On a very basic level, mobile analytics is similar to web analytics; it will provide you with lots of key metrics detailing how people engage with your app. The number of users, the length of sessions, the OS being used — all of this information will be available to you through basic mobile analytics.
Since this is so vital to their success, there have been many mobile analytics platforms that have sprung up to fill that need. Up until now, the focus of those platforms has been purely on numbers. While numbers are important, they don’t tell us the whole story. I strongly believe that all of these types of analytics platforms will need to adjust from old-fashioned numeric-only analytics, called quantitative analytics, to qualitative analytics.
Ideally, you want to know not just what your users are doing, but WHY they are doing it. The bottom line is that you need to really know what your user is experiencing so that you can optimize it, and scale for massive growth. The cutting edge in-app analytics platforms will offer qualitative analytics. They’ll go beyond the numbers and give you deeper insights into your users and their experience.
One of the most common ways to deploy mobile data analysis is using A/B testing, which lets you track how different incremental variations of the same page or screen perform. Mobile data analysis, or mobile analytics, grants you the ability to try multiple versions and identify how each performs with users and why.
Additionally, mobile analytics is often an umbrella term for a variety of more concise data analytics tools that shed light on different aspects of an app or mobile site. For instance, marketing teams can track different user segments and demographics, grasp how advertising and marketing campaigns are performing in ROI terms, and even assist in automated campaigns.
One great example of this is tracking mobile attribution, which allows you to visualize how user events are connected to different marketing strategies and campaigns. Product and UX/UI analytics track user behaviors and preferences to determine the best configurations of features and tools, as well as find errors and bugs which affect user satisfaction. For instance, an app that has multiple crash reports and complaints can use mobile analytics to determine what is causing the problem, as well as how users are reacting.
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