If you have an app in the App Store, you’ll also have access to some analytics for your app so that you can monitor its performance over time. With this data, you can see what devices and operating systems your users are using as well as how frequently they’re launching your app and more. This article will explain what the different terms mean in iOS analytics and how to interpret their significance. Analyzing these statistics will help you understand who is using your app so that you can make appropriate changes or updates in the future. Let’s dive in!
How To Decode Your iPhone Analytics Data
The term “app analytics” refers to monitoring how people interact with your app. This is usually done through the use of a tracking system that records certain activities performed within your app such as how often the app is launched by each user. These systems also often record information about each user’s device, operating system, location, and other details that can help you better understand your user base. The basic idea behind app analytics is to help you understand how people are using your app so that you can make improvements in the future. This information may also be used to inform marketing decisions such as which devices to focus on and what features to prioritize.
Sessions refer to the length of time between when a user launches the app and when they close it again. If a user opens your app, performs some action, and then closes it without opening it again until the next day, that’s still one session. Sessions are measured in minutes, not hours. While the length of a session isn’t always indicative of how engaged a user is, it can be useful when looking at the data as a whole. If a large portion of your users is closing their apps after only a few minutes of use, this could be a sign that your app is too difficult to navigate or that its content isn’t engaging enough.
Daily Active Users (DAU)
Daily active users (DAU) is a metric that refers to how many users are launching your app on a daily basis. You can calculate this by taking the total number of daily sessions and dividing it by the number of users that have downloaded your app. A high DAU count will generally be good for your app as it means more of your users are interacting with your app on a daily basis. However, it’s important to remember that a high DAU count doesn’t necessarily equate to a high retention rate. You’ll want to look at that metric in addition to DAU so that you can get a full picture of how engaged your users are over time.
Retention refers to how often users are coming back to your app after having interacted with it once. This can be measured in both a daily and monthly capacity, though daily retention is most common. You can calculate daily retention by taking the total number of daily active users (DAU) and dividing it by the total number of users that downloaded your app. There are a few key metrics to look at when you’re calculating retention. First, you want to examine the ratio of DAU to new users. This will show you how many of your current users are coming back each day as opposed to how many new users are downloading your app for the first time. This will help you understand how many users are sticking with your app long-term and how many are dropping off after their first visit.
Cohort analysis is a method of examining how different groups of users (known as cohorts) interact with your app. For example, you might want to analyze how different user cohorts are performing in terms of retention or sessions per day. You can do this by taking a specific date as your starting point and then looking at the metrics for each cohort based on when they first downloaded your app. By analyzing your data by cohort, you can get a better idea of how various groups of users are engaging with your app so that you can make appropriate changes. For example, you might want to re-focus your marketing efforts on a specific cohort if you notice that group has a low retention rate.
New User Conversion
New user conversion is the percentage of users that are downloading your app and subsequently launching it at least once. This metric is important because it tells you how many of your users are actually using your app even if they aren’t retaining it. If your new user conversion rate is very low, it could be an indication that you need to update your app to be more user-friendly. This can include things like making your onboarding process more intuitive or removing difficult steps from your app. The new user conversion rate can vary greatly depending on the type of app you have. Games, for example, may have a lower new user conversion rate since many of them are designed to be played for longer periods at a time.
Device usage refers to the percentage of users on each device type that are engaging with your app. This will help you understand what types of devices your users are generally using as well as the percentage of users on each type. While the device usage data may not directly impact your app’s performance, it can help you understand your user base better. If you notice that a large percentage of your users are using an outdated device, you may want to consider rewriting your app for a different device type. Similarly, if you notice that a device type is being used significantly less than others, you may want to consider offering a different experience for users on that device type.
Why It’s Important To Decode Your iPhone Analytics Data
You don’t know what you don’t know
Most of the time, your app’s performance is directly associated with how well you know your user base. You can use analytics to get a better understanding of your user base and their behavior, but there are some things that you just can’t know without having a deeper understanding of how they interact with your app. For example, if you notice that users who log in via Facebook aren’t retaining any data from your app, it could mean that they are using the Facebook login feature as a way to quickly log in and then immediately exit out of your app. This might be an indication that there is something about the Facebook login feature itself that isn’t working for them. Knowing why this might be happening will help you improve on any issues with this feature as well as provide more insight into how it could be improving engagement and retention for other users.
Get a better understanding of different user types and their behaviors
Analytics offer a lot of information about the behavior of specific user types, but they don’t tell the whole story. If you want to truly understand what is happening within different groups of users, it’s important to look at where those groups come from as well as where they go after using your app. For example, if you notice that most users who download your app are only using it on their iPhone while others are also downloading it on their iPad or Android device, this could be an indication that there are different types of users within your user base. Knowing how each type of user is using your app will help you develop a deeper understanding of how to engage and retain them, as well as how to reach the most people with the fewest resources.
Understand how users are changing over time
Analytics can give you a lot of information about your user base, but it doesn’t tell you anything about their behavior over time. If you notice that there has been a significant increase in installs for one particular device type, but then there is no change in engagement or retention over time, it might be because the type of user who downloads your app isn’t very similar to the first group—they may have just been new users who downloaded the app for the first time and then never used it again. Understanding why this might be happening will help you understand what changes need to be made to ensure that these new users are retained and engaged with your app over time.
You can’t always rely on analytics alone
While analytics can provide a lot of information about what is happening within your app and within different groups of users, they don’t tell you everything. Analytics aren’t always able to answer all questions related to performance, so sometimes having another way of measuring performance is necessary. For example, if you notice that there is a significant increase in crashes after an update was released but it hasn’t affected engagement or retention, you will need to determine why this is happening and if there is anything that can be done to prevent it from happening again.
You can’t always trust your own analytics
When working with analytics, it’s important to remember that they are only as good as the data that they are based on. If you’re using a third-party analytics solution, make sure you ask them how their data has been collected and what their privacy policies are so that you know how your data will be used. If you don’t trust them, find an alternative solution or think about using a different company or approach altogether.
As you can see, there are many different terms you can use when referring to app analytics. It’s important to understand what each of these terms means so that you can better interpret your data. Once you have access to this data, it’s up to you to make sense of it and determine how it can help you make the most of your app. For example, you can use this data to decide which device types to focus on and which features to prioritize in the future. Keep in mind that these statistics are only useful if they are accurate. It’s up to you to ensure that your tracking system is set up properly so that its data is reliable.