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Mobile App Analytics by Wolfgang Beer

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Chapter 1. Introduction

One of the differences between publishing mobile apps and selling traditional products without a network connection is the possibility to instantly collect usage statistics and context information in real time about individual customers worldwide. Collecting real-time data about all product users is daily business for marketing and sales experts, as well as for the engineers who develop and improve mobile apps.

Today’s competitive situation within global app marketplaces makes it hard for app developers to distinguish their app from thousands of similar mobile apps. Providing a reliable and highly responsive mobile app and showing a good level of attention to details helps you stay on top of your competitors. To know your typical app users and how they experience your mobile app is the first step toward good product reviews and a growing user base. Mobile-user experience monitoring helps publishers understand how customers are using an app and what features they prefer. Without this deep visibility, it is impossible to drive innovation and to improve the usability of your mobile offering.

Often, product innovation arises just by following your customers through their business process workflows and by tracking and measuring key performance indicators. User surveys and A/B tests with a target audience have a long tradition and can answer different hypotheses about your product and functions within different market segments and user groups.

A/B tests are used to measure the difference between two slightly different variants of the same product with the goal of selecting the one that performs best according to a given metric. A/B testing is the typical methodology to decide which variant performs better in terms of number of conversions.

The result of user studies, on the other hand, directly leads to usability improvements, possible products, upselling opportunities, ideas to improve advertising campaigns.

For decades, marketing experts have analyzed and studied target audiences for traditional paper mailings, catalogs, or emails. Digital marketing experts take target audience analysis to the extreme by using global real-time information to observe changes in global usage within minutes; for example, monitoring how users’ navigational behavior changed in real time after an email campaign announced a new feature within your mobile app.

Today, real-time collection and analysis of usage information is not limited to websites and mobile apps; it has been seamlessly adapted for smart TVs, watches, or even personal fitness-tracking devices. Actual usage information helps to better understand how a product is handled by the customers, which parts of the user interface are hot spots, and which parts are never used at all.

Modern analytic frameworks offer a vast amount of different metrics and key perfomance indicators (KPIs)1, but the interpretation is still up to human analysts and involves the definition and testing of hypotheses. The most profound collection of user information is worth nothing without a reasonable formulated hypothesis. There isn’t a generic one-size-fits-all collection of metrics and KPIs; instead, the selection of a feasible set of metrics depends on the hypotheses to check. This report will give a short introduction to important categories of metrics and their application. To dive into the details of formulating detailed hypotheses and correctly interpreting the results would go beyond its scope.

The application domains and questions to answer by collecting usage statistics are manifold, and range from marketing to product management to quality management and testing aspects. While questions around the marketing aspects will most likely focus on ad targeting, conversion rates, and effectivity, quality management’s interests are directed toward stability, heterogeneity of target platforms, and maximizing the quality of products by focusing their testing budgets into specific parts of the product.

This report gives an overview of the different metrics that are collected by instrumenting mobile-native apps. By introducing different use cases and corresponding metrics, readers get a deeper understanding of how their customers experience the usability and performance of a mobile app. The report also introduces the typical instrumentation and publishing process of mobile apps and provides some detail insights about different instrumentation approaches. The goal of this report is to help readers to set up real user monitoring for their own mobile apps and to drive product improvements by choosing the right metric for specific questions.


This report focuses on general vendor- and tool-independent techniques for collecting and analyzing metrics for your mobile app business. All of the metrics mentioned within the report can be collected with any mobile monitoring framework available today. In order to present real-world examples, I’ve used screenshots from popular tools such as Google Analytics, Ruxit, Fabric, and Flurry Analytics.

1 9 Mobile App KPIs to Know, Lauren Drell, Sept 05 2013, http://mashable.com/2013/09/04/mobile-app-metrics

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