Predicting Potential Customer Support Needs and Optimizing Search Ranking in a Two-Sided Marketplace
This work addresses the issue of customer support inefficiencies for Airbnb, hosts, and guests, but it is incremental as it applies an existing predictive modeling approach to a specific operational problem.
The paper tackled the problem of predicting customer support needs in Airbnb's marketplace by building a model to assess the likelihood of support required for each guest-host match, and incorporating this score into search ranking to reduce such bookings, resulting in a significant reduction in bookings requiring support.
Airbnb is an online marketplace that connects hosts and guests to unique stays and experiences. When guests stay at homes booked on Airbnb, there are a small fraction of stays that lead to support needed from Airbnb's Customer Support (CS), which may cause inconvenience to guests and hosts and require Airbnb resources to resolve. In this work, we show that instances where CS support is needed may be predicted based on hosts and guests behavior. We build a model to predict the likelihood of CS support needs for each match of guest and host. The model score is incorporated into Airbnb's search ranking algorithm as one of the many factors. The change promotes more reliable matches in search results and significantly reduces bookings that require CS support.