{"id":28049,"date":"2018-11-11T21:21:20","date_gmt":"2018-11-12T02:21:20","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/airbnb-machine-learning-to-optimize-the-future-of-gig-economy-and-the-power-of-economy-sharing\/"},"modified":"2018-11-11T21:23:20","modified_gmt":"2018-11-12T02:23:20","slug":"airbnb-machine-learning-to-optimize-the-future-of-gig-economy-and-the-power-of-economy-sharing","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/airbnb-machine-learning-to-optimize-the-future-of-gig-economy-and-the-power-of-economy-sharing\/","title":{"rendered":"Airbnb: Machine Learning to optimize the future of Gig Economy and the power of economy-sharing"},"content":{"rendered":"

The Gig Economy and the customer behavior<\/strong><\/p>\n

The world of work has changed dramatically in the past decade, shaped by technological advances and a new generation of customer with very different ideas of what their employment should look.<\/p>\n

The gig economy\u2014driven as much by the economic downturn of 2008-09 as by the influx of Millennials in the workforce [1]. According to the McKinsey Global Institute report \u201cIndependent work: Choice, necessity, and the gig economy\u201d, up to 162 million people in Europe and the United States\u2014or 20 to 30 percent of the working-age population\u2014engage in some form of independent work.<\/p>\n

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

The figure portrays that independent work is rapidly evolving as digital platforms create large-scale, efficient marketplaces that facilitate direct and even real-time connections between the customers who need a service performed and the workers willing to provide that service [2].<\/p>\n

Airbnb maximizing potential of the Gig Economy offering lodging sharing<\/strong><\/p>\n

These part of the population in the gig economy are pursuing a different kind of success. One that comes from finding a balance between predictability and possibility, as well as viability and vitality [3]. There is a new social contract, or new business paradigm, in which customers, especially Millennials, are less incentivized to buy own houses, but are more eager to prioritize experiences and flexibility.<\/p>\n

Millennials are prioritizing their cars and homes less and less, and assigning greater importance to personal experiences \u2014 and showing off pictures of them. It’s a trend that’s ultimately helping fuel growth of billion-dollar-plus start-ups like Uber, WeWork and Airbnb [4].<\/p>\n

With his in mind, Airbnb \u201cconnects people with places to stay and things to do around the world. The community is powered by hosts, who provide their guests with the unique opportunity to travel like a local\u201d [5]. They have 2.9 million hosts, around 800k average revenue per night via Airbnb, with 14k new hosts per month [5].<\/p>\n

In order to maximize the matching between hosts and guests, Airbnb created many machine learning tool. One of them is called the \u201cPrice Tips\u201d [6] in which the computer is suggesting to the host customized prices for each night instead of flat rates. Airbnb believes that dynamic pricing com offer competitive advantage where the host can offer to the guest a good value for money, and consequently maximizing the number of booking nights.<\/p>\n

The tool help hosts to predict better prices. They use the machine learning to predict the probability of booking (based on a binary model). Given the output of this probability, the app gives a price suggestion. Below, are the results achieved for the hosts utilizing the \u201cPrice Tips\u201d tool[6].<\/p>\n

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

Not only has Airbnb managed to reach a scale equivalent to the top five hotel chains combined in less than a decade, but it is now worth twice as much as AccorHotels, with an equity value of $29.3B compared to a market capitalization of $14B (2017) [7].<\/p>\n

Challenges of becoming a giant in the lodging sharing industry <\/strong><\/p>\n

It has been clear Airbnb success in maximizing the returns for hosts and guests. And has today a business of over 30 billion dollars, from which it is actually does not own properties, but the matching connectiveness among users. Currently, Airbnb accepts less approximately 5% of the owners who offer to be hosts.<\/p>\n

However, Airbnb is now focused more than only the matching principle among users, and look towards Legal Challenges. In New York City, the state Attorney General found that, between 2010 and 2014, more than 300,000 Airbnb reservations violated the law, representing about $304 million in booking revenue, with about $40 million of that going to Airbnb [8]. Airbnb argues it cannot legally be held responsible for landlords that use the platform towards their government obligations8, and that they are merely a platform for communication among host and guest. If Airbnb does not adapt their matching machine learning tool, it could drastically influence its revenues.<\/p>\n

Balancing the pros and cons..<\/strong><\/p>\n

It is unquestionable that Airbnb platform brought a new market for a generation of millennials who have greater importance to personal experiences. However, not all bonus comes without onus, and therefore some critical questions we should pose in regard to the subject:<\/p>\n

1. In your opinion, should Airbnb have any responsibility in choosing hosts that are fully committed to tax payments?<\/p>\n

2. Considering Airbnb success is grasping part of markets that already existed, who wins in the Gig Economy of lodging sharing, and who loses?<\/p>\n

3.Is machine learning and the facility of the app connection enough to allow the users (hosts and guests) enough to engage loyalty brand to Airbnb and retain life time users?<\/p>\n

(Word Count: 797)<\/p>\n

[1] Milligan, Susan. \u201cHR THEN AND NOW: 6 trends that have shaped the workplace–and HR – over the past decade.\u201d HR Magazine. August, 2017. https:\/\/www.shrm.org\/hr-today\/news\/hr-magazine\/0817\/pages\/6-trends-that-changed-hr-over-the-past-decade.aspx, accessed November 2018.<\/p>\n

[2] Manyika, James; Lund; Bughin, Jacques; Robinson, Kelsey; Mischke, Jan; and Mahajan, Deepa. \u201cIndependent work: Choice, necessity, and the gig economy\u201d. McKinsey Global Institute. October, 2016. https:\/\/www.mckinsey.com\/featured-insights\/employment-and-growth\/independent-work-choice-necessity-and-the-gig-economy, accessed November 2018.<\/p>\n

[3] Petriglieri, Gianpiero; Ashford, Susan; and Wrzesniewski, Amy. \u201cThriving in the Gig Economy\u201d. HARVARD BUSINESS REVIEW. Mar\/Apr2018, Vol. 96 Issue 2, p140-143. 4p. 2 Illustrations.<\/p>\n

[4] Uptin, Saiidi. \u201cMillennials are prioritizing ‘experiences’ over stuff\u201d. CNBC. May, 2016. https:\/\/www.cnbc.com\/2016\/05\/05\/millennials-are-prioritizing-experiences-over-stuff.html, accessed November 2018.<\/p>\n

[5] Airbnb, \u201cAbout Airbnb\u201d. https:\/\/www.airbnb.com\/host\/homes?from_footer=1, accessed November 2018.<\/p>\n

[6] Airbnb, \u201cMachine Learning\u201d. https:\/\/www.airbnb.com\/careers\/departments\/engineering\/machine_learning, accessed November 2018.<\/p>\n

[7] CB Insights. \u201cReinventing The Hotel: How One Of The World\u2019s Largest Hotel Chains Is Meeting The Airbnb Challenge Head-On\u201d. October, 2017. https:\/\/app-cbinsights-com.prd2.ezproxy-prod.hbs.edu\/research\/report\/accorhotels-teardown-expert-intelligence\/, accessed November 2018.<\/p>\n

[8] Somerville, Heather; Bellon, Tina. \u201cAirbnb\u2019s Legal Challenges Get Real\u201d. Money Magazine. October, 2016. http:\/\/time.com\/money\/4541529\/airbnb-legal-challenges\/, accessed November 2018.<\/p>\n","protected":false},"excerpt":{"rendered":"

Airbnb has focused in delivering a product that boosts the actual volatility, uncertainty, complexity and ambiguity of the market scenario by addressing the value in \u201cexperiences\u201d for millennial generation<\/p>\n","protected":false},"author":11673,"featured_media":28050,"comment_status":"open","ping_status":"closed","template":"","categories":[2536,282,2396,4304,1130,346,1155],"class_list":["post-28049","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-business-intelligence","category-business-scalability","category-digital-technology","category-gig-economy","category-leveraging-scale","category-machine-learning","category-matchingservice","hck-taxonomy-organization-airbnb","hck-taxonomy-industry-technology","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\nAirbnb: Machine Learning to optimize the future of Gig Economy and the power of economy-sharing - 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