Airbnb: your home away from home

Airbnb is a multi-sided online platform that enables people (hosts) to list their available space and earn rent while enabling travelers to book unique home stays and save money.  Upon each reservation booked, Airbnb charges hosts a 3% host service fee and travelers a 6-12% guest service fee. With over 2 million listings across 190 countries [1], Airbnb offers more lodging than any hotel chain in the world, without holding any physical assets.

The success of Airbnb relies on power of the network effect: the more guests and hosts the company acquires, the more dominant and valuable it becomes. Furthermore, having a large network enables Airbnb to increase its margin, as fixed costs are spread over a growing customer base. Fortunately, the Airbnb marketplace is set up perfectly as a position feedback loop to take advantage of the network effect. The more users Airbnb has, the more inclined someone becomes to list their own spare room on the site. Likewise, guest who have a good experience at an Airbnb rental often go on to list their own space for rental on the site [2].

Many aspects of Airbnb’s operating model aim to increase users and further take advantage of the network effect. First and foremost, the co-founders obsessed over delivering the best customer experience. They hired a Pixar animator to create illustrations of each stage of what they imagined would be the ideal Airbnb trip, from both the host’s and the guest’s perspective [3]. In order to build trust on its platform, Airbnb built rating systems for both hosts and guests to increase transparency and manage expectations.

To reduce the barrier for someone to host, Airbnb simplified the payment process, so guests could just enter a credit card number and hosts could get paid automatically once the stay was successful [3]. Furthermore, it offers a one million dollar Host Guarantee program to protect the property against damage and give hosts a peace of mind [1]. To help hosts get the most out of their listings, Airbnb is currently leveraging its big data and testing Price Tips, a feature that tells hosts how likely it is for them to get a booking at the price they have on any day of the year and offers price suggestion to maximum booking [4].

Airbnb’s curated content are designed to further increase booking rates and maximize guest browsing experience. At a push of a button, hosts could schedule a free professional photographer to shoot their place for their Airbnb profile [4]. This not only improved the appeal of these listings, it also created a stylish look for the site overall.  The site is further manually and algorithmically curated to show users the best listings, making it easier for them to book a reservation [5].

Two days ago, the company filed paperwork with the SEC to report that it had raised a $1.5 billion financing round back in June. It is believed that the new funding was invested on a $25.5 billion valuation, making the company the third-most valuable privately held startup, after Uber and Xiaomi [6]. Clearly, Airbnb’s operating model is effectively aligned with its business model. As the network effect gets stronger, Airbnb is well on its way to disrupt the hospitality industry.

0716_airbnb3

Sources:

[1] https://www.airbnb.com/

[2] http://northwesternbusinessreview.org/looking-at-airbnb-through-michael-porters-competitive-advantage-framework/

[3] http://www.inc.com/magazine/201412/burt-helm/airbnb-company-of-the-year-2014.html

[4] http://www.forbes.com/sites/ellenhuet/2015/06/05/how-airbnb-uses-big-data-and-machine-learning-to-guide-hosts-to-the-perfect-price/

[5] http://firstround.com/review/How-Modern-Marketplaces-Like-Uber-Airbnb-Build-Trust-to-Hit-Liquidity/

[6] http://siliconangle.com/blog/2015/12/07/sec-filing-confirms-airbnb-raised-1-5b-in-new-funding-on-25-5b-valuation/

Previous:

Soylent – Winning your heart with your stomach

Next:

Mumbai Dabbawalas Vs Top Gear

2 thoughts on “Airbnb: your home away from home

  1. Hey! Very interesting article! Has AirBnb done anything to adapt its operating model to respond to increased regulatory pressures?

  2. Interesting to read about the ways that they actively capitalized on the network effect by doing things to help build it. Is there any point at which the network would be too big and require a change to the operating model?

Leave a comment