The market for mobile apps is flourishing.
There are 2.2 million applications available in the App Store and 3.5 million in the Google Play Store – the competition for app users’ attention is fierce.
It’s predicted that by 2022 mobile app downloads will reach 258 billion, which represents both a huge opportunity and a huge challenge for software companies.
Why is that? Switching between apps is easy. In fact, one in four mobile users abandon an app after one use – how can you ensure higher retention rates?
You can achieve it by implementing mobile app analytics and optimizing your tactics.
How to analyze a mobile app?
Before you begin the process of analyzing your mobile application, there are a few important things to know about how it works.
How does mobile app analytics work?
The mobile app analytics process works by tracking each unique user, categorising them by different data points like demographics and browsing behaviour. The technology used for this tracking differs between solutions.
There are many different types of data that these platforms track. Typically they include:
- Log-in and/ or log-out data
- Device information
- Screen views
- Source data
- Strings of action
- In-app actions and user behavior
This data is then used to determine important information for the optimization of services with the goal of retaining customers or winning new business.
Using app store data and behavioral information, you can identify:
- How long users stay on the app, or on a certain screen
- What draws them there in the first place - e.g. do push notifications work?
- Which features they interact with the most - such as in-app purchases
- Where users tend to encounter problems
- Which factors or features encourage higher app usage
- Which features correlate with higher purchases
All of this information enables product development teams and marketers to create a positive feedback loop.
It can assist in redeveloping your app details or launching new marketing campaigns, based not just on guesswork but on how users actually respond to mobile app features, boosting customer acquisition and retention efforts.
What's the difference between SEO and ASO?
SEO stands for Search Engine Optimization. ASO stands for App Store Optimization.
The difference between SEO and ASO is that SEO is the process of improving ranking in a search engine for a website or piece of content, while ASO focuses on improving app store performance in a specific app store - such as Google Play Store or Apple App Store.
What is performance analytics?
Mobile app performance analytics is a process that puts business data through powerful, intelligent engines to provide data insights into how certain aspects of a business are performing.
These insights then help business teams to uncover new areas of value or identify opportunities for optimizing existing services.
Performance analytics works by collecting, tracking and analyzing data and translating it into a visualized form so that business leaders can learn from and act on it.
Why Mobile App Analytics?
User Analytics for mobile apps have two main purposes:
To improve conversions and make more informed business decisions
Using app analytics is the only way to find out who your valuable users are, how they engage with your app, and why they churn - this can also be called behavioral analytics. It can help you identify parts of your app which drive most conversions, as well as reveal stages where conversion drops (think of e-commerce apps).
All of these insights will help you make better business decisions in the future.Dashboard in Firebase
To create remarkable user experiences and boost customer satisfaction
People have zero tolerance for bad user experience, which isn’t surprising considering the number of alternatives available to them. 52% of users admit that bad mobile experience negatively impacts their brand perception.
Use mobile app tracking to improve user experience with real-time analytics. Discover the most widely used features, as well as the underused ones.
Use product analytics features to check if there are any features missing or if there are features in your app which users find confusing. This is a great source of information that should drive your product roadmap development.
Mobile App Analytics Tools
Now that you’re aware of the benefits of mobile analytics, it’s time to discuss what mobile app analytics tools you can use for data tracking. There are plenty of free app analytics options available, here are a few that you can consider:
- Firebase - a mobile development platform created by Google which tracks user behavior data including:
- Events – what is happening inside your app, for example user actions, system events, and errors,
- Properties – mobile attribution for defining user segmentation, like language preference or geolocation;
- Mixpanel – allows you to track individual user interactions;
- Google Analytics – especially useful for marketers as it lets them identify lead sources, highest converting channels, and split customers/users into segments;
- Smartlook – provides you with data on user behavior by offering screen recording and heatmaps. Useful for identifying areas in your app where users struggle, it can also help you with conversion funnel optimization.
- Adobe Analytics - provides customizable visualizations with industry-leading data analytics. Also adds predictive analytics and powerful attribution capabilities.
- Flurry - Flurry is completely free, and allows you to track installs, sessions and run on-demand analytics. Useful for apps of any size.
- Localytics - Specializes in delivering personalized mobile app campaigns
- Amplitude - Amplitude Analytics democratizes data by providing self-service, qualitative analytics features, enabling employees to identify personas, spot friction, and truly understand conversion data
Read more about mobile analytics tools here.
How To Track Mobile App Analytics
Irrespective of the tools you use, it’s worth checking they have the following features:
Filters are a must-have for data segmentation. Without them it’s hard to draw any conclusions. What can you filter by?
- Platform usage: iOS vs Android
- Date: last week, last 30 days, or even a specific date
- Audience: you can split your users into segments based on shared traits
- User property: filtering users based on their age, gender, device, app version, etc.
It tells you the number of app installs for your app. By monitoring the number of downloads you can verify the effectiveness of your mobile marketing analytics efforts like:
- your targeting – are you focusing on the right audience?
- your messaging, especially your Unique Value Proposition – does it appeal to your prospects or do they find it confusing?
- your App Store or Google Play listing – is it attractive enough to the prospects?
Few downloads or lack thereof might indicate that your marketing strategy needs more work.
This metric differs from app downloads as it tells you how many users actually use your app which is a good engagement indicator. It’s important to define what an active user means to you.
The definition of an active user varies between tools. For example, Google Analytics views this metric through the prism of user sessions. For Google Analytics, an active user is a user who starts a session. This is sometimes called Smart Session Tracking.Dashboard in Firebase
Average visit time and screen views
Average visit time tells you how long the user spent in your app, while the number of screen views shows you how many screens they interacted with.
Both of these mobile app metrics are a good indicator of your user engagement. Think about this way: the longer the user stays in your app and the more screens they see, the more engaged they are.
It shows you the flow of users, from user acquisition to conversion. You can check how many steps your users take before they convert. If you offer in-app payments, you must define the conversion steps to create a sales funnel within your app.Funnels in Firebase
The total sales your app generated from all revenue sources. You can verify your most profitable sources and check what the average revenue per user and per paid user was for your chosen time period.
Adoption and acquisition
It tells you how much you spend to acquire new users. This might include mobile advertising - such as Google ads or Apple Search ads, PR, customer support costs, etc. You can also check your most effective user acquisition channels and the lifetime value of your customers (CLTV). You’ll want to keep your customer acquisition cost (CAC) below your CLTV.
Retention analytics functions give you the percentage of users who return to your app after their first visit. To reveal why users fail to return to your app, measure your retention rates over daily, weekly and monthly periods.
If your retention rate rises, it means your users find the app valuable. If it drops, it’s worth investigating why. Did you run any updates that might have had a negative impact on your app’s usability? It’s a good idea to ask your users for feedback.New User Retention in Firebase
Measures the percentage of users who stop using your app. It’s especially painful if your highest paying or most engaging customers leave you. According to Localytics, 57% of users churn within their first month. This number increases to 71% by the third month from using the app.
Mobile app analytics can provide you with a wealth of knowledge – if you know how to use the data. One of the best ways to draw data-based conclusions is by using cohort analysis in Google Analytics. It tells you how well your app performs among different user groups, also known as “cohorts”.
Cohort analysis helps spot different behavioural patterns among customer segments. It might show you that your churn rate or retention rate is higher or lower within specific segments. It’s a great starting point for analysis. There are 3 things you should keep in mind while conducting your cohort analysis:
- Cohort type – the date of acquisition you want Google to select when tracking users;
- Cohort size – the timeframe you want Google to consider for user tracking;
- Metric – the data you want to measure, such as retention, revenue, session duration.
A Few Extra Tips For Implementing You Mobile App Analytics
Define your mobile app goals
One of the steps you should take before setting up your mobile app tracking is defining your goals. Is it increasing your revenue, raising brand awareness, generating a specific number of downloads, or maybe a mixture of all of the above? When you decide on your goals, you’ll know which metrics to focus on, which will impact the selection of your analytics tools.
Set up tracking for mobile apps
After selecting your toolkit, you’ll need to configure it. To enable data collection you’ll have to install a tracking code. It’s worth it to appoint a person responsible for setting up the tools and configuring the dashboard so that it displays the metrics you’re interested in monitoring.
Analyze your data using the Mobile App Sources report
Implementing mobile app analytics brings no value without regular crash reporting. After you install tracking in your mobile app you’ll be able to collect and send data to your Google Analytics account, generating interactive reports.
Use the Mobile App Sources report to reveal how users found your app, i.e. what brought them to your download page in iTunes as well as other marketplaces.
Mobile analytics case studies
Pokemon Go is now known as a huge success in mobile gaming, raising Nintendo’s market value by several billion. But it wasn’t always so successful.
The real world exploration game was initially launched as Ingress, a collaborative endeavour between Nintendo and Niantic.
But there was no audience for Ingress. Why? The brand wasn’t established.
The companies were able to use mobile analytics to recognise that a rebranding was needed, and that laying the already-famous Pokemon brand over the structure of Ingress was bound to deliver results - and it certainly did that!
Tinder suffers from the ‘dating app retention paradox’. Whenever Tinder does its job to bring people together, it loses two customers.
On the face of it, this might seem to be a flawed business model, but Tinder’s continuing success suggests otherwise. So how do they do it?
You guessed it - by using mobile analytics. Tinder’s dilemma is the opposite of most apps - as they get better, they lose more customers.
This shows that analytics and understanding retention is about much more than knowing how many active users you have per day.
Tinder instead has established a ‘built-to-churn’ model, using mobile and performance analytics to understand how, why and when customer churn occurs and use this data to segment users into different cohorts, tailoring algorithms and user flows to find the ‘good’ customer churn, optimizing the app’s performance, keeping people using the app and coming back to it if or when things (sadly) don’t work out.
Square devices enable entrepreneurs to accept credit or debit cards anywhere, transforming how and where commerce can be done. With the amount of Square point-of-sale devices out there, their analytics team has to deal with unimaginable quantities of data generated through every interaction.
The solution needed was clear: an accessible, high performance mobile analytics data dashboard to process billions of data points every day at speed, allowing Square employees to focus on deriving value from that data, rather than spend time collecting and sorting it themselves.
The ability to do this enables product managers, marketers and other professionals to easily understand user behavior at a glance, speeding up the process of response and improvement to services.
Avoiding common mobile analytics mistakes
While there are many benefits to mobile analytics, there are also some common mistakes you can make which will reduce the effectiveness of your mobile app analytics.
Just to list a few:
While most businesses aim to be completely objective and data-driven, the reality is that companies are still made up of humans. And humans always have different experiences and viewpoints which can lead to biases - intentionally or not. Amplitude lists a number of different types of biases - from confirmation bias to Bandwagon Effect - which can exist within organizations and individuals.
Cognitive bias can impede rationality and objective decision making, so it’s important to overcome it if you want to perform successful mobile analytics.
The key to doing this is first to acknowledge that biases exist. It isn’t necessarily anyone’s fault or intention, but they do. With this recognition, you can identify organizational structures or processes to mitigate the damage these biases can do to your analytics.
Other steps you can take include anonymizing ideas and continuously updating your knowledge on analytics and app growth.
Tracking across multiple platforms
These days, people use so many different devices, platforms and operating systems that no analytics program is complete unless it is truly cross-platform. If you don’t track user behavior across all the platforms and devices they use, you are failing to get the full picture you need to help improve your mobile app with analytics.
Make sure your analytics platform is set up to collect and standardize data from different platforms and devices.
Choosing the wrong analytics vendor
The popularity and importance of mobile analytics has led to a huge number of analytics vendors and app developers promising to solve all your app challenges in one place.
But once you have made the decision to purchase or subscribe to an analytics solution, you need to be careful to choose the right vendor for your specifications.
Make sure to do your research - you need a provider that can provide the right scalability, within the price range for your budget, and that can also provide access to raw data if you need it.
There are quite a few options out there, but it can be tough to sift through and find the right one. If you’re unsure, give us a shout.
Implementing mobile app analytics
Implementing mobile app analytics is the only way to monitor how your users engage with your app. Gaining insights on user interaction will help you create a great user experience that should positively impact your user retention.
There are plenty of metrics you can track including app downloads, the number of active users, conversion, retention, or churn. However, your metric selection should depend on your mobile app goals. What are you trying to achieve? Boost revenue, raise brand awareness, or both?
Only after agreeing on your goals will you be able to define your metrics and select the right tools to track them. Good luck!