The state of mobile analytics
Since the iPhone’s release in 2007, mobile activity has boomed. Mobile device usage has boasted double digit growth every year. Usage data shows that in 2016, it overtook desktop internet usage, and in early 2020, Google announced mobile first indexing, switching their algorithm for indexing and ranking all content on the internet to be mobile first. It is now estimated that by 2023, the global mobile app market will grow to be worth almost a trillion dollars in value.
The mobile revolution
For modern businesses, optimizing their digital presence for mobile is no longer optional. But for organizations whose product is predominantly web based, making the leap to mobile is no simple task: there are key differences between web and mobile. Firstly, while the web is based on common technologies, mobile apps need to support the two major platforms (iOS and Android) which means mobile apps generally cost more to develop. This has led to a fragmentation in technologies: apps can be native (written for iOS and Android), hybrid (written in a mix of native code and web technologies) or written in cross platform languages that compile to native code (such as React Native or Flutter).
Secondly, users behave differently on mobile devices. Let’s take simple interactions. Mobile interactions on smartphone devices are completely different to desktop: users swipe and touch instead of scrolling and clicking, and typing takes longer so users tend to search with fewer search terms. Thirdly, mobile consumers expect entirely different experiences to their web counterparts. Mobile apps tend to be more personalized, faster, more intuitive to use (they are not restricted to the normal navigation of the back button, the refresh button and link clicks on web) and in some cases, can be used offline. These differences are just part of what makes creating a robust mobile experience challenging to a modern organization.
Robust analytics is no longer nice to have
It is essential for businesses to have robust analytics in place to help them understand how users interact with their digital products, and deliver rich,relevant experiences. In their 2016 report on ‘Competing in a data-driven world’, McKinsey estimates that real-time optimization and radical personalization are the two most valuable data use cases for companies to tackle. Both involve developing a deep understanding of user behaviour.
“Data permeates everything that the leading organizations do. Digitizing customer
interactions provides a wealth of information that can feed into strategy, marketing, sales,
and product development.” McKinsey & Company
We have already discussed some of the differences between web and mobile. These pose an additional challenge when it comes to analytics. Since most analytics tools started on web, they naturally have web-centric concepts and assumptions deeply embedded. For example, the session is a core concept in web analytics. But what does a session look like on mobile? And is it a relevant aggregation of activity to look at?
The mobile analytics landscape
Mobile analytics aims to understand user behavior to drive engagement, conversion and retention.
While mobile analytics includes the mobile web, it tends to focus on analytics for native iOS and Android applications. Depending on the use case, there are various tools available in the market to help companies understand how users behave in their mobile applications. Broadly, these use cases can be classified into the following categories:
- Marketing analytics (user acquisition and conversion)
- Product analytics (user engagement and retention)
- Performance analytics (application performance, i.e. load times, errors and crashes)
There are many tools that specialize in one of these three areas, but few that span all three. Similar to Google Analytics on web, Google Analytics for Firebase (and associated Firebase solutions such as Crashlytics), is popular for mobile analytics. However, given some of the additional complexities on mobile (such as marketing attribution requiring the connection of external touchpoints to app installs), it has not established itself as the defacto solution the way Google Analytics has on web. Rather, there are different tools that specialise in the different use case categories. I’ve given a brief overview of some of the popular tools in each category and their main benefits below.
Marketing analytics tools
Mobile marketing solutions such as Branch, Adjust, Appsflyer or Airship allow companies to examine the role their marketing touchpoints and channels play in driving app installs and in-app conversions by leveraging deep-linking technology and the mobile device identifiers. They have the necessary agreements with major advertising platforms (Facebook and Google) to obtain device-level attribution data, or alternatives when device-level data is not available due to privacy measures. They also enable effective marketing automation, whether it’s through ads or push notifications. However, they do not focus on providing in-depth understanding of the in-app user experience.
Product analytics tools
Product analytics tools such as Amplitude, Indicative, Mixpanel, Heap or Pendo enable companies to better understand how their users are interacting with their apps, and therefore build user experiences that are highly engaging. They provide in-depth insights into how users move through the app, how they use different features and what behaviors correlate with conversion and retention. Experimentation tools such as Firebase’s A/B Testing (beta) and Optimizely can power A/B testing as well as the evaluation of its effectiveness to improve user acquisition, engagement and retention.
Performance analytics tools
From uptime to crashes, responsiveness and resource usage, tools like Sentry or Firebase Crashlytics and Performance Monitoring help companies understand their app performance. Ensuring apps are working as expected on all devices is key for user retention: nobody will stick to an app that is slow or crashes frequently.
Given this fragmentation of analytics tools, many companies run multiple solutions in parallel to serve all of their mobile analytics use cases. But because most of the tools discussed only focus on a specific set of use cases, it can be difficult for companies to understand user journeys in their entirety, let alone to align on strategies across marketing, product, customer success and support. We will discuss these challenges in the next chapter.
Mobile as a platform has become too important for companies to ignore. In this post, we explained why we think comprehensive mobile analytics is the key to developing highly engaging experiences for your users on mobile devices, and we introduced some of the tools that are available to help you with this today. In later chapters, we will dive deeper into building a robust mobile analytics platform and explore Snowplow’s approach to mobile analytics. Stay tuned!
This is a 5-part series
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