Our story begins with a question:

How do you discover new, growing problems from your customers right now?

For a typical person, this seems like a pretty straightforward question that every company should be able to answer. However, we found that it wasn’t so simple.

Of course, we had the data of customer issues stored in our database but we didn’t have a great way to answer the question. We didn’t have a way to bubble up trending problems that were starting and we lacked the ability to see this in real time. Most of the trends we discovered were found on an ad-hoc basis which meant that by the time we discovered a trend, it was too late to act on fixing it. So with that in mind, we began to devise a solution.

To provide some background, since Airbnb began, we’ve handled 80 million guest arrivals and we’re quickly growing. With our rapid growth, our engineering team is finding ways to tackle the new and challenging problems that arise. A large part of Airbnb’s operation relies on having customer service agents to handle the high volume of incoming questions from our hosts and guests — check out this previous post by Emre Ozdemir. One of our challenges is to understand this large volume of tickets and detect trends or unexpected problems as they occur in real time. We need a way to monitor and alert whenever we see an increase in folks calling us about a certain issue.

What we built