Analytics integration: Four best practices for getting meaningful data from your app
Although the app development process tends to take place in somewhat of a vacuum, enterprises must eventually unleash their product to the world, which can be quite an overwhelming undertaking. By taking advantage of mobile analytics, developers can gain real-time information about how users are viewing and consuming a particular app. These valuable, relevant insights arm enterprises with the tools they need to stand out in the mobile app environment.
Here are four best practices for analytics integration in the mobile sphere:
Determine your long-term reporting goals
Before you implement app analytics integration, you must understand how you plan to use this data.
You should ask yourself the following questions:
- Are you trying to understand how certain demographics interact with your app?
- Are you attempting to determine if there are any features or functions in your mobile app that consumers find frustrating or do not use on a regular basis?
- What will you do with the analytics integration data? How will this data inform your app development?
- Will you prioritize certain features?
- Will you separate any features in order to make a different app?
- Will you provide further help and guidance for users who find certain areas of your app difficult to navigate?
By answering these questions, you can establish specific goals before you begin the integration process.
Track metrics based on the similarities between their feature sets
The most interesting, relevant data you will obtain from these analytics will be focused on users and their experiences. For instance, you will be able to determine how often users engage with your app in different scenarios and which screens they choose to interact with the most. It’s important to make sure that apps with similar feature sets are represented in the same overall analytics aggregate, as you’ll want to be able to compare apples to apples when you crunch these numbers and analyze the provided data.
Use a different set of analytics for each of the different mobile platforms on which you are deploying your app
In order to get the best results, you should separate analytics for apps that are running on one phone or mobile OS platform from those on another distinct platform. This separation is important because it’s possible that there will be variations in how different platforms implement certain features in your apps. The demographics of users on one platform may also be different than those on another. By creating different analytics sets, you can take these variations into account.
Avoid data sampling
Unfortunately, some platforms have a hard time processing the data derived from your app’s analytics integration. After all, these platforms may not have engineered their service to handle the load that this review process requires.
In order to shed some of that load, they sample certain subsets of data and then attempt to piece together statistically significant trends based on that sample. Unfortunately, this subset is not always chosen at random, so results can be skewed. As such, you should avoid working with platforms that rely on data sampling. Or, if necessary, you should work to configure one of these platforms so that sampling does not occur.
By following these guidelines, you can ensure that you are getting the most value out of your analytics integration.