Airbnb increases booking conversions by 5% using machine learning to match property hosts' guest preferences with consumers
Summary:
AirBnB wanted to better better understand what accommodation requests are accepted or rejected by property hosts. Using logistic regression techniques they modelled whether hosts preferred stays that resulted in limited gap nights, lots or fewer stays, and also how far in advance the bookings were made. AirBnB changed their consumer search results to emphasise host properties that would more likely be accepted. This led to a 5% increases in booking conversions. This demonstrated a two-sided marketplace with search results that were a function of not only consumer but host preferences.
Problem:
AirBnB has over 4.5M property listings and hosts. When a consumer requests to stay at a property, the host reviews the request and can choose to accept or reject. Some hosts try to maximise occupancy and prefer stays that result in lots of short gaps that can be hard to fill. Some hosts are not interested in maximising their occupancy and would rather host infrequently. Host preferences seem to vary between big and small markets. And some hosts prefer far in advance bookings to just in time.
- Industry
- Travel and Leisure
- Function
- Sales
- Company Name
- Airbnb
- Vendors
- Confidential
- AI Technologies
- Link to usecase
- link