### “I want to tell you about the time that I almost got kidnapped in the trunk of a red Mazda Miata.”

That was the intro to a talk Joe Gebbia, one of Airbnb’s co-founders, recently gave at TED. You can watch it here to find out how the story ends, but (spoiler alert) the theme centers on trust — one of the most important challenges we face at Airbnb.

Designing for trust is a well understood topic across the hospitality industry, but our efforts to democratize hospitality mean we have to rely on trust in an even more dramatic way. Not long ago our friends and families thought we were crazy for believing that someone would let a complete stranger stay in their home. That feeling stemmed from the fact that most of us were raised to fear strangers.

“Stranger danger” is a natural human defense mechanism; overcoming it requires a leap of faith for both guests and hosts. But that’s a leap we can actively support by understanding what trust is, how it works, and how to build products that support it.

How best to support trust — particularly between groups of people who may not have the opportunity to interact with each other on a daily basis — is a core research topic for our data science and experience research teams. In preparation for Joe’s talk, we reflected on how we think about trust, and we pulled together insights from a variety of past projects. The goal of this post is to share some of the thoughts and insights that didn’t make it into the TED talk and to inspire more thinking about how to cultivate the fuel that helps the sharing economy run: trust.

### Building the scaffold

When Airbnb was just getting started, we were keenly aware of the need to build products that encourage trust. Convincing someone to try us for the first time would require some confidence that our platform helps to protect them, so we chose to take on a series of complex problems.

We began with the assumption that people are fundamentally good and, with the right tools in place, we could help overcome the stranger-danger bias. To do so, we needed to remove anonymity, giving guests and hosts an identity in our community. We built profile pages where they could upload pictures of themselves, write a description about who they are, link social media accounts, and highlight feedback from past trips. Over time we’ve emphasized these identity pages more and more. Profile pictures, for example, are now mandatory — because they are heavily relied upon. In nearly 50% of trips, guests visit a host’s profile at least once, and 68% of the visits occur in the planning phase that comes before booking. When people are new to Airbnb these profiles are especially useful: compared to experienced guests, first time guests are 20% more likely to visit a host’s profile before booking.

In addition to fostering identity, we knew we also needed defensive mechanisms that would help build confidence. So we chose to handle payments, a complicated technical challenge, but one that would enable us to better understand who was making a booking. This also put us in a position to design rules to help remove some uncertainty around payments. For example, we wait 24 hours until after a guest checks-in before releasing funds to the host to give both parties some time to notify us if something isn’t right. And when something goes wrong there needs to be a way to reach us, so we built a customer support organization that now covers every timezone and many languages, 24/7.

One way we measure the effect of these efforts is through retention — the likelihood that a guest or host uses Airbnb more than once. This isn’t a direct measure of trust, but the more people come to trust Airbnb, the more likely they may be to continue using our service, so there’s likely a correlation between the two. Evaluating customer support through this lens makes a clear case for its value: if a guest has a negative experience, for example a host canceling their reservation right before their trip, their retention rate drops 26%; intervention by customer support almost entirely negates this loss — retention rebounds up from 26% to less than 6%.