Airbnb: How Big Data is disrupting hospitality


Name:宋宇婕
Student ID : D0740683

Blog #1 Airbnb: How Big Data is disrupting hospitality
Website: https://www.bernardmarr.com/default.asp?contentID=708


Airbnb is a traveler’s most preferred method to explore a new city and stay in residential spaces. It’s one of the best online homestay networks where you can rent out houses, and it's the closest you can get to live like the locals. Airbnb is a trusted community and is preferred by travelers around the globe. Instead of living out of lifeless pods in exorbitantly priced hotels, tourists now prefer to dwell in homes that are managed by locals. Airbnb helps tourists immerse themselves in the land’s culture.

To accommodate travelers around the world and to help them find the most preferred properties, Airbnb actively engages in extensive research. How can the organization cater to the diverse needs and preferences of travelers around the world? The answer to this lies in Big Data.

  Airbnb uses data to not only improve their service and search, but their hiring practices and customer groups as well. Data is used to determine the appropriate price of a room or apartment, based on a number of variables such as location, time of year, type of accommodation, transport links, etc. 

   Airbnb use an algorithm to help their hosts determine the right price for their offering. This is particularly challenging given the sheer range of accommodation available and when you consider these are real homes, not bog-standard hotel rooms that can be easily rated on a star system. After all, what is desirable in a city apartment (Wi-Fi, good transport links, etc.) may be less important in a quaint cottage (where the guests may prefer peace and romantic decor over Wi-Fi and subway connections).

 To help hosts set the price, Airbnb released a machine-learning platform called Aerosolve. The platform analyses images from the host’s photos (listings with photos of cosy bedrooms are more successful than those with stylish living rooms!) and automatically divides cities into micro-neighbourhoods. The platform also incorporates dynamic pricing tips that mimic hotel and airline pricing models.

 Airbnb have also just unveiled Airpal: a user-friendly data analysis platform designed to allow all of their employees, not just those trained in data science, access to all of the company’s information, and tools to query it with.

Comments

  1. Gary LaiJune 14, 2020 at 10:31 PM

    The article share the information about how Airbnb use big data offer requires a clear understanding of guests’ and hosts’ preferences. features that uses machine learning to help hosts, e.g. Price Tips — a host can look at the calendar to see which dates are likely to be booked at their current price, as well as which aren’t, and get suggestions. Aerosolve — an open source machine-learning system that detects patterns and attempts to use these to see why certain listings command higher prices.

    ReplyDelete
  2. Sally D0740993June 14, 2020 at 10:37 PM

    Sally 何昕朣 D0740993

    Accumulated so far, Airbnb has brought more than two to the world. 500 million tourists. In Taiwan, in the past year alone, it has brought in more than one million international tourists, accounting for nearly 10% of the overall inbound tourist ratio. However, Airbnb's amazing growth momentum, in addition to the original vision of the founder's demand for the housing sharing market, another lesser-known key is the role played by his big data team.

    Each of the over 4 million listings on the Airbnb website has its own characteristics. In addition to the considerable differences in room size, address, and interior decoration, tenants also have their own needs in terms of reception, food, or tourist guides. Considering seasonal factors or large-scale events unique to each region, the permutation and combination will produce almost "infinite" data. The team will first classify the similarity of the source of the room, the newness of the data and the location of the listing. The similarities include the type of housing, the capacity of the room, the shape, and the comments of existing tenants.

    Airbnb uses certain algorithms to help landlords determine the correct price of their house or room. The appropriate accommodation price depends on many data points, such as location, transportation links, type of accommodation, time of year, etc. I find it very convenient. Compared with the price of a hotel, it is often because of the brand, so the price will not be adjusted due to location or transportation. If you take a lot, I would prefer to choose airbnb. Airbnb is more and more prosperous, it is suitable for many people, there are many room types to choose from.

    ReplyDelete

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