Enhancing Airbnb's
Map Feature Interaction
Redesigning the Airbnb map feature to streamline user decision-making by integrating accessible local insights, simplifying the search for the perfect stay.
TOOLS
Figma
Google Forms
TIMELINE
2024 January
Skip to Prototype
INTRODUCTION
Airbnb offers a vast selection of unique places to stay and things to do worldwide, but finding the right fit can be tough, especially in new cities. Travelers often struggle to match their needs with available options, and the limited information can result in choices that don't meet expectations.
During my study abroad program in Singapore, I explored Southeast Asia, visiting bucket-list destinations like Bali, Vietnam, and the Philippines through Airbnb. Inspired by this experience, I embarked on a personal passion project to redesign a key aspect of the Airbnb booking process—the map feature. My aim was to streamline the decision-making journey for users, enhancing the efficiency of selecting the perfect place to stay.
Ho Chi Minh City, Vietnam
My favorite
Uluwatu, Bali, Indonesia
Lazarus, Singapore
PROBLEM STATEMENT
Finding the right airbnb in a foreign city is hard.
A key challenge I encountered during these trips was choosing the right Airbnb when navigating foreign cities. Many questions regarding the neighborhood of the Airbnb popped up in groupchats.
For guests, the primary concern is to make a decision that aligns with their preferences and travel itineraries, the lack of information can lead to a mismatch between expectation and reality.
For hosts, they may face the challenge of highlighting the unique advantages of their location to attract potential guests. This is currently done through a length written description.
Questions like...
USER ANALYTICS
Users book through Airbnb for authentic local experiences.
With Airbnb's younger audience seeking authentic local experiences, the platform's current lack of detailed local insights limits travelers' ability to fully immerse with their destinations. Bridging this information gap will enable users to choose the right airbnb for them.
59%
Airbnb users are aged 25-44
Source: Search Logistics
77%
Guests say they choose airbnb to live like locals
Source: Search Logistics
QUALITATIVE USER RESEARCH
There are two types of users: research-oriented and spontaneous.
To gain deeper insights, I conducted five comprehensive interviews with individuals aged between 25 and 44, all of who have prior experience using Airbnb.
The interviews revealed two main user profiles: Research-Oriented Users, who utilize third-party platforms to gather information about their Airbnb location, enhancing their booking decisions; and Spontaneous Users, who tend to book impulsively, often finding themselves in unexpected areas.
QUANTITATIVE USER RESEARCH
Both types of users want better methods to look for local insights
To get a broader understanding of user perspectives, I carried out an online survey with 200 participants to assess their preferences for local insights and how these insights might influence their Airbnb booking choices.
AFFINITY MAP
Overall, users want more information regarding neighborhoods in foreign cities.
This affinity map revealed a significant gap in providing detailed local insights. The insights gathered suggested solutions such as enhancing Airbnb's map feature by offering local insights and real-time local information to improve user experience and satisfaction.
GOALS
Optimize Airbnb's interface with local information for improved decision-making.
My key design goal is to streamline Airbnb's interface and enrich it with accessible local insights, simplifying user decision-making process, boosting platform engagement and satisfaction.
For guests, the aim is to enable faster and more efficient discovery of suitable accommodations, ensuring quick access to detailed local insights for making more informed booking decisions.
For hosts, the focus is on improving property visibility through enhanced functionalities and attracting guests whose preferences align with what the property offers.
Business Impact
Increased User Retention
Simplifying discovery with the map feature encourages users to stay longer and return, reducing drop-offs.
Revenue Growth
Improved map engagement leads to more bookings, directly increasing Airbnb's earnings.
User Impact
Less decision fatigue
Less time wasted on searching on third-party platforms
More local gems discovered
Better travel itineraries
IDEATION
How can we enhance Airbnb stay selections by providing comprehensive local neighborhood insights?
Approach 1
A local insights feed
Access hidden gems through a local insights feed recommended and verified by airbnb hosts.
Approach 2
Community Spot Sharing
Discover key attractions and insights at a glance with an intuitive, map-integrated exploration tool.
Approach 3
Explorer Mode
Discover key attractions and insights at a glance with an intuitive, map-integrated exploration tool.
Based on user research and pain points, I chose approach 3 ‘Explorer Mode’ to meet our users' varied needs, from planners to explorers. This feature uses a heat map to show the density of local insights along with preview cards to browse images of the location.
APP AUDIT
The Airbnb map feature is limited.
Before starting my design process, I reviewed Airbnb's map feature and identified shortcomings such as the non-interactive interface and scarce information, making it hard for users to find suitable accommodations.
PRODUCT USER TESTING
Gaining feedback from the community.
To refine and improve the design, I conducted user testing with 5 participants, resulting in three key findings:
FINDING 1
Red is often associated with danger, suggesting a need for a more intuitive gradient.
FINDING 2
The abundance of pins on the map made it hard to focus on specific interests like safety or food.
FINDING 3
Without real-time information or information about the distance from and to restaurants and attractions, users felt that the information was unreliable and out of date.
FINAL SOLUTIONS
Key Features of 'Explorer Mode'
Based on these findings, I created product solutions specific to the pain points found during the user testing.
FEATURE 01
Local Insights Density Heatmap
This feature displays a heatmap of local attractions and eateries, guiding users to high-density neighborhoods for deeper exploration and informed stay decisions.
FEATURE 02
Insight Type Filter Categories
Introducing distinct categories such as dining, transportation, attractions, and safety to facilitate targeted and a visually simple interface.
FEATURE 02
Live Data and Navigational Map Directions
Enhanced exploration by integrating live crowd level data and providing mapped directions to each location.
MEASURING SUCCESS
Measuring results through ‘Explorer Mode’
Although I do not work at Airbnb, I'm confident this idea could succeed with proper validation and iteration.
Success metrics could include:
# of bookings made directly from 'explorer mode' map interactions
# of users retained comparing ‘explorer mode’ users vs. non-map users
# of hours users spend using the map feature with ‘explorer mode’ compared to previous levels
REFLECTION
Measuring results through ‘Explorer Mode’
As an active user of airbnb, there were many features I wanted to explore and address inspired by my travels in Southeast Asia. But ultimately I had to scope it down to one problem to dive deeper on. This study was the result of multiple rounds of problem definition and scoping of issues. Overall, the process was both rewarding and personally meaningful.
For next time…
Improve ‘Explorer Mode’ search and discovery with themed filters.
Enable offline map access for seamless navigation without internet.
Promote sustainable travel by highlighting eco-friendly options.
Moving forward…
My focus is on continuous improvement—gathering feedback, refining the solution, and exploring new enhancements to better the Airbnb experience, making every travel experience memorable.
Travel Bucket List
Great Barrier Reef in Australia
Northern Lights in Iceland
Surfing in Fiji Islands