Mobile analytics has become one of the most common and mandatory inclusions in a mobile application - now, a developer would hardly think twice before allocating some of her time to integrate a mobile analytics framework into a new app being built. The effort has always proven to yield results, in terms of giving her information on how many users would try out her application, what their favourite screens are, when exactly they tire of her application and many other parameters that would allow her to improve the application. Chances are a simple integration of any popular mobile analytics framework would get you this results without much hassle (any of the top 5 frameworks you’d find by googling). However, this is only so for general consumer apps on a public App Store - not for your enterprise mobile apps. Why?
Business Context
An enterprise mobile app user would not use a mobile application purely based on his preference. Instead, his user behaviour would be a mix of his own way of doing things but heavily influenced by the business context that he works within. Straight-forward information such as the percentage of active users per day, how many seconds a user would stay on a particular screen, how many mistaken taps he would make, would differ and be dependant on the nature of his work.
How do you get a clear picture of this? Map business context with user behaviour. As an enterprise app developer, you would know in-and-out all the business scenarios that encapsulates each user and screen. Embed this useful data to your analytics tracking and you will then know how users use your app for a specific work scenario.
But that’s lot of work. What would be my ROI?
Good Analytics Means Money Saved
Unlike in any other type of mobile application, each action the user performs in an enterprise app is directly tied to his productivity. If two taps associated with an item on a list screen could be replaced with one swipe gesture, it will result in an estimated 40% productivity increase per action. Your HR team would agree 40% is a big productivity boost and the accounts team would appreciate the reduction of time spent per task.
That’s just a crude example but streamlining your app experience would make your users efficient, happy and more geared for their daily work. This will save you money both in the short-term as well as in the long-term. So you see that mobile analytics can be even more important for enterprise apps than for consumer apps.
Sounds legit. How exactly do I do this?
Customisation of Analytics
The core of enterprise app development is customisation. From splash screen branding to features that hug your ERP, you make your best effort to map your application to business processes. The same practices that you use to map features and business processes together can be applied to your analytics as well. You can encapsulate these parameters in a new layer between your features and the standard analytics, thus giving you the freedom to extend, experiment and tweak your analytics, both conceptually and technically (finally putting that Dependency Injection to use!). Your aim would be to capture how users use your application in their important business scenarios within the contexts in which they operate.
I see. Got any silly examples?
One Silly Case Study
Once upon a time, there was an app to track time spent by professionals on field work. There were mechanics, supervisors and interestingly, 50% of them were both. They had a screen with details of a particular task and two buttons: ‘Log work’ and ‘Verify Work’. The app was just a utility, they hurried through inputs getting their work done. Basic analytics revealed that they would tap on the wrong button (nearly 22% of the times), causing them to just go back and tap the right one where they input values and end the use case. Mistakes at a rate of one in five can be quite annoying.
Many analytic parameters were added to see how can this be fixed. Erick, one young developer with an eye for detail, noticed most of the ‘Verify Work’ mis-taps happened in the morning and vice versa. He found it quite amusing although the product owner couldn’t explain this either. A chat with few end-users later revealed that 80% of the mechanical fixing happens in the morning and 80% of supervisor work happens in the afternoon. That explained it! Based on active time-frame analytics, developers did a silly and a dynamic fix. Without changing anything on the UI (that would confuse users) they overlaid two transparent buttons on top of visible buttons, where the size of the invisible button (hence the touch surface) would grow slightly bigger or smaller based on time and user actions!
You get a larger touch area for ‘Log Work’ in the morning and the same for ‘Verify Work’ button in the evening. Mistaken taps got reduced to 6%. TADA! Will you ever get this feature as a requirement on a spec?
Fine, I think I’m buying this. Anything else?
Analytics on Analytics, Cross Functional Analytics and the Next Up
While mobile analytics will help your good application become a great application, it’s a shame if your analytics stop just there. In an enterprise environment, analytics data from multiple sources put together can create brand new insights. Big data on employee behaviours and performance - what kind of employees, who uses what BI filters on their mobile app, would do most sales - derived from multiple sources such as financial analytics and HR analytics, along with your mobile user analytics would take business into new levels of strategizing that were never possible before. Quite a few possibilities! Now is the time to think of your own analytics in your enterprise business context.
