Analyze why Wishlists usage is growing but revenue is flat at global scale

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 has observed a global increase in Wishlists usage, especially among experience-seeking travelers who browse, save, and organize potential stays or activities for future trips. However, revenue has remained flat over the same period. Your task is to investigate this mismatch and frame a rigorous root-cause analysis for why higher Wishlist engagement is not translating into bookings or revenue growth.

Assume Wishlists are used across trip-planning journeys: early inspiration, collaborative planning, price comparison, destination research, and revisiting saved listings later. The issue may involve user intent, marketplace supply, conversion funnel friction, pricing, availability, seasonality, regional differences, competitor behavior, or measurement problems.

You should approach this as an Airbnb product leader diagnosing a global marketplace anomaly. The analysis should distinguish between real user behavior changes and data artifacts, and should consider both stays and experiences where relevant, without jumping directly to a product fix.

The experience should consider:

- How to define the anomaly: Wishlist creation, saves per user, active Wishlist users, return visits, booking conversion, gross booking value, revenue, and time window.

- Whether the denominator has changed: new users, logged-out users, low-intent browsers, repeat travelers, domestic versus international travelers, and experience seekers versus stay-only guests.

- Funnel segmentation from Wishlist action to booking: save, revisit, compare, share, check availability, initiate checkout, complete booking, cancellation, and post-booking revenue.

- Marketplace constraints: listing availability, pricing, fees, minimum nights, host acceptance, quality, trust signals, reviews, safety concerns, and dispute risk.

- Geographic, seasonal, and supply-side cuts: region, destination type, travel season, urban versus leisure markets, stay length, inventory mix, and host supply health.

- Instrumentation checks: event definitions, tracking changes, bot or spam activity, cross-device attribution, app versus web behavior, experiment exposure, and revenue attribution lag.

- Competitive and external factors: Google Travel, Booking.com, Expedia, hotels, local rental platforms, airfare changes, macroeconomic pressure, and travel demand shifts.

- Evidence needed to prioritize hypotheses, assess user impact, guide mitigation, and prevent recurrence through monitoring and alerting.

The goal is to present a structured RCA plan that identifies the most plausible causes, the data needed to validate or reject them, and how Airbnb should decide whether the issue is measurement-related, funnel-related, marketplace-related, or driven by broader travel market dynamics.