Everybody knows what AirBnB is and how quickly it managed to win millions of people’s heart by offering feasible and convenient accommodation. But did anybody think about how it all started? Or the struggles that the founders had to experience and jump through countless hurdles? Well, that is what we are here for today, to discuss the complete story of AirBnB and how data science has helped them taking their business to where it is today.
- How it all Started?
- AirBnB and Data Science
- How can you Make the most out of Data Science like AirBnB?
Data Science: R Basics
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- Harvard University via edX
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How it all Started?
In the year 2008, Brian Cheskey who is also the cofounder and CEO back then founded AirBnB with his roommate Joe Gebbia. This business was started with the intention of paying off their rent. But who knew, this not only helped them pay their rent but also turned out to be great business for them. So, let us take a quick look at how is started and then we’ll move to understanding the role of data science in this business.
In the beginning, the founders were unable to pay off the rent of their San Francisco loft. Left with no choice, they decided to turn their living room into a mini bed and breakfast where they could easily host three local guests from a trade show who were unable to find accommodation for themselves. As word spread, people started to talk about it and it became a temporary living quarter for those who couldn’t find a place to stay for local events because of high demand.
So this is how AirBnB grew quickly from a niche site providing convenient accommodations for high profile events.
Now let us take a look at how AirBnB made heavy use of data science to build new product offerings and improved their service.
AirBnB and Data Science
What Cheskey realized is that if they were to use Data science then they could easily prioritize product decisions. And this is what exactly happened and thus can be considered the secret behind tremendous growth of this business. The AirBnB data scientists were able to amplify the voice of their customers with the help of data science. They were able to predict their customer desires through the customer interaction logs and then interpret them into incorporating actionable decision for the product and marketing team.
There are many many data science techniques used by AirBnB to help them grow more and some of them are mentioned below.
Using A/B Testing Methodology
The data science team at AirBnB uses A/B testing to help them understand the behavior of their users. The main purpose here is to make sure that the team is doing a fine job by matching the right people together. So what they do is expose the users of their website to different kinds of recommendation and ranking algorithm and then correlate their behavior with actual ratings or reviews they leave. Thus this helps them test the effectiveness of the algorithm. You can also find out best paying Data Science Jobs at takethiscourse.
Creating Market using Predictive Modeling
AirBnB has to work hard to find out or predict how different markets will perform so as to prioritize the resources accordingly. So what they do is use the predictive modeling technique to help them achieve the purpose. With the help of this data science technique, AirBnB is able to create a market specific forecast with multiple variables. There is a specific team at AirBnB that only has a task to forecast and report so as to optimize the existing predictive models. And this is how data mining at AirBnB makes them capable enough to predict the most suitable rates for their rentals.
Use of Natural Processing Language to Interpret True Feelings
Whenever a customer is being asked of their experience face-to-face, often they are unable to express what they really experienced. This is because they don’t want to offend the staff or be rude. But this prevents AirBnB analyze what the customer really felt. So to interpret the actual feelings of the users, AirBnB has another technique is use that helps them analyze the true feelings of the customer. It is through natural language processing technology. With the help of this technology, they are able to analyze the review boards through sentiment analysis. Now what good can come out of this analysis? Well, if AirBnB is able to find out the actual feeling of a customer or whether they are satisfied or not, they would be able to make amends in the future.
Improved Search Using Data
The heart of AirBnB is its search. The reason behind was to make sure that the customer is amazed and delighted at every step whether big or small. But it wasn’t like this in the beginning. Initially AirBnB was not able to figure out the type of data to give to its customers. So they had to settle on a model that gave them highest quality listings in a certain radius that was based on user search. But this changed after a while when more users came to their site and gave them more data. So that is how AirBnB was able to replace their basic search method with a more advanced and user-data driven one.
Statistical Thinking for Data Science and Analytics
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- Columbia University via edX
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Data Science: Machine Learning
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- Harvard University via edX
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Data Science: Linear Regression
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- Harvard University via edX
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How can you Make the most out of Data Science like AirBnB?
So above were all the important details we had to share about AirBnB and how they use data science to make their business even more successful. But the real question here is that what can we learn from the story of AirBnB and make the most out of data science.
To put into simpler words, all the trends or surprising results that one face is not meant to overwhelm you. Rather it is only meant to help you understand the importance of having raw data and how you can use it wisely. And this is what AirBnB did. They used the data correctly and made hypothesis, new ideas and so much more from it and improved the quality of what they had to offer. And all of this was possible because of the data science techniques. So yes, we must embrace the science that is behind data and not be afraid to dig deeper into it. Only then we shall inspire more and build a learning, adapting, and successful company like AirBnB.
