Posted by Mike Schneider
To be indispensible, an app does one more of the following:
More than ever, apps are now more dependent on having a vital UX. And keeping the user engaged with the app is not only about improving usage metrics, it’s also about increasing revenue. So how can you get there?
As designers and developers, we can insist that a feature is vital. But until you substantiate that claim with real user data, it’s just an assumption.
Start by making a list of which features you think are most important to your app, and then take a look at which features your users actually use. Sort them by number of times used, which equals feature popularity. Are your vital features at the top? Do you know why or why not?
You may not have enough information to answer your question yet. Or enough info to get to your ultimate end goal — which should be delivering personalized app experiences to each individual user.
One amazingly epic thing to do is ask users for feedback on which features they use most frequently — which we recommend doing continuously. Another thing to do is to look at a deeper cut of the data — look at usage by place.
In fact, precise location data has quickly become essential to providing richer insights into your users’ behavior and interests. Armed with this kind of contextual data on your users, you can understand how to make your app that vital part of your user’s daily life.
Get a deeper dive. Check out the Skyhook case study on how the CardStar app doubled its user engagement with location-based context.
Understanding your Vital Ratio (Usage / Visits)
Looking at features by location may be a bit overwhelming at first, so you need to do some aggregation by location types to make it useful. Any app with location enabled can extract this data in the form of location requests. Then overlay the latitude / longitude location information with a venue database to understand in aggregate where your users go.
This can be a laborious, manual process, but on-device software can automatically deliver the contextual data you need. Location-based context will allow you to categorize these places so that you can aggregate information by things that make more sense than looking at usage by each individual coffee shop, retail store, airport and so on.
Once you have this location data, you can then roll up places by Brand (Dunkin Donuts, Starbucks, CVS, Walmart, etc.) or Category (retail, pharmacy, coffee shop etc.) instead of trying to make sense of each address or location point.
Compare your Vital Ratio (which is defined as your app functionality usage / total visits) across these locations and you can see if users are getting the value you expect or if they are finding the functionality they need in the place that they are. Understanding this will help you prioritize how to design for place.
In the example above, you see an app’s specific functionality breakdown by where the user is accessing it. Looking at functionality, usage and place, it looks like users are using the payment functionality of this particular app 70% of the time when they’re in a coffee shop to pay for their purchases.
To increase that number and get maximum usage for that feature, you need to make the functionality as frictionless as possible – at the time those users find themselves in a coffee shop. That means less screens to dig through to use the functionality they want when they want it.
To do this effectively means you must design your app for place — that is, making different modes available for your app when users are in different locations. So, going back to the payment example, when a user enters a Starbucks, the venue’s geofence is triggered, and their app serves up “Buy Mode”, with the payment functionality featured prominently so the user can pay for their coffee in a heartbeat.
In our next post, we’ll talk more about how to categorize your features and the possibilities for adding another layer of personalization using this context data — or what we call Appticipation.