Estimate infrastructure or operational capacity needed for Groups

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 is considering or scaling a Groups experience for families and other multi-person travel parties who plan trips together, compare stays, coordinate preferences, and complete bookings with shared context. The interview asks you to estimate the infrastructure or operational capacity required to support this product area at Airbnb scale.

Frame the estimate around a concrete capacity need, such as backend traffic, storage, notifications, customer support workload, trust and safety review volume, or host/guest operations related to group trip planning and booking. You should define the unit you are estimating, the relevant user population, and the expected usage pattern across discovery, planning, booking, trip management, and post-stay support.

Because Groups touches families, hosts, payments, messaging, notifications, and dispute resolution, your estimate should account for both digital system load and human operational load where relevant. The goal is not precision, but a structured, defensible sizing approach that makes assumptions explicit and tests whether the result is reasonable for Airbnb’s marketplace context.

The experience should consider:

- The scope of “Groups”: family trips, friend trips, shared wishlists, collaborative planning, group messaging, split decisions, or multi-guest booking coordination.

- The unit of capacity being estimated, such as requests per day, storage per trip, notifications sent, support tickets, moderation reviews, or operations headcount.

- The addressable population, including Airbnb active users, family travelers, group-travel trips, repeat usage, and geographic or seasonal variation.

- Adoption assumptions, including what share of eligible trips use Groups and how usage differs before booking, during the stay, and after checkout.

- Frequency and intensity assumptions, such as number of collaborators per group, properties viewed, messages sent, votes cast, itinerary updates, and support contacts.

- Peak-load considerations, including holidays, summer travel, weekends, major events, and last-minute coordination spikes.

- Sensitivity checks around high- and low-adoption cases, larger family groups, international travel, and different trip lengths.

- Sanity checks against Airbnb’s marketplace scale, host supply health, user trust expectations, and comparable travel planning behaviors.

Your goal is to produce a clear guesstimate framework that an Airbnb product or operations leader could use to understand capacity implications, key drivers, and where more data would be needed before committing engineering or operational resources.