{"id":34161,"date":"2018-11-13T18:45:09","date_gmt":"2018-11-13T23:45:09","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/airbnb-utilizing-machine-learning-to-optimize-travel\/"},"modified":"2018-11-13T18:45:09","modified_gmt":"2018-11-13T23:45:09","slug":"airbnb-utilizing-machine-learning-to-optimize-travel","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/airbnb-utilizing-machine-learning-to-optimize-travel\/","title":{"rendered":"Airbnb: Utilizing Machine Learning to Optimize Travel"},"content":{"rendered":"

\u201cEvery time you interact with an Airbnb app or the website, you\u2019re interacting with machine learning in some way or another.\u201d <\/em><\/h4>\n
\u2013 Mike Curtis, VP of Engineering, Airbnb\u00a0[1]<\/em><\/h5>\n

\"\"<\/a><\/p>\n

Founded in 2008, Airbnb has quickly grown into one of the largest players in the travel industry. Airbnb\u2019s 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 [2]. 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.<\/p>\n

In the near-term, Airbnb is focused on utilizing machine learning to (1) personalize search rankings for guests, and (2) optimize pricing for hosts.<\/p>\n

Personalized 性视界 Rankings <\/strong><\/p>\n

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 [1]. However, as Airbnb scaled its number and tenure of users, it collected valuable data that could be used to predict listing preferences [3], [4]. During an interview with VentureBeat, Mike Curtis, VP of Engineering at Airbnb, noted, \u201cThere\u2019s a bunch of other signals that you\u2019re 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.\u201d [1]. 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.<\/p>\n

Price Optimization<\/strong><\/p>\n

One challenge that hosts have consistently communicated to Airbnb is how challenging and time consuming it is to determine nightly rates [5], [6]. 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.) [7]. Airbnb combines this data to predict appropriate pricing for a listing. This feature is called \u201csmart pricing\u201d and today uses more than 70 factors to determine optimal nightly pricing [8], [9].<\/p>\n

Exhibit 1:<\/strong> Example \u201csmart pricing\u201d [10]<\/em>
\n\"\"<\/a><\/p>\n

Looking Ahead<\/strong><\/p>\n

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 [11]. Airbnb can use image classification to improve search rankings by ordering photos based on what guests care about the most (i.e. bedroom).<\/p>\n

Exhibit 2:<\/strong> Example of photo classification [12]<\/em>
\n\"\"<\/a><\/p>\n

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.<\/p>\n

Recommendations<\/strong><\/p>\n

While Airbnb\u2019s 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\u2019s former reviews. In the long term, Airbnb\u2019s vision is to own the entire travel ecosystem: lodging, experiences, transportation and services. To achieve this \u201cone-stop-shop\u201d model, Airbnb needs to \u201cown\u201d 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.<\/p>\n

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?<\/p>\n

(777 words)<\/p>\n

Sources:<\/strong><\/em><\/p>\n

[1] VB Staff, \u201cAirbnb VP talks about AI\u2019s profound impact on results,\u201d Venture Beat, June 14, 2017, https:\/\/venturebeat.com\/2017\/06\/14\/airbnb-vp-talks-about-ais-profound-impact-on-profits\/, accessed November 2018.<\/p>\n

[2] Airbnb, \u201cFast Facts,\u201d https:\/\/press.airbnb.com\/fast-facts\/, accessed November 2018.<\/p>\n

[3] Amelia Heathman, \u201cHow AI is powering Airbnb\u2019s mission to change how we travel forever,\u201d London Evening Standard, April 17, 2018, https:\/\/www.standard.co.uk\/tech\/airbnb-artificial-intelligence-21st-century-travel-a3816336.html, accessed November 2018.<\/p>\n

[4] Malay Haldar et al., \u201cApplying Deep Learning To Airbnb 性视界,\u201d Aibnb, Inc., https:\/\/arxiv.org\/pdf\/1810.09591.pdf, accessed November 2018.<\/p>\n

[5] Stephanie Pandolph, \u201cMachine learning is driving growth at Airbnb,\u201d Business Insider, June 16, 2017, https:\/\/www.businessinsider.com\/machine-learning-is-driving-growth-at-airbnb-2017-6, accessed November 2018.<\/p>\n

[6] Airbnb, \u201cHow we used host feedback to build personalized pricing tools,\u201d https:\/\/airbnb.design\/smart-pricing-how-we-used-host-feedback-to-build-personalized-tools\/, accessed November 2018.<\/p>\n

[7] Airbnb, \u201cMachine Learning in Matching & Marketplaces | Tech Talk | Airbnb,\u201d YouTube, published August 29, 2017, https:\/\/www.youtube.com\/watch?v=XiZJfBQqvdI, accessed November 2018.<\/p>\n

[8] Harriet Taylor, \u201cAirbnb launches ‘Smart Pricing’ for hosts,\u201d CNBC, November 12, 2015, https:\/\/www.cnbc.com\/2015\/11\/12\/airbnb-launches-smart-pricing-for-hosts.html, accessed November 2018.<\/p>\n

[9] Airbnb, \u201cWhat\u2019s Smart About Smart Pricing,\u201d https:\/\/blog.atairbnb.com\/smart-pricing\/, accessed November 2018.<\/p>\n

[10] Sharan Srinivasan, \u201cLearning Market Dynamics for Optimal Pricing,\u201d Medium, https:\/\/medium.com\/airbnb-engineering\/learning-market-dynamics-for-optimal-pricing-97cffbcc53e3, accessed November 2018.<\/p>\n

[11] Airbnb, \u201cSharing More About the Technology That Powers Airbnb,\u201d https:\/\/press.airbnb.com\/sharing-more-about-the-technology-that-powers-airbnb\/, accessed November 2018.<\/p>\n

[12] Shijing Yao, \u201cCategorizing Listing Photos at Airbnb,\u201d Medium, https:\/\/medium.com\/airbnb-engineering\/categorizing-listing-photos-at-airbnb-f9483f3ab7e3, accessed November 2018.<\/p>\n","protected":false},"excerpt":{"rendered":"

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