It’s not enough to put an app out there and hope clients, customers or employees use it as intended. You need as much information as possible about when they are using it, in what manner and where any points of frustration may be, which is where analytics enters the picture. Designed and implemented properly, analytics as part of mobile app design can help you improve processes, sales—and profits.
Non-technical app users may not understand that information not yet uploaded (i.e., you haven’t hit “send”) can still be available—if the app has been designed with usability and analytics in mind. For example, take a mobile registration screen that has five pages. We’d recommend keeping the number of screens for demographic information to a minimum, but multiple pages are common.
So let’s say the user stops on page three. Maybe he doesn’t want to input his credit card information or forgot the ZIP code for his work address, but for whatever reason, he closes the app without completing the five pages. Every keystroke the user has completed tells a story about the user, about the experience and about the app. Analytics can be used to parse that information to determine what may have happened and facilitate the successful completion of the task.
It’s important to understand why requesting information was not successful. It could be because the form was frustrating—important data to have. Or maybe the user lost connectivity or had to take an important call.
If your app designers are smart, the first questions were demographic in nature, to facilitate communication in case the user drops off before completing the task. If you know how far the user got, you can offer an enticement to complete the task. If it’s a purchase, with items left in a shopping cart, offer a 10% discount. You could send a quick survey to determine why the information wasn’t completed. You even can send a link to the user with the information that already has been input, making it easier to complete the form.
And once that form is complete, you can collect, collate and parse that information—and the data from the other forms you’ve collected—in as many ways as you have questions.
Let’s assume you work for a property insurance company. By looking at the data, you can see how many people are changing residences or not paying their premiums. By parsing the information further, you may be able to see patterns in both trends. It could be a certain socioeconomic class of people or those in a particular suburb or ZIP code.
Much of the data can be viewed in real time or near-real time, giving managers an opportunity to act quickly to capitalize on an advantageous situation or reduce the impact of a negative event.
Over the past few years, financial companies have been using analytics to beef up their security, checking the IP addresses, for example, of devices interacting with their networks. For IP addresses that may be questionable, transactions can be denied or more information requested to validate the user and the device.