Analyze why Wishlists usage is growing but revenue is flat

Problem Statement Description

Product context: Airbnb is a travel marketplace connecting guests and hosts; its products include stays, experiences, host tools, guest booking, trust and safety, payments, and reviews.

Airbnb’s Wishlists help travelers save stays and experiences while planning trips, comparing options, collaborating with companions, or collecting inspiration for future travel. Recently, the team has observed that Wishlist usage is growing, but revenue attributed to users who engage with Wishlists is flat.

Your task is to frame and investigate this as a root-cause analysis problem. Assume this is happening in a global marketplace context with different traveler intents, trip types, geographies, supply conditions, and booking windows. The issue may relate to user behavior, product changes, measurement, marketplace dynamics, or external travel demand shifts.

Focus on how you would diagnose the anomaly before recommending any action. Clarify what “Wishlist usage” and “revenue” mean, how they are measured, which user journeys are affected, and where the conversion from saved item to booking may be breaking down.

The experience should consider:

- The exact anomaly definition, including time period, baseline, magnitude, and whether the flat revenue is absolute, per-user, per-Wishlist, or per-saved-listing.

- Segmentation by traveler type, geography, platform, trip purpose, booking window, new vs. returning users, stays vs. experiences, and domestic vs. international travel.

- Funnel steps from impression to save, Wishlist revisit, listing detail view, availability check, checkout start, booking completion, cancellation, and repeat booking.

- Instrumentation and attribution checks, including event tracking changes, revenue attribution windows, duplicate saves, bot/spam activity, and cross-device behavior.

- Marketplace factors such as price changes, listing availability, host quality, fees, minimum-night rules, supply mix, and competition from hotels or other travel platforms.

- Behavioral hypotheses, such as Wishlists being used more for inspiration, collaboration, aspirational browsing, or long-horizon planning rather than near-term booking.

- Guardrail metrics such as conversion rate, booking value, cancellation rate, guest satisfaction, host response rate, and trust or safety-related friction.

- How you would prioritize evidence gathering, isolate the root cause, and distinguish correlation from causation.

The goal is to demonstrate a structured RCA approach that identifies where the revenue disconnect is occurring, validates or eliminates plausible causes with data, and defines what evidence would be needed before the product or marketplace teams decide on mitigation.