For Hopper (an app that tells you when to book and when to wait for flights and hotels), the ability to narrow down flight search results by hiding undesirable flights was one of the most asked for features.

When they finally shipped the feature, however, they realized that filters weren’t what users wanted at all. They wanted something much more basic: content that was relevant to their unique situation, with the minimal number of steps required to get it.

Give The Users What They Want

Since the early days of Hopper, users wanted more control over what results they saw and the app notified them about.

At the time, the price notifications only informed users about the lowest available price. Sometimes, that price could be the result of a low-cost carrier or a bare fare, with lots of added fees. Sometimes, it was because a flight had very long layovers (upwards of 18 hours, at times) or several unnecessary stops.

Less relevant content meant more noise for their users, less value from the solution, and lower conversion rates. Hopper couldn’t ignore this problem any longer. They had to give the users what they wanted.

Focused Filters

There are countless examples of filters in travel and e-commerce.

Filters for Kayak, SkyScanner, Hipmunk

Hopper decided not to re-invent the wheel and planned for a simple “filters” button in the app. This button would trigger a filters list, where users could apply what they wanted.

Some digging taught them that over 80% of filter requests would be addressed with only 3 filters:

  • Remove Basic or “Bare” fares from low-cost carriers
  • Remove Stops
  • Remove long layovers

They went for simplicity and decided to go only with these three.

Once they released the filters, they sat back, waiting for the love and praise to pour in. Fast Company even wrote about it, using the flattering headline: “Hopper’s new filters make it easier to book the cheapest, most optimal flight.

To their surprise, what followed was a bit anticlimactic. They found a dismal 2.6% of their total watches had filters applied.

Were they mislead about the importance of this feature by a small and vocal minority of users?

Was their implementation not discoverable enough?

But, they also had another data point. They found out that filtered watches had higher conversion rates than unfiltered ones. This was the motivation they needed for another go; this time, to increase adoption.

To make sure users weren’t missing the feature completely, they knew they could make the button larger and more prominent in the interface. But, instead of making the button to filters easier to find, could they make the filters themselves easier to find?

They made filters a tip and a more integrated part of their interface. They placed a new card on the prediction screen to suggest filters based off of the user’s unique route. They used dynamic copy to help them weigh the financial trade-offs of applying the filters.

To apply the suggested filter, the user simply tapped a button, right in thumb’s reach. Instead of going to the filters screen, Hopper automatically applied the filter on tap. If there was another filter worth suggesting, it would display that one next. Tapping it would then apply the new filter too. Every time a filter was applied, the app updated the user’s watch with the new settings. No need to enter a menu, no need to scan a list of options.

After releasing the new format, Hopper saw the number of watches with filters go from 2.6% to over 23%! The change resulted in a nearly 10x increase in their conversion rate.

Conclusion

Give the users what they >don’t know< they want.

People don’t want filters. They don’t want a list of even more options to sort through. People want better, more relevant content and help making difficult trade-offs, in as few steps as possible. Can you blame them?

Users Don’t Want Filters, They Want Better Content

PS. From my experience, people usually want filtered results, but don’t want to apply filters. Every time when someone applies the filters (or they are applied for them), the conversion rate grows. It’s a fine balance.