Estimate how many superhosts could adopt Wishlists in one year

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 wants to understand the potential first-year adoption of Wishlists among Superhosts. In this context, “adoption” may refer to Superhosts actively using Wishlists as part of their hosting workflow, such as saving comparable listings, curating local recommendations, organizing aspirational property ideas, or sharing collections that help guests plan trips.

Your task is to estimate how many Superhosts could adopt Wishlists within one year. This is a guesstimate question, so focus on defining the scope clearly, making reasonable assumptions, and building a simple bottom-up or top-down estimation approach rather than trying to know Airbnb’s exact internal numbers.

Consider the global nature of Airbnb’s marketplace, the distinct behavior of Superhosts versus regular hosts, and the fact that Wishlists may have different relevance depending on host type, geography, listing count, guest communication style, and how visible or useful the feature is in the host experience.

The experience should consider:

- The unit being estimated: unique Superhosts who adopt Wishlists within one year

- A clear definition of “adopt,” such as creating, saving, sharing, or repeatedly using a Wishlist

- The estimated population of Airbnb hosts and the subset who qualify as Superhosts

- Differences between single-listing Superhosts and professional or multi-listing hosts

- Likely awareness, eligibility, and feature discoverability among Superhosts

- Frequency of relevant use cases, such as trip inspiration, local guides, guest planning, or competitive research

- Adoption funnel assumptions, including exposure, interest, activation, and retained usage

- Sensitivity checks around high-, medium-, and low-adoption scenarios

The goal is to produce a structured, defensible estimate with transparent assumptions and sanity checks, while explaining which variables would most influence the final number and what real-world data you would want to validate the estimate.