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Airbnb Data Analysis Using R


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This is a short data analysis we did using R and then representation using Excel. Data from Kaggle. Made by Ashley, Graham, Ayman, Anna & Michael.

Published in: Data & Analytics
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Airbnb Data Analysis Using R

  1. 1. airbnb insights & findings 191+ countries 2m+ listings 60m+ guests
  2. 2. Duration of Sessions Bookings Made No Booking Made Lower session duration corresponds with a a larger count of bookings made. As session duration increases, booking count decreases. Longer session duration corresponds with lack of bookings made.
  3. 3. Searches Messages Messages Searches Booking Requests Booking Requests Duration Duration Data shows significant correlations for searches, messages and duration for booking requests. However, there is no one clear contributing factor. By increasing the effect of each factor by 10%, there will be a multiplier effect of roughly 30%. Thus individual strategies could focus on improving these areas marginally rather than focusing on large improvement on a sole area.
  4. 4. SEASONALITY OF VISITS V i s i t s B ro ke n D o w n b y M o n t h - Clear dip in visits between March to May - Emphasizes seasonality of consumer interests in housing - Indicates planning for future trips once the seasons change - Prospect of travel in early months induces more site visits - April – May dip indicates a high travel time for Spring Break and work holidays
  5. 5. SEASONALITY OF VISITS V i s i t s B ro ke n D o w n b y T i m e Airbnb viewership drops between noon and six pm on most days As this period conflicts with prime business hours, we can understand that most customers browse the website during their leisure as opposed to during working hours. Thus those properties featured on the website during those hours could be charged a premium for promotion due to higher user traffic.
  6. 6. Airbnb needs to increase attractiveness of android platforms, in order to capture this market share. They could receive a small amount of credits to offset their first booking when downloading apps or booking through Android platforms to capture this market segment. 0 100 200 300 400 500 600 700 800 Android Phone Android Tablet iPhone iPad Desktop Searches Messages Sent Booking Requests BOOKINGS BY PLATFORM Majority of bookings made on desktops, followed by Apple platforms, and then android.
  7. 7. Website design should encourage users to correspond with hosts. Data shows a higher proportion of messages are followed by bookings. Website should be designed to highlight chat function, and guidelines for hosts should highlight this (i.e. encourage interested guests to correspond with them). CUSTOMER CONVERSION 1.87% 8.39% 16.49% Visits that lead to bookings Of people sending messages Messages followed up by bookings
  8. 8. Anna Pouschine Ashley Yap Ayman Siraj Graham Place Micheal Abushacra