“Every time you interact with an Airbnb app or the website, you’re interacting with machine learning in some way or another.”
– Mike Curtis, VP of Engineering, Airbnb 
Founded in 2008, Airbnb has quickly grown into one of the largest players in the travel industry. Airbnb’s business model is simple: it is a global, online marketplace that connects travelers who are looking for a place to stay with hosts who are looking to rent unique accommodations. The company has recorded more than 400 million total guest arrivals, 5 million listings, and 2 million guests per night on average . Airbnb has fueled this growth by utilizing machine learning to solve a complex problem: matching guests and hosts. Despite limited available data for both parties, Airbnb has successfully integrated machine learning into many aspects of its product development process.
In the near-term, Airbnb is focused on utilizing machine learning to (1) personalize search rankings for guests, and (2) optimize pricing for hosts.
Personalized Search Rankings
During the early days of Airbnb, search rankings were determined by a handful of hard-coded, basic variables such as dates, duration of stay, and price . However, as Airbnb scaled its number and tenure of users, it collected valuable data that could be used to predict listing preferences , . During an interview with VentureBeat, Mike Curtis, VP of Engineering at Airbnb, noted, “There’s a bunch of other signals that you’re giving us based on just which listings you click on. For example, what kind of setting is it in? What kind of decor is in the house? These are things Airbnb can use to feed into the model to come up with a better prediction of which listings to show you first.” . While Airbnb launched the personalized search ranking model in 2014, the product has and will continue to evolve over time. Particularly, as the company continues to launch new product offerings (i.e. Experiences), it will capture new data, refine its algorithms, and become even more accurate at predicting user preferences.
One challenge that hosts have consistently communicated to Airbnb is how challenging and time consuming it is to determine nightly rates , . To help address this issue, Airbnb developed a proprietary model to predict maximum revenue per night for listings. This model utilized machine learning to predict the probability of bookings at various price points. The model is based on both external factors (such as hotel rates, seasonality, market popularity, or local events) as well as control inputs from hosts (minimum/maximum prices, frequency of hosting, etc.) . Airbnb combines this data to predict appropriate pricing for a listing. This feature is called “smart pricing” and today uses more than 70 factors to determine optimal nightly pricing , .
While personalized search rankings and price optimization are two near term initiatives, there are many other ways that Airbnb can utilize machine learning in the medium term. The VP of Engineering at Airbnb has identified several initiatives, including: (1) using images to improve search rank, and (2) improving reviews by using natural language processing . Airbnb can use image classification to improve search rankings by ordering photos based on what guests care about the most (i.e. bedroom).
Airbnb can also use natural language processing to improve guest reviews. For instance, reviews often focus on the city that the guest visited rather than the quality of the accommodations. Through using natural language processing, Airbnb can rank reviews based on quality, content and relevance.
While Airbnb’s management has developed key focus areas for machine learning over the next few years, there are many other opportunities for the company to use machine learning. In the near term, Airbnb could further advance its search rank algorithm by using machine learning to analyze guest reviews. Through analyzing reviews, Airbnb could capture valuable data about positive and negative guest experiences. This data could be used to inform the search rank: if another guest had a similar review of a listing, Airbnb can promote or demote that listing based on the guest’s former reviews. In the long term, Airbnb’s vision is to own the entire travel ecosystem: lodging, experiences, transportation and services. To achieve this “one-stop-shop” model, Airbnb needs to “own” the customer long before he or she begins booking the trip. Airbnb should use machine learning to understand the trip ideation process as well as what other services users demand.
As Airbnb amasses more and more users, this data-driven approach will become increasingly complex and increasingly powerful. Can machine learning alone propel Airbnb through its next phase of growth?
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