IUI Example – AirBnB

One of the principles that I covered in my recent post on IUI Design Principles, was: “Reduce Cognitive Load”.

Part of the inspiration for that principle was an article I read a few years ago about a pricing tool that AirBnB built for people listing their properties.

The problem, they had discovered, was that critical moment when a person is trying to do decide what price to charge when they’re listing their property:

In focus groups, we watched people go through the process of listing their properties on our site—and get stumped when they came to the price field. Many would take a look at what their neighbors were charging and pick a comparable price; this involved opening a lot of tabs in their browsers and figuring out which listings were similar to theirs… [clip] …some people, unfortunately, just gave up

Dan Hill, Product Lead @ AirBnB

There is really so much to love about the discovery of that insight. At the risk of stating the totally obvious, if people don’t list their properties, the supply side of the market starts to erode, which isn’t good. 🙂

AirBnB isn’t the only company that has to deal with pricing, obviously! I’ve spent a lot of the last 20 years in the Consumer Goods / Retail domains, and think about the number of products on the shelf at your local grocery store, those prices were set by a person.

The awesome thing is how they solved this, and it is a great example of a truly intelligent interface! They actually built a tool called Aerosolve, a machine learning algorithm to provide intelligent pricing recommendations to people listing their properties.

They aren’t the only company that has used algorithms to set prices. Think about ride sharing apps like Uber, Grab or Go-Jek, prices change based on different variables: distance, weather, and demand (give or take). But those are pretty simple, all known quantities and over time develop a tremendous amount of historical data… e.g.  how much more are people willing to pay when it’s raining? In other words, price elasticity.

Setting prices for a property on a site like AirBnB is a little more complicated. In a technical paper that Dan Hill wrote (which is where I got the quote above), he covers both some of the history or Aerosolve as well as some of the challenges in building it.

In addition to all the normal things you’d expect to be factors, like number of rooms, WiFi, seasonality, and so on, there are some other interesting factors that come into play. It turns out that the number of reviews plays a large part in pricing, people are willing to pay more for a listing with good reviews (seem obvious retrospectively).

But what about big events?

Consider SXSW, as depicted in the above graphic, what about the World Cup? Those are obvious big examples, but there are events in cities all the time that don’t get the press of things like those events? AirBnB has to account for those as well.

It’s interesting to note here the origin story of AirBnB was that Brian Chesky came up with the idea for the site when we wanted to attend a design conference, realized that all the hotels in SF were sold out, and decided he could pay for his ticket to the conference by renting an air mattress in his apartment to someone who wanted to come to SF but couldn’t get a hotel room.

One of the other really interesting things is the way they had to deal with geographic boundaries for properties. An early version of their algorithm simply drew a circle around a property, and considered anything within that radius a “similar listing”, but what they discovered was that simplistic view had a serious flaw…

Imagine our apartment in Paris for a minute. If the listing is centrally located, say, right by the Pont Neuf just down from the Louvre and Jardin des Tuileries, then our expanding circle quickly begins to encompass very different neighborhoods on opposite sides of the river. In Paris, though both sides of the Seine are safe, people will pay quite different amounts to stay in locations just a hundred meters apart. In other cities there’s an even sharper divide. In London, for instance, prices in the desirable Greenwich area can be more than twice as much as those near the London docks right across the Thames.

We therefore got a cartographer to map the boundaries of every neighborhood in our top cities all over the world. This information created extremely accurate and relevant geospatial definitions we could use to accurately cluster listings around major geographical and structural features like rivers, highways, and transportation connections.

Dan Hill

What a great example of improving the experience people have using a tool by applying computational intelligence.

One of the things I’m going to cover in an upcoming post is how to discover opportunities for IUI.

In the meantime, I’d love to hear what you think about what AirBnB are doing! Thoughts?