Taking a pass at designing a second version of AirBnB's mobile home for intent-minded users/Guests. (This interface would be served to intent-minded Guests similar to digital ads served to a specific segment.)

**NOT formally affiliated with AirBnB! I'm just a designer/researcher (and big fan!) looking to inspire others.

Consideration 01

Intent-based scrolling with stickiness per each listing for controlled exploration + review

Impact - Creating a guided method of vertical listing exploration (in AirBnB-curated, specific categories) would build trust and infrastructure of users. The need for open exploration across the vastness of the entire bookable listings catalog would become secondary to users carefully reviewing each 'recommended' listing.

Key Product Metrics to Watch - Time-to-Value (Booking) | Churn Rate

Consideration 02

AirBnB-sponsored listing categories with own treatment + section/space in overhead category section

Impact - Developing a sense of separation and user-specific curation for ALL AirBnB mobile user (mobile Guests). This would extend the hospitality feel that the platform channels through hosts into the technology itself. Curated experiences would also allow for extreme efficiency for users looking for immediate feedback across listings including: 1) quick re-booking on app entry, 2) social proof across listings, 3) quick reference and wayfinding for previously-explored listings.

Key Product Metrics to Watch - Session Duration | Time-to-Value (Booking) | Reactivation MRR

Consideration 03

3 AirBnB-sponsored/user-specific listing categories each consisting of varied listings user-curated across 'Book Again', 'In Network', and 'View Again'

01 - Book Again:

For Guests looking to rebook listings already booked (for work, travel, destination return, or anything else)

02 - In Network:

For groups of Guests looking to book listings for grouped travel, or for Guests simply looking to see how friends/community members are viewing/liking other listings on the platform

03 - View Again:

For those navigating in traditional 'exploration' mode of categories but not always conscious of (or remembering to) Wishlist certain listings that they explore

Impact - User-specific, AirBnB-sponsored listings would curate across above categories as the key sub-segments of 'intent-based' users fall into these general categories. Across each, information per-listing would display with nuances across tags, ratings, and, listing sub-details.

Key Product Metrics to Watch - ARPU (Booking Revenue directly from AirBnB Mobile Platform) | Time-to-Value (Booking) | Conversion Rate (Booking Rate)

Consideration 04

Differentiation between AirBnB-sponsored categories and traditional categories with treatment variation and positioning with in-line navigational divider

Impact - Creating subtle differences between user-specific categories and categories seen by all users would be emphasized with design decisions made across other AirBnB mobile interface elements. This would be constituted across color and spacing/positioning.

Key Product Metrics to Watch - Feature Adoption (across AirBnB categories vs. traditional categories)

Consideration 05

Introduction of a quick filter to manage immediate needs of an intent-minded Guests

Impact - Similar to travelers navigating unexplored land with a map, sometimes exploration can be as impactful with guardrails as with unimpeded discovery. This quick-filter mechanism would embed selection of Room Type, Amenities, and Price Range (as the most-considered filter user decision factors during research). The key here would be in granting guidance and in-line efficiency for users battling expansive exploration of thousands of listings at a time. Similar to how binoculars allow one to focus sensory exploration, here a quick filter would allow for efficient focus for users. Theoretically, getting to the most-relevant listings quickly could be done with curated categories and/or with creating quick guardrails when user-specific, curated listings exploration is still too inefficient.

Key Product Metrics to Watch - Time-to-Value (Booking) | Conversion Rate (Booking Rate) | Feature Adoption (category-only exploration vs. quick-filter-guided exploration)

Note

Treatment here reversed out the current floating 'Map' button and leveraged other AirBnB design system decisions to create a secondary floating button extension. Skeleton categories and listing containers all populated after finalizing quick-filter search results to grant computing space for the system while ushering users along.

Consideration 06

Introduction of a hidden scroll display to manage infinite-scroll user cognitive overload

Impact - With open scroll available on non AirBnB-sponsored category exploration (see: all traditional listing categories), users possessing a method to quickly return to and reference previous listings was found extremely helpful. This would apply for users moving quickly, forgetting to Wishlist a given listing, or users wishing to quickly navigate back-to-top. The 'mini-listings' view available in the scroll display were extracts of the traditional interface listings display with image, location, bookable dates, bookable price, listing rating, and current user Wishlist status.

Key Product Metrics to Watch - Time-to-Value (Booking) | Session Length | Activation Rate (per increase in Listing views/per user)

Note

Treatment of the scroll bar itself mimicked both display and aesthetic of similar button-style elements in the AirBnB design system. The listings shown in the scroll navigation display extracted key, referenceable information researched to remind users of listings they had previously navigated to/beyond. This information was blanketed across traditional 'primary'/'secondary' treatments for limited user cognitive strain.

Overall Interface Consideration

Traditional elements of the AirBnB mobile home screen were adjusted for the purposes of this study. The traditional 'intent bar' (the Search + Filter button elements living in the traditional design header) were shifted down to allow for ease of user access. The general goal here was to make intent-minded features as 'touch friendly' as possible while living in prime touch real estate.

'Where to?' Search Bar

As the main part of the traditional 'intent bar', shifted downwad to live direclty above the navigation bar/footer of the interface.

Filter Button

Shifted downward along with Search button above to maintain intergrity of the traditional 'intent bar' of the interface.

Map Button

Shifted upward to maintain distance from lower navigation bar/footer of interface. Shape adjusted to include space for a contrasting 'quick filter' addend.

Note

Treatment of the scroll bar itself mimicked both display and aesthetic of similar button-style elements in the AirBnB design system. The listings shown in the scroll navigation display extracted key, referenceable information researched to remind users of listings they had previously navigated to/beyond. This information was blanketed across traditional 'primary'/'secondary' treatments for limited user cognitive strain.

**Prototyping did NOT constitute a full recreation of each of these element functions for 2 reasons:

1) scope of this work was the AirBnB home screen only

2) usability + acceptance testing did not necessarily require full recreations to capture touch area of the Search, Filter, and Map buttons

Other Notes

Timeline:

week 1 framing, research, Figma build (AirBnB recreated design)

week 2 Figma rebuild (AirBnB net-new design)

week 3 Figma acceptance testing/rebuild, Figma finalization/cleanup

Methods Used:

UI Paper/Pencil Sketching ideation, lo-fi design, usability research

Card-Sorting research feature priortization

Usability Study early framing research, bias reduction, acceptance testing

Generative AI + UI ideation and UI direction

Tools Used:

Figma design, prototyping, usability testing, acceptance testing

Notion organizing research documentation

Bear App writing and distilling communication

ChatGPT initial text-based design direction options

Paper + Sketchpad deriving drafted visual interface and design direction from ChatGPT text output