What Is Mobile Analytics?

What is mobile analytics?

Mobile analytics, also known as mobile app analytics, refers to the process of analyzing how users interact with a mobile application at scale. By monitoring overall app interactions with feature usage, friction points, crash rates, process bottlenecks, and abandonment, mobile analytics provides critical information that app developers can use to improve user experience (UX).

Mobile analytics not only helps uncover app usability issues but also monitors the effectiveness of improvements, allowing app developers to make informed UX decisions based on usage metrics quickly. The better the UX, the more users an application attracts and retains.

For these reasons, mobile analytics should be a vital part of the mobile app deployment and maintenance process, helping ensure that apps succeed and continue to evolve toward the needs and expectations of their users.

Continue reading to dive into the differences between web and mobile analytics, types of mobile analytics, specific metrics you should track, and how teams in an organization can utilize mobile analytics to their benefit.

• Web analytics vs. mobile analytics
  
• Types of mobile analytics
• Mobile analytics metrics
• How different teams use mobile analytics in an organization
• Mobile analytics best practices
  
• Mobile analytics challenges
  
• How to track mobile app analytics
  
• How Couchbase Mobile can help
  

Web analytics vs. mobile analytics

While both are about analyzing how users interact with applications, web analytics is different from mobile analytics. Web apps have very different access and interaction patterns and metaphors than mobile apps. For example, interacting with web apps involves clicking links and scrolling through pages, while interacting with mobile apps is based on gestures such as taps, swipes, and slides. As such, each analytics effort has distinct differences. To break it down further:

• Web analytics measures and monitors interactions like views, ad clicks, top pages, total revenue, and event tracking.
• Mobile analytics measures and monitors metrics like application speed and uptime performance, as well as in-app engagement, monetization efforts, and UX bottlenecks, and must consider the differences between each mobile platform’s user interaction metaphors.

Because of the differences, mobile analytics is not as one-size-fits-all as web analytics. While web analytics metrics apply to pretty much all web apps, many mobile analytics metrics may not apply to every mobile app. Because of this, you should have clear goals and a strategy outlined beforehand to ensure you’re tracking the metrics most relevant to measuring success.

Types of mobile analytics

There are many types of mobile analytics metrics to measure; however, the ones you employ depend on how your app works, its platforms, and the features powering the user interface (UI). Here are a few typical examples:

App performance
Measuring the performance of a mobile app involves capturing things like initial load times, speed of transition between screens and tasks, and error rates. Metrics like these help app developers understand where to spend their time to improve performance.

In-app engagement
Measuring user activities like time spent on a given screen or set of tasks can uncover UX bottlenecks and areas for usability improvements.

App monetization
Many app developers offer free versions and monetize them with in-app purchases and premium for-pay features. By measuring monetization efforts across time and demographics, developers can see what works best for monetization strategies for which types of users.

Mobile advertising
Analyzing the engagement rate of in-app advertising across user demographics benefits marketers, who can gauge the effectiveness of ads based on actual engagement.

Mobile analytics metrics

Metrics to measure for mobile analytics vary, but some of the most common include:

• Downloads or install volume: Tracking the number of downloads for a mobile app gives you a sense of its adoption rate and overall popularity.
• Monthly and daily active users (MAU/DAU): Tracking average user volume in daily and monthly time frames provides you with a sense of the overall usage of an app.
• Retention rate: Monitoring repeat usage over time helps determine app stickiness and popularity.
• Conversion rate: Tracking the volume of users who converted from free to paid or basic to premium tiers helps measure the success of your marketing efforts and provides insight into areas needing improvement.
• Abandon rate: Measuring the number of uninstalls, where they occurred in the app UX, and any associated ratings or feedback can help developers improve issues that lead to abandonment.

How different teams use mobile analytics in an organization

Depending on your organizational role or focus area, you may rely on different mobile analytics metrics and techniques. For example, what’s important to a product team might not be as important to a UX/UI, marketing, or engineering team. It’s also important to consider that upper management may have different goals than teams performing day-to-day tasks and might determine success via other metrics.

Here are some of the ways teams within an organization may use mobile analytics:

• Product managers and their teams rely on analytics to reveal the popularity of specific features and attributes of their apps. Analytics helps them decide where to invest time and effort in innovations and which features to sunset or improve. Through measurement, they can see the effectiveness of changes and adjust quickly.
• UI/UX teams use techniques like A/B testing to measure feature discoverability and determine the best UI paths for optimal user experience.
• Marketing teams use mobile analytics to gauge the effectiveness of app promotions and ad campaigns, allowing them to amplify or adjust messaging and tactics based on analysis results.
• Engineering teams use mobile analytics to understand performance issues and code problems that lead to crashes and poor UX.

Mobile analytics best practices

Following mobile analytics best practices ensures you get the most out of your analysis efforts and don’t overlook crucial issues. Always be sure to:

• Plan carefully and establish goals before beginning a new project.
• Get executive buy-in early to streamline participation from stakeholders and their teams.
• Identify metrics for analysis and clearly define what constitutes success.
• Ensure privacy for sensitive data.
• Take immediate action on analysis findings and track improvement or decline.

Mobile analytics challenges

Mobile analytics challenges often revolve around data. The most common obstacles include:

• Data gathering: If you don’t capture data from numerous devices at scale, you could overlook valuable insights into areas for improvement and potentially lose out on revenue.
• Data volume: You must have a foolproof way to store and handle large amounts of data for analysis.
• Data cleanliness: Data must be clean, consistent, and uncomplicated for traceability and actionable insights.

Understanding these fundamental challenges upfront will help you prepare appropriately.

How to track mobile app analytics

There are many different key performance indicators (KPIs) to consider with mobile analytics; however, the most important ones to focus on are those that determine retention, growth, and abandonment rates. You can use these KPIs to analyze and predict future spending and growth.

A poor user experience is the primary cause of mobile app abandonment. Because of this, it’s crucial to monitor navigation smoothness and speed and measure items like average wait time for common tasks such as installation, updates, and saving state, as well as crashes and unexpected behavior.

It’s also important to track interaction with campaign ads and offers for marketing efforts. You can simplify tracking by segmenting users into demographic categories like age range, geographic region, and occupation. What works for users in some professions or areas of the country may not work for others, so it’s crucial to ensure you analyze these nuances properly to determine alternate courses of action.

Lastly, you should consider why things are happening, not just what happened. If a crash happens intermittently during a specific task, look at the entire progression of the interaction. Are some users tapping too frequently while waiting and overloading your process handling, leading to the crash? If that is the case and the task is prone to frustrating delays, look into making it faster or displaying a wait indicator.

How Couchbase Mobile can help

Couchbase Mobile is helpful for mobile analytics initiatives because it captures data on device and syncs it to Couchbase Capella™ in the cloud, where it can be analyzed in aggregate at scale using Capella Columnar services. Specifically, our mobile offering provides:

A cloud-native database: Capella Columnar allows real-time data analysis on the same platform as operational application workloads. You can quickly act on the information gained from analytics to make changes to your application. It can also quickly scale to meet changing application or analytical needs. This is ideal for the real-time operational analysis required for mobile analytics.

An embedded database: Couchbase Lite is the embeddable version of Couchbase for mobile and IoT apps that stores data locally on the device. It provides full CRUD and SQL query functionality and support for vector search and predictive queries for calling AI models at the edge.

Data synchronization from the cloud to the edge: A secure, hierarchical gateway for data sync over the web, as well as peer-to-peer sync between devices, with support for authentication, authorization, and fine-grained access control.

Additional mobile resources

If you’d like to continue reading more about mobile related to mobile applications and analytics, you can visit our blog and concepts hub and review the following resources:

Capella Columnar product page
Why you need a mobile database
What is native mobile development? (benefits, tools, resources)
Offline-first: A mindset for developing faster, more reliable mobile apps