Define and Increase New-User Activation for Airbnb
Company: Airbnb
Role: Product Manager
Category: Product Design & Strategy
Difficulty: medium
Interview Round: Other
You are a product manager at Airbnb, and your goal is to raise the activation rate of new users. You may decide what counts as activation. State your definition, then explain how you would improve the rate.
```hint Choose the first value moment
Airbnb has guests and hosts, and each side reaches its first real value at a different moment. Pick one side, define that moment with a time window you can measure, and find where new users stall before they reach it.
```
### Clarifying Questions
- Should the work focus on new guests, new hosts or both?
- Do we already know where new users drop off: for example, do many sign-ups never search with dates, or do many search but never book?
- Do first bookings of Experiences count, or only Stays?
- Are incentives such as first-booking coupons in scope, or should the improvement come from the product itself?
### What a Strong Answer Covers
- A measurable activation definition with a time window, and why that moment matters for Airbnb's marketplace.
- A chosen segment of new users and the specific barriers that stop it before the first booking or listing.
- Solutions mapped to those barriers and prioritized by impact, confidence and effort.
- A primary metric with leading indicators and guardrails on stay quality and on hosts.
- The risk of inflating activation with low-quality or quickly cancelled first bookings.
### Follow-up Questions
- How would your plan change if activation meant a new host's listing receiving its first booking?
- Activation rises after launch, but the new cohort's repeat bookings within 90 days fall. What might be happening, and what do you do?
- Many new users take weeks to book a trip. How would you design the experiment so you can read results quickly?
Overview: A product sense question about raising the activation rate of new Airbnb users, where the candidate chooses how activation is defined. It tests picking a measurable first value moment, choosing a user segment, diagnosing where new users stall, prioritizing fixes, and setting success metrics with guardrails.