Apps

Data Collected for Internal Use

The power of in-app data management is massive. In order to extract data-driven insights from your organization’s app, a strong data infrastructure is necessary. Such questions include:

“What interactions do users have with our app?”

“How long do users usually spend on our app?”

“Can we predict which app users will tend to purchase certain products?”

There are programs that will automatically collect this type of data about your app. The data is stored in a cloud-based storage provider such as Google Cloud, Microsoft Azure, Amazon Web Services (AWS), or others. Boxplot works with all kinds of data structures and systems. The benefits of data storage systems is that they are centralized-meaning you only have to update each data point once for it to be available everywhere.

There are many types of data that apps collect, but some of the most common types are:

Demographic Information.

Patterns often emerge in app data according to the demographic information of the users, and these patterns often lead to valuable insights that can improve the effectiveness of your app. For example, younger users may tend to spend longer on your app each time they use it than older users, European users may tend to use the app at different hours than American users, users from certain states may tend to spend more on every purchase than users from other states, and so on.

Usage Information.

In a generalized sense, how do users tend to use your app? What is the flow of their actions? Knowing this will allow you to optimize ad spend, ad targeting, product recommendation, and more.

Ad Data

Monitoring how in-app ads are performing is central in ensuring advertisers are reaching the correct audience. In order to accomplish this goal, well-designed apps record data pertaining to the ads that are shown, whether users tend to stop and look at each ad and for how long, the clickthrough rate (CTR) of each ad, and so on.

Data Collected for the End-User

Many apps offer an analytics page to its users. It allows them to understand their own data and to make better decisions. For example, if a photography app tracks projects, it may offer a dashboard that shows how many jobs were completed in a given year, trends over time, a breakdown of jobs by type, etc.

It might also offer suggestions to its users in this analytics page. In-app machine learning provides a customized user experience by training a predictive model based off of other users’ activity.

For example, an ecommerce app may make purchase recommendations to users based off of the purchasing decisions of similar users using machine learning. Yet what really overarches the entire realm of in-product data analytics—whether for reporting or machine learning—is real-time analytics, or the ability to ensure that the analytics displayed to users are completely up-to-date with the data that have been collected. View this article if you’d like to read more about real-time analytics and its use cases.

Data Collection & Analytics For Your Organization’s App

If you’re working on a new product or app for which you want strong data collection and/or analytics systems, or if you want to add these features to a pre-existing product or app, there are a few questions for you to consider:

Do you want to collect data within your product/app, analyze data within your product/app, or both?

Which programming language will your app use?

What are your data reporting requirements, and are there any components which might benefit from machine learning capabilities?

How do you want to store your data?

Will you build the app yourself, or will you consult an outside source?

No matter how you answer these, Boxplot is here to help; our experts have extensive experience with in-product data management. Whether you want us to build the whole app, or if you just need a few ideas, start a discussion with us today.