Excerpted (with edits) from How Airbnb used data to propel its growth to a $10B valuation by Max Song and Carl Shan:
Airbnb head of data science Riley Newman sees the collective data as the “voice” of the customer. Data scientists serve as the megaphone that amplifies the voice of the customer by teasing out their desires from the logs of customer interaction, and interpreting them into actionable decisions for the product, marketing and customer support teams.
Example: As a marketplace, search is at the heart of Airbnb’s matching. However, in the beginning, Airbnb didn’t know what kind of guidance to give customers. So they started with a simple solution, returning “the highest quality set of listings within a certain radius from the center of wherever someone searched.” Then, as Airbnb acquired more data they found that by substituting their initial model with a user-data driven one, they were able to see an increase in customer bookings and satisfaction.
As Riley describes: “We decided to let our community solve the problem for us. Using a rich dataset comprised of guest and host interactions, we built a model that estimated a conditional probability of booking in a location, given where the person searched. A search for San Francisco would thus skew towards neighborhoods where people who also search for San Francisco typically wind up booking.”
(1) Note the job description of a data scientist in a startup: To interpret data into actionable decisions for the product, marketing and customer support teams.
(2) Thank you Guy Cohen for the link.