8 ways big data is being used to create the next generation of mobile apps

Data Science   |   
Published August 28, 2019   |   

Big data can help increase app engagement, optimize resources, and provide a significantly better user experience. It can personalize content delivery and make the content more relevant. For business, it can improve conversion rates.
Quite simply, big data can provide information for app developers to help create apps that users want.
Here are 7 ways app developers are using big data to create the next generation of mobile apps:

A more seamless UX

We can now track every movement a user makes on an app. Big data can crunch the numbers and provide valuable insight into improving the user experience (UX). Similarly, alerts can be automated to flag developers when apps are failing to meet UX and design standards.
This allows for continuous improvements to refine user interfaces. In study after study, mobile app users rank ease of use as the most important reason why they stay with an app or delete it. Reducing the friction for users is crucial.

Artificial intelligence and machine learning

Artificial Intelligence (AI) and Machine Learning tools can recognize patterns pitfalls and suggest areas that need improvement.
One way this might be used is to constantly monitor for load times. 53% of users abandon mobile apps or websites that take more than three seconds to load. Big data can identify what’s slowing down the process. AI can suggest ways to reduce the load.

Personalization and predictive analytics

Data can be used to customize experiences for users and deliver them content based on past practices.
The best apps seem to be intuitive. They seem to know what you want before you ask for it. Netflix, for example, is so good at it that 80 percent of the streaming video platform’s viewing comes from its recommendation engine. The more you use the service, the better it gets. Using predictive analytics, Netflix even knows which image is more likely to get you to click. It personalizes every image for each user despite processing as many as 20 million requests per second.
Amazon uses big data to set pricing based on product availability, shopper activity, competitor prices, and other factors. Product prices can change as frequently as every 10 minutes to entice you to buy now while maintaining profit margins.

Enhanced engagement

You’ve probably heard the term “app stickiness.” When an app is sticky, users are more engaged and tend to return more often. Tracking metrics like screen flow, duration sessions, and churn can help you improve stickiness.

Real-time analytics

To say the mobile app market is dynamic is an understatement. Things are changing every minute. Real-time analytics provide an insight into what’s happening right now so that you make dynamic changes.
Media companies do this well. As specific content become popular, apps can rearrange the layout on the fly to increase visibility. Ridesharing company Uber uses real-time analytics to analyze traffic patterns, weather conditions, and driver locations to provide the most efficient routes, expected wait times, and fares.

Resource optimization

Automation relies on data assessments to route traffic and allocate (or re-allocated) resources depending on usage and affordability. RPA (Robotic Process Automation) can be configured to capture and interpret applications and trigger immediate responses.
Instead of waiting for system operators to notice spikes in usage, it can determine the most cost-efficient way to process data. It can automatically scale up or scale down resources based on need without human intervention.

Marketing strategies

Capturing user data can tell you a lot about the kind of person that uses your app. This pool of data can help craft marketing strategies to reach new users and re-engage existing users. User demographics, purchase patterns, and social behavior can all contribute to building user personas. These personas can be used to customize marketing messages and deliver them to the right audience.

Cost reduction

Building a quality app has always been an expensive and time-consuming process. Calculating app development costs involves understanding how many designers, developers and testers will be involved throughout the app’s development process. This all has an effect on driving the cost up significantly the longer it takes to build the app. New technologies like low-code development leverages big data and machine learning to significantly reduce the time and cost to deliver apps to the market.