Will Rogers once said, “Even if you’re on the right track, you’ll get run over if you just sit there.” This statement aptly summarizes the purpose of mobile app analytics.
Mobile App analytics are the most widely relied upon tools for app marketers and developers. Research suggests that about a third of mobile app professionals will spend their budget increase on analytics tools because they provide crucial data that helps developers optimize the performance of the app while marketers use this data to accelerate business performance.
App analytics is the accumulation and analysis of data that is collected from mobile app and mobile web activity. This data can be turned into information and the information into insight. It can be used to optimize performance and diagnose problems within the app and correct them. With over 3 million apps on the App Store and Play Store collectively, mobile app analytics has become critical to succeed in the mobile app business.
The concept of app analytics has been benefited from so widely that its market is said to grow at a Cumulative Annual Growth Rate (CAGR) of 22.1% by the year 2023, says a market research published by MarketsandMarkets™.
Mobile app analytics serve two main purposes.
1. To improve the conversion rate and make better business decisions App analytics tools help identify who your customers are and how they use your app – what they like or dislike about it; What makes them convert or what leads to conversion drops.
2. To improve the user’s experience and satisfy customers Considering the number of applications available, app users have a low tolerance for poorly performing apps and will switch to a better option, if available. Mobile app tracking helps identify the most used features, the rarely used ones, missing features or features that the user may be struggling with. This helps in improving and evolving the product’s functionality.
Not every mobile app requires the same app analytics tools. This is because all apps function differently and have a unique set of goals. While some may focus on revenue, others may want to create awareness or even generate a high number of downloads. Once the goal (or a set of goals) is determined, it becomes much easier to pick the appropriate app analytics tools. The most common information that can be derived from these tools are:
This helps determine the channels that users came from.
This is the ratio between the number of mobile app users who continue to use an app after a certain period of time.
This is the number of people who open your app repeatedly after downloading it.
This determines the number of mobile users who open an app on a daily basis.
This refers to those who shared their app experiences with others by posting a link to social media like Facebook, Twitter, etc.
This is the number of monthly users who open the app on a monthly basis.
This helps determine the time spent by users within the app.
This refers to the monetary income from the user’s activity
This is the process of making major or minor tweaks into a conversion goal.
Funnel analysis the process of using a series of events that lead towards a defined goal. This is an effective way to calculate the conversion rate for specific user behavior.
Given all the options on the app analytics tools market, it may be slightly browbeating to pick the ideal stack for your business. We have compiled a list of some of the most commonly used app analytics tools to help you decide better:
Google analytics is one of the most trusted and widely used free tools that works really well for in-app analytics. It also has a premium version designed specifically for larger enterprises that provides more detailed information and analysis of data.
Google Analytics helps to understand and measure the following:
Number of users in the app, their in-app behaviour, their demographic and their geographical location.
What actions are the users taking within the app.
In-app payments and revenue generated.
Google Analytics provides a unique feature called ‘Cohorts’ that helps you determine how your app performs among different user groups, also known as “cohorts”. Cohort analysis helps spot different behavioural patterns among customer segments and is 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.
Apple Analytics is a free tool that comes included with the Apple Developer Program membership and does not require any implementation. It is a great tool to provide acquisition and attribution data, usage, sales and App Store data. Unfortunately, it hits the ceiling at iOS apps and does not integrate Android apps. This tool could be beneficial to get a snap-shot of your iOS app but it is strongly recommended that you supplement its usage with another tool for in-app analytics.
Flurry, Launched in 2008, this platform is one of the first mobile apps analytics providers on the market. It has a great reputation and is trusted by over 1 million active apps – from start-ups to industry leaders. Flurry provides powerful insights ranging from usage, engagement, retention rate, geography and demographics of audience to techno graphic metrics. Flurry allows you to track up to 500 events along with unlimited parameters.
For advanced users, Flurry includes custom querying for fast, on-demand data exploration, segmentation, user paths and funnel analysis. It also helps with Crash Reporting, Push Notification Management and Remote Configuration.
UserExperior provides the platform for recording in-app user sessions and heatmap tools that helps you view the whole experience from the user’s point of view. The monitoring, capturing, recording and replaying of the user’s session helps you keep track of all the activities in proper order. It also provides insights on how the app performed during the session. It has another unique robust feedback mechanism that helps you understand the reasoning behind the user’s actions within the app.
Localytics serves a dual function as that of a marketing tool and analytics tool. As a marketing tool, it allows the feature to send push notifications to segments and individual users and as an analytics tool, it tracks retention metrics and attribution models across your apps.
It is an excellent tool to track the following metrics
Sessions and Events
Mixpanel is a popular one among the mobile app developers particularly because it tracks user interactions and creates custom reports. It can also be used for targeted marketing like push notifications. It allows you a live view of the user’s interaction with the app in real time as well.
It is an event based mobile analytics tool, so you have to define the events and event properties for your app. After that, it helps do the following:
Track these events separately or create series of events (funnels);
Use cohort analysis to see precisely how often users come back and get engaged with your application;
Build complex queries based on events and demographics.
Countly is a convenient, all-in-one mobile app analytics platform that helps cover all the important metrics that you should keep track of as an app developer/ marketer. It also has the feature to keep account of how customers are using the app for up to 10M unique identities in real time. It also collects information on User Profiles, Attribution and Segmentation.
Countly is relied upon by some big players like L’Oreal and Intel. It offers a free package for up to 10,000 monthly sessions and all versions track up to 90 different statistics.
While different mobile app analytics tools serve different purposes, they all share a common set of features across all of them, as listed below.
The first step into starting an analysis is filtering out the data based on what information you want to see. You can filter on the following premises:
Platform: The App’s Version – iOS or Android;
Date: The dates starting from today, dating back to this week, 7 days ago, last 30 days or even a custom date range;
Audience Category: Audiences that share similar traits (ex: users, purchasers, etc.);
User Property: Age range, app version, device model, gender, ads frequency, etc. of the user.
In most cases, most mobile app analytics tools allow you to download a CSV version of your filtered data as well.
This is one of the important metrics that helps determine how much you spend to acquire a new customer. It includes several marketing aspects like PR, customer support costs, advertising and so on. It helps you determine the most effective acquisition channel for your business and check the Lifetime Value of your customers.
This metric tells you how many people actually downloaded the app, thus indicating the effectiveness of your marketing efforts. It tells you whether your targeting is focused on the right audience or not; Whether your Unique Value Proposition appeals to the prospects or they find it too complicated and whether your App Store and Google Play Store listing is attractive enough for prospective users.
A lack of users may mean that your marketing needs to be aggravated.
This metric must not be confused with App Downloads as it tells you how many users actually use the app as opposed to the ones who have simply downloaded it. You can visualise the usage over a period of time like daily, weekly and monthly active users or in real time over the last 30 minutes. You can also track notable conversions like the user opening the app for the first time, completing a tutorial and so on.
This metric shows you the step-by-step process of the user from acquisition to conversion. It tells you the number of steps taken by the user before they actually converted. If the app includes in-app payments, you must define the conversion steps to create a sales funnel within the app.
This metric indicates the percentage of returning users to your app. It is a great barometer to measure the retention rates over daily, weekly and monthly time frames. If you see a rise in returning users, it shows that the app is valuable to them. On the contrary, if you see falling rates, you may want to investigate the cause by collecting user feedback.
You can also check on the specific characteristics of your users in terms of their –
Location: Geographical location of your users;
Device: The device models of your users and their OS versions;
Demographics: Gender and age group of your users.
The Average Visit Time tells you how much time your users spend on the app on an average and Screen Views tell how many screens they interacted with. Both these metrics indicate the user engagement. Simply put, a longer Average Time Visit and more number of Screen Views amounts to higher User Engagement.
This metric shows you the total sales that you have generated from all your revenue sources. It helps you place a finger on your most profitable sources and check the average revenue that each customer generates over a period of time.
Churn is the percentage of users that stopped using your app. According to a study by Localytics, 57% of users churn within their first month and this number increases to 71% by the third month from using the app.
To sum up, mobile app analytics tools are a great means to an end. When used correctly, they help you to improve your product and create long-lasting customer relations. It is necessary to understand that different tools serve different purposes and to integrate the correct tools for your specific needs. Your analytics stack should have a good balance of qualitative and quantitative mobile analytics tools to ensure that you don’t miss out on any valuable data along your journey. If you know of any other tools that have helped you, then comment down below!
A slow launching app can affect retention and may also cause abandonment. We believe this new feature will be quite useful to monitor your app's performance.
Real user monitoring is the ability to monitor your user experience in real-time. It lets app developers identify and discover the performance fault, even before the user faces it.