App analytics best practices: Key metrics to track, solutions to use, and implementation tips to remember
Experienced mobile developers know that building a successful mobile app requires more than coding and shipping: it’s an iterative process of build, ship, and learn that the best publishers never finish cycling through. To achieve that, it’s essential to understand how your app is performing, what your users are doing, how you’re really making money, why users are not getting engaged, what’s causing gaps in retention, and any other areas that might need improvement. This is where app analytics best practices come into play. App analytics provides valuable insights into user behavior, retention rates, revenue metrics, and other key performance indicators (KPIs) that help you make data-driven decisions.
In this post, we’ll explore:
- The basics of app analytics
- App analytics best practices
- The importance of app analytics
- Some of the key metrics to track
- Different types of app analytics tools available in the market
- How AI can improve advanced app analytics
- Some popular app analytics tools
The importance of app analytics best practices
App analytics provides valuable insights into user behavior and app performance, enabling data-driven decisions. Tracking key metrics with marketing analytics tools like Singular’s measures marketing campaign success, helping you optimize the user experience and maximize profitably user acquisition and retention.
With performance analytics tools, developers can understand the entire user journey as well as impact and guide it, with the addition of retention, engagement, and live ops tools like CleverTap or Braze. Utilizing app analytics is crucial for improving the onboarding experience — critical for retention — enhancing key features, and increasing app store conversion rates.
App analytics plays a crucial role in understanding user engagement and app usage patterns. By tracking app analytics, developers can identify areas for optimization and improve app performance. Moreover, app analytics provides valuable data for making informed decisions on pricing and subscription plans. It also enables targeted marketing efforts for better user acquisition and helps evaluate the success of marketing channels and social media campaigns.
Key metrics in app analytics
The metrics you care about will depend on the kind of app analytics tasks you have, and the tools you select.
Common categories of metrics include:
- User engagement
A key part of app analytics best practices is selecting the most important metrics for you, your app, and your company’s needs. Retention rate measures users’ continued app usage over time. User engagement indicates interaction and activity levels within the app. Revenue metrics track financial performance, including subscriptions and in-app purchases. Usage metrics analyze session length and active users. Downloads metric measures the number of app downloads.
But all of them are broad categories from which app publishers typically select various specific metrics.
For instance, you might care most about a revenue metric like ROAS, or return on ad spend, for a growing hypercasual game. For a more mature hybrid casual game, or an app that relies on IAP (in-app purchases) or subscriptions, you might care more about ARPA (average revenue per active user) or ARPDAU: average revenue per daily active user.
Retention rate and user engagement
Tracking the retention rate of your app is crucial for assessing user satisfaction and determining the effectiveness of app features. It’s also crucial to knowing how much user acquisition spend you need in order to reach profitability, and how to set your bidding strategies when buying ads: you can’t bid more than your expected LTV (lifetime value), and LTV is heavily limited by poor retention rates.
By measuring user engagement metrics, such as frequency and activity within the app, you can gain insights into how users interact with your product. Using app analytics best practices will improve retention rate and user engagement, which will lead to increased app usage and ultimately drive higher revenue.
Revenue metrics and ROI
Revenue metrics, including total users, subscribers, ad views, and in-app purchases, play a crucial role in determining the financial success of an app.
By employing a platform to track these metrics, app developers will know their return on investment (ROI) for all marketing campaigns.
Furthermore, analyzing revenue metrics provides valuable insights for making informed decisions about monetization strategies and pricing models. With the help of other app analytics tools such as performance analytics tools and mobile ad audits, developers can use app analytics best practices to optimize their revenue and ensure a successful app store conversion rate.
Usage metrics: Session length, active users
Session length measures the duration of user activity within the app, providing insights into user engagement.
The active users metric indicates the number of users who interact with the app, reflecting its popularity. Tracking downloads metric helps evaluate the reach and success of the app. These usage metrics play a crucial role in understanding user behavior and optimizing the app’s performance.
Additionally, crash analytics is critical, and many developers use platforms such as Firebase Crashlytics to track, prioritize, and fix stability issues that erode app quality and drive users away.
One key difference between Android and iOS in-app analytics
Analyzing Android app analytics provides valuable insights into user behavior specific to the Android platform.
Comparative analysis of Android app analytics with other platforms uncovers performance differences that can impact user experience. It’s important to consider the unique aspects of the Android ecosystem when implementing app analytics app analytics best practices for this platform. Additionally, understanding the nuances of iOS analytics can help in developing a comprehensive app analytics strategy.
When analyzing Android app analytics, it is important to consider compatibility testing across various devices and OS versions. There’s just much more diversity in Android devices than in iOS devices, so this is a critical step to delivering a stable, usable app across a broad range of Android phones.
Types of app analytics tools
There’s a wide range of mobile app analytics tools available to track user behavior and app performance. The list includes:
- Basic analytics (users, sessions, etc)
- Performance analytics (speed, crashing, etc)
- Monetization analytics (ad revenue, IAP revenue, etc)
- App store analytics (downloads, placements, rankings, etc)
These tools offer unique features and capabilities, allowing app developers to choose the ones that best fit their specific needs. From in-app analytics tools to AI-based analytics tools and performance and advertising analytics tools, there are various options to explore for comprehensive data analysis.
Basic in-app analytics tools
Gain insights into user behavior and engagement using in-app analytics tools. Track key metrics like app downloads and user retention to optimize performance.
Understand the effectiveness of marketing campaigns and user acquisition efforts. Make informed decisions to improve user experience and drive revenue. Enhance your app’s onboarding experience with performance analytics tools and leverage email campaigns for new user onboarding.
Performance analytics tools
Monitor app performance to identify areas for optimization and ensure uptime, responsiveness, and overall app performance. A slow, buggy app will not retain users and ultimately won’t monetize well.
Monetization analytics tools
To maximize revenue and ROI, you need to measure the impact of advertising campaigns on app downloads and revenue by tracking ad clicks, visibility, and conversion rates: exactly what Singular can help you with.
Gain visibility into financial reports and ROI of marketing channels to optimize marketing efforts. Utilize performance analytics tools to maximize the effectiveness of marketing campaigns and improve app store conversion rates.
App store analytics tools
In some cases, you’ll want analytics tools for your App Store and Google Play rankings, placements, click-through rate, installs, and everything else that App Store Connect and Google Play Console can deliver. In many cases, ASO or app store optimization tools can improve on those tools and give you additional insights that will boost growth.
A key part of app analytics best practices is finding the right tools in the right places to provide the information you need, but not so much you get overwhelmed.
Key Performance Indicators (KPIs) in app analytics
Identifying and defining relevant KPIs specific to your app’s goals is crucial for effective app analytics. There are dozens of common KPIs that will be helpful, but the biggest and most successful app publishers often find themselves creating specific KPIs that are unique to their apps and are more predictive of success.
Measuring user engagement through metrics like session duration and screen views provides valuable insights into user behavior. Tracking conversion rates allows you to understand the effectiveness of your marketing strategy, while monitoring retention rates helps gauge app stickiness and user satisfaction. Analyzing resource usage and app performance optimizes efficiency.
Defining relevant KPIs for your app
Defining the most relevant key performance indicators (KPIs) for your app is essential for measuring its success. To align with your app’s objectives, start with metrics such as average revenue per user (ARPU) and customer lifetime value (CLV). Then focus on KPIs that provide insights into the performance of specific features or user journeys.
Leverage industry benchmarks to set realistic targets and continuously review and update your KPIs as your app evolves, and if necessary create your own metrics that tell your app’s unique growth story.
SDKs and app analytics best practices: choose wisely
SDKs of course play a crucial role in app analytics, enabling seamless integration of analytics tools. SDKs enable tracking of user behavior and engagement within the app, and implementing SDKs allows app developers to monitor key performance indicators and gain valuable insights into user experience and app performance.
But SDKs are getting more complicated.
On Android, SDKs are going to have to live in Privacy Sandbox’s new-for-2024 SDK Runtime. On iOS, SDKs are going to have to declare what data they use in iOS 17’s privacy manifests, and for specific API calls SDK makers will have to specify required reasons.
Selecting the appropriate SDKs is crucial for accurate app analytics. But choosing the right SDK vendors is critical for trust and safety: ensuring that you do not run foul of Apple or Google privacy requirements.
When implementing app analytics best practices, consider the specific metrics and data points you want to track. Evaluate the compatibility and integration capabilities of different SDKs. Choose SDKs that align with your app’s platform (iOS or Android). Additionally, look for SDKs that provide comprehensive documentation and support.
These factors will help you make informed decisions regarding the integration of SDKs into your app.
Common pitfalls to avoid when implementing app analytics
When implementing app analytics best practices, it is crucial to avoid common pitfalls that can hinder the effectiveness of your tracking efforts.
One common mistake is neglecting to define clear objectives and key performance indicators (KPIs) before implementation. Overloading the app with excessive tracking events can also affect its performance.
Adding too many SDKs can do the same!
Another pitfall is failing to regularly review and analyze the collected data. It is important to consider user privacy and consent requirements in data collection, as well as the scalability of the chosen app analytics solution.
Getting the most from your analytics data
Identifying patterns and trends in user behavior allows for targeted optimizations. By using segmentation, you can analyze the performance of different user groups to gain valuable insights. Tracking the entire user journey, from acquisition to conversion and beyond will provide a comprehensive understanding of user interactions.
A/B testing is a useful tool to experiment and improve app features and design. Combining app analytics data with other sources offers a complete view for informed decision-making.
AI is of course the flavor du jour for literally everything, but it can help with app analytics best practices too.
AI has the potential to enhance app analytics by leveraging advanced algorithms. It can automate the analysis of large amounts of app data, identify patterns, and predict user behavior. AI-powered recommendations can optimize app experiences and increase engagement, leading to more accurate predictions and actionable insights. And, AI can summarize insights to make the outputs of analytics platforms easier to digest and implement.
Choosing the right analytics platforms for your needs
When it comes to tracking app analytics, there are several top tools to consider.
Consider using popular third-party tools like Mixpanel, or Firebase Analytics to effectively track your app analytics. Remember to regularly analyze and adjust your app analytics strategy to improve overall performance and user experience.
Consider your app analytics requirements and goals before selecting a tool. Assess if the pricing model aligns with your budget. Look for features like user segmentation, funnels, and heat maps in potential tools. Read customer reviews and ratings to find the best fit for your needs.
And, of course, for user acquisition, attribution, fraud prevention, and more, Singular can help.
App analytics play a crucial role in understanding and optimizing the performance of your mobile app. By tracking key metrics such as retention rate, user engagement, revenue, and usage metrics, you can gain valuable insights into how users are interacting with your app. This data helps you make informed decisions and improve the user experience.
By implementing app analytics and following best practices, you can harness the power of data to drive app success. Make sure to read and understand the analytics documentation provided by your chosen tool and explore the potential of AI in advanced app analytics.