Airbnb Booking Analysis


Business motivation

Understanding customers’ dynamics and behaviour is essential for digital companies, especially for those whose business model is based on purchases or reservations from customers.

In the case of a company such as Airbnb, where customers rent rooms or apartments from other users, it is key to understand what drives customers to make the final reservation. If the company manages to understand the patterns, and has a systematic process to analyze the behaviour, it will be able to implement actions to improve the booking ratio, as well as to assess the success of the actions.

For this case, we have selected a public shared file with data from customers, and we will guide you through the different data that the file contains, and the different measures that we find interesting in order to analyze customers’ behaviour.

The raw data file is sourced from airbnb website and contains data from May 2014 to May 2015. It is publically available data put up by airbnb as a part of an analytics competition.

It is very important to understand the columns, let’s review its content:

  • id_visitor: the id of the visitor
  • id_session: the id of the session
  • dim_session_number: the number of session on a given day for a visitor
  • dim_user_agent: the user agent of the session
  • dim_device_app_combo: the parsed out device/app combo from user agent
  • ds: date stamp of session
  • ts_min: time of session start
  • ts_max: time of session end
  • did_search: binary flag indicating if the visitor performed a search during the session
  • sent_message: binary flag indicating if the visitor sent a message during the session
  • sent_booking_request: binary flag indicating if the visitor sent a booking request during the session

Let us look at the data in available form. We have taken first hundred rows of data to display the data:

Conversion rates

Let’s have a look at the conversion rates for the users. We are going to analyze in this case several ratios, such as: - the percentage of visits that end in bookings - the percentage of times that users send a message to the owner - the perc. of times that a booking is realized after a message has been sent

Before any other analysis, let’s look at these ratios (Each block of code corresponds to one ratio) for the global set of customers (without considering the number of session or other parameters):


As we can see, for every 100 visits to Airbnb, only less than 2 end up in a booking. Although we can’t conclude whether this figure is high or low, it will be interesting to keep a record of how this percentage change along time, and whether it changes with specific actions Airbnb takes in marketing.
On the other hand, if we check the success ratio after sending a message, we can see it is much higher than without a message (for every 100 messages sent, over 8 bookings were realized). It would be useful to have more data about what it is that drives the customers to send messages.
If Airbnb could gather the information about why customers send messages, it could develop more targeted campaigns, and could track whether the ratio of messages vs visits increases, and/or whether the ratio of bookings vs messages increases.

Seasonility, visitor and device analysis

In the following we will visually analyze the data by date, unique visitor and device. This helps Airbnb to get a better intuition about who their customers are and how they behave. In the following we will visualize data along the date line, unique visitors and devices/app from which they accessed Airbnb.

Visualization of data by date

Here we grouped all data by date with the goal to see whether we can identify any seasonal trends with regards to customer activity.
First let’s look at the data sorted by date, between 2014-05-05 and 2015-04-23. Below are the first 10 entries:

Looking at the data we see spikes in activity around September, as well as the end of the year and early February. It is interesting to see that while there seems to be some seasonality effect with the number of visits this does not fully translate in a similar pattern for the number of searches.

A similar pattern also holds for messages and bookings which are relatively evenly spread throughout the year.