrative helped shape our UI direction—with relevant visual design elements and seasonal context—while also guiding our data scientists in building a personalized search recommendation engine tailored to adventure-driven travel.


Outcomes

Outcomes

Our approach was recognized by two senior members of Airbnb's product team for its seamless integration within the platform's existing interface and thoughtful application of user data, prioritizing relevance without raising privacy concerns.

While we initially set out to design for mobile, we quickly pivoted to a desktop-first approach due to time and development constraints, reinforcing the importance of clear communication and adaptability within our sprint team.

For us, this challenge reinforced the importance of building upon an already strong UX foundation—effective design introduces new features with intention and clarity, meeting users where they are.

Our approach was recognized by two senior members of Airbnb's product team for its seamless integration with the platform's existing interface and thoughtful use of user data that prioritized relevance without raising privacy concerns.

While we initially set out to design for mobile, we quickly pivoted to a desktop-first approach due to time and developer constraints, reinforcing the importance of clear communication and adaptability within our sprint team.

In the end, this challenge reminded us that great UX isn’t just about adding new features—it’s about introducing them with intention, clarity, and empathy, always meeting users where they are.

Our approach was recognized by two senior members of Airbnb's product team for its seamless integration with the platform's existing interface and thoughtful use of user data that prioritized relevance without raising privacy concerns.

While we initially set out to design for mobile, we quickly pivoted to a desktop-first approach due to time and developer constraints, reinforcing the importance of clear communication and adaptability within our sprint team.

In the end, this challenge reminded us that great UX isn’t just about adding new features—it’s about introducing them with intention, clarity, and empathy, always meeting users where they are.



Credits

Special thanks to Hackathon contributors Maggie Pyke, Emile Tal, Ivan Marcus, Laura Koch, Mychal Ortiz, Yael Brown, and Mayur Brown.

Credits

Special thanks to Hackathon contributors Maggie Pyke, Emile Tal, Ivan Marcus, Laura Koch, Mychal Ortiz, Yael Brown, and Mayur Brown.

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© 2025 Michelle Murvai

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© 2025 Michelle Murvai

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Home

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About

Contact

© 2025 Michelle Murvai

Let's bring your idea to life

Get in touch with me

Home

Case Studies

About

Contact

© 2025 Michelle Murvai