Swipe left for no; swipe right for yes: Such is the basic rule of interaction with “Tinder for X” apps. Although the origins of card-based interfaces is unclear, Tinder, a dating app, has become synonymous with the now-ubiquitous swipe gesture.
A basic onboarding rundown of Tinder: you login with your Facebook account to create a Tinder profile, set age and location limits for who you’d like to meet, then begin swiping photo cards left (if you’re not interested) or right (if you are interested). If you get a mutual match, your phone lights up and happily declares that you’ve been matched with another user. Heart icons flutter, and it’s up to you to strike up a conversation.
Shuffling the decks
We use our phones with one hand, and we navigate with one finger – the thumb. As the thumb is to touch-enabled mobile devices what the mouse is to the computer, “cards” on mobile devices become more important as easily digestible units of information. Most mobile users look at their phones hundreds of times per day, but often for only a few minutes at a time. In an age where our attention is dwindling, cards become a great way to deliver information at a glance. Swiping alleviates information overload, and makes interactions simple. As Tinder has shown, the card-based interface is wildly successful. Swiping becomes an instantaneous action, a mere flick of the thumb. It’s particularly well-suited for small-screen mobile devices: a user can hold a phone in one hand and flick cards across the screen with that same hand. Without realizing it, they could have gone through fifty potential matches in less than fifteen minutes. According to a recent re/code article, Tinder now gets 800 million swipes per day and has matched a total of 1 billion users. A lot of apps have implemented a swiping interface similar to Tinder’s, because designers have realized that swiping is one of the most intuitive user interactions on the phone. The physics of flicking virtual cards on a mobile screen is often familiar to users, who compare it to physically turning over a playing card in real life for more information. Let’s take a look at some of these apps, which are all designed to introduce users to a potential something – a potential favorite podcast, a potential puppy you want to adopt, a potential person you’d like to meet, a potential article you’d like to read, a potential job you’d like to have.
“Tinder for X” Apps
BarkBuddy – “Tinder for Dogs”
BarkBuddy is an app from Bark&Co., the company behind the monthly doggie goodie boxes at BarkBox. The app works across the United States and Canada, and its repository of adoptable furry friends are from PetFinder’s aggregated data of adoptable shelter puppies as well as Bark&Co.’s own network of shelters that are involved with BarkBox’s referral program. According to a TechCrunch article, BarkBuddy listed around 300,000 dogs in May. BarkBuddy’s tagline is ingenious – “find fluffy singles in your area.” Like Tinder, users swipe left if they’re not interested or right if they are. The app collects data from each swipe interaction, and in the Stats section of the hamburger menu, users can see how many cards they swiped left to, how many cards they tapped on, and how many cards they swiped right to. BarkBuddy saves all of the cards you swiped right to in a Favorites section, where you can easily view all of your favorite pups and decide which ones you’re truly interested in. Eventually, by spending enough time with the app, users can learn what kinds of dog breeds they are more partial to, which helps when they eventually make in-person visits to adopt their new pet.
Swell – “Tinder for Podcasts”
Swell is an audio/news player from the startup Concept.io that uses a card interface to let users personalize their podcast listening experience. Users open the app, hit play, and content immediately starts streaming. There is an ability to customize what tracks a user would like to listen to, based on certain topics such as comedy or technology. On top of an option to swipe left or right to skip tracks, users can also swipe up to bookmark and swipe down to reveal advanced controls. There’s also a WiFi-only mode, where users can download tracks when on WiFi and listen to them offline.
Coffee – “Tinder for Networking”
Coffee is built by a team of three: Nathan Bernard, Sawyer Xie, and Jason de Guzman. Aimed at connecting the next generation of young professionals across the United States with each other and with mentors based on their interests, users link either their LinkedIn or Facebook profile and swipe for connections. The app follows basic Tinder-popularized swiping gestures: swipe left if you’re not interested, swipe right if you are. If you mutually match with another user, the app invites you to strike up a conversation and eventually to connect over coffee.
Daily by Buffer – “Tinder for Content”
Daily, a content-discovery mobile app, is meant to go hand in hand with Buffer, an app that allows individuals and businesses to schedule and share content on social networks such as Twitter, Facebook, LinkedIn, and Google+. Daily allows users to add up to 5 content suggestions per day, out of 25 shown, to their queue using a simple swiping user interface. Their MVP was built in 2 weeks and their suggested content, according to the blog, is curated by members of the Buffer team. Daily offers content suggestions across five topics – marketing, lifehacking, inspiration, design, and entrepreneurship, and shows a user a curation of 25 articles per day. Swiping right will queue a post, and swiping left will dismiss it. Tapping on the card gives users the option to read the story in-app before they decide if they want to share it with their social circles.
Jobr – “Tinder for Jobs”
Jobr is founded by Hari Ananth and Alex de SImone, and is aimed at simplifying job hunting. Jobr recently raised $2 million in seed funding. Since their launch in May, the app has seen over 3 million swipes, and over 1,000 recruiters from top companies have signed up to connect with talent on Jobr. On the user side, the app shows users jobs it thinks they may be interested in, and like Tinder, allows them to anonymously like or pass on them. If a recruiter from the company is also interested in them, Jobr introduces them and lets them chat about opportunities within the app. On the recruiter side, Jobr lets hiring managers post jobs and like or pass on potential employees based on experience and fit. If a candidate likes one of the jobs posted, Jobr introduces them, and lets them chat about opportunities within the app. Using the two-sided opt-in model allows the Jobr algorithm to filter out jobs that aren’t right for candidates and candidates that aren’t right for a job.
Better decision making
Technology has introduced us to a multitude of options, and wading through all of them is oftentimes tedious. Many apps could benefit from a card-based interface that shows users enough necessary information to make a quick maybe/no decision. Apps can further leverage engagement data – each individual user-swipe interaction – to keep track of what options users have seen and what they are considering, to help users make better choices even faster.
BarkBuddy already does this in their “Stats” section. Here, you can see that I have swiped “no” 49 times, clicked a card for more information 12 times, and swiped “yes” 7 times, along with the breeds of the dogs I favorited.
Swell also uses each user interaction to retrieve data for the algorithm behind the app. The algorithm evaluates content with a formula that’s concerned with many factors: ratings from a curator, ratings from users based on their engagement levels with the audio track, how closely the content and topic matches a user’s specified interests, ratings from users with similar interests, and overall ratings from the Swell user community as a whole. This allows for collaborative filtering so that the app can deliver great content to users.
Card-based user interfaces have a lot of potential, especially when you consider the physics of cards that can be applied to mobile. Cards can be turned over to reveal more information, folded for a short summary, expanded for more details, pinned for memory’s sake, stacked to save space, grouped and sorted by specifics, spread out to view more than one at a time. As an interface, cards may ultimately help users narrow down their possibilities instead of overloading them with possibilities. Content consumption across applications may soon be built on cards as an information medium – on our end, we’ll keep on shuffling.
(This post is part one of a two-post series about card-based UIs and swiping. Check back next week for an analysis of “Why swiping works” and “How to make swiping better.”)