Building confidence in long-stay decisions
AIRBNB
Interviews revealed that evaluating long stays was fundamentally a problem of fit, comparison, and trust.
Generative Research · User Interviews · n=8 · Independent
The tension
A longer stay is not a weekend away. For digital nomads, one booking has to work as a home, an office, and a base in a new city.
How might Airbnb help people evaluate whether a stay will truly support their work and daily life before they commit?
Research frame
8 Airbnb users
interviewed
1+ longer stays
booked in new locations
2 contexts
work + living
I explored how people search, compare, verify, and recover when a longer stay does not match expectations. Participants identified as digital nomads and consistently booked longer stays in new locations, sometimes new countries.
Synthesis
I synthesized the interview findings into three decision needs: match, compare, and trust. This reframed the problem from finding more listings to verifying whether a stay would support work and daily life. The resulting framework translated user uncertainty into design priorities: enforce must-haves, make tradeoffs visible, and provide evidence behind listing claims.
Search does not consistently enforce the guest’s non-negotiables.
Guests build a manual spreadsheet across listing tabs.
Claims, photos, and reviews do not always provide enough proof.
Findings
Search should enforce needs, not merely suggest them.
A search that looks broad can still fail the guest’s real criteria: total cost, workspace quality, neighborhood boundary, or specific amenities.
Design implication: Treat hard constraints as hard constraints. Let guests distinguish must-haves, preferences, and exclusions.
Tradeoffs are managed across browser tabs.
Guests try to remember which listing had the better price, setup, host, layout, and location.
Design implication: Make tradeoffs visible in one place before guests have to remember them.
Trust requires proof, not more listing copy.
Workspace quality, amenity accuracy, photo recency, room layout, noise, and host reliability can be difficult to verify.
Design implication: Make the guest’s risk legible before booking and preserve a meaningful recovery path after arrival.
Prioritization
I used the match, compare, and trust framework to prioritize the moment with the highest decision burden: choosing a longer stay before booking. This gave the design response a clear scope and connected each recommendation to a specific source of uncertainty.
Concept focus: Help guests establish fit, compare tradeoffs, and verify a listing before committing.
Future opportunities include neighborhood guides, review quality, special-price offers, arrival support, and refund and rebooking experience.
The design response: Stay Fit
A longer-stay experience that makes fit visible before guests fall in love with a listing.
Set hard constraints, preferences, and exclusions.
See the meaningful differences across top options.
Assess proof behind photos, amenities, host, and location.
Evaluation plan
The next step is testing, not claiming impact that has not yet been measured.
- Decision speed: time to identify viable stays that meet all required criteria.
- Decision confidence: confidence in knowing what the guest will receive before booking.
- Comparison effort: listings and tabs opened before selecting a top option.
- Listing accuracy: reports that a stay materially failed to match key claims.
Prototype usability testing would validate the flow; operational metrics would assess whether the experience earns trust over time.
What this project demonstrates
I translated interview findings into a trust and verification framework, giving the design response a clear basis for prioritizing pre-booking decision quality.
- Start with the decision: long stays are high-stakes work-and-life choices.
- Synthesize to a point of view: match, compare, and trust became the strategic frame.
- Scope the response: the concept focused on pre-booking decision quality, not a platform overhaul.