Most apps capture the user's age and gender data when they install the application for the first time. This will help you to understand the common user group of your application. You will also have the user's data, which will give you the usage and frequency of how much the user utilizes the app, as well as location data, if that is permitted from the user's end. This will be helpful in predicting customer targets in the near future. For example, you will be able to see whether your user audience is coming from an age group of between 18 and 25, and is predominantly female. In that case, you could devise a strategy to pull more male users, or just stick to targeting female users only. The algorithm should be able to predict and analyze all of this data, which will be helpful in marketing to and increasing your user base.

There are a lot of niche use cases where ML-based mobile apps can be of great help; some of them are as follows:

• Automatic product tagging
• Time estimations, as used in Pedometer, Uber, and Lyft
• Health-based recommendations
• Shipping cost estimations
• Supply chain predictions
• Money management
• Logistics optimization
• Increasing productivity