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Three Ways to Use Predictive Analytics For Mobile Applications

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As processing power increases, we’re seeing tremendous growth in the realm of predictive analytics. Predictions of future outcomes are developed using historical data, statistical data, predictive modeling and even artificial intelligence or machine learning in the case of more sophisticated PA platforms.

Chances are, you probably see predictive analytics at work on a regular basis, although you may not recognize these encounters as such. Here are a few examples of predictive analytics applications:

  • Amazon, eBay and hundreds of other shopping platforms use predictive analytics to offer product recommendations based on your browsing history and past purchases.
  • Google and other search engines use predictive analytics to provide suggested search phrases based on your search history, location and regional search trends.
  • An online dating website may use predictive analytics to recommend another member who is likely to be a good match.

These are just a few ways in which developers are utilizing predictive analytics on the web. But technology is also rapidly evolving in the mobile realm. So how are developers leveraging predictive analytics for mobile apps? And how will this impact your search for a mobile developer who can implement PA technology in a way that brings maximum returns?

Using Predictive Analytics to Drive Sales and Profits

If you’re building a mobile e-commerce platform, predictive analytics can provide users with good recommendations for products or services they may not purchase otherwise. For example, once a shopper adds an item to their shopping cart, a predictive analytics engine can suggest items that are frequently purchased alongside it. This is ideal for any product with accessories or refills.

A mobile device has the added benefit of detecting the user’s precise geographic location, which can help generate recommendations for brick and mortar shops, restaurants, attractions and other businesses.

Similarly, a predictive apps can collect shopper browsing data. This can then be used to determine which featured items are most likely to appeal to an individual who has viewed or purchased specific products or services.

Apps that are monetized using advertisements can also benefit from predictive analytics. This technology is widely used to serve up ads related to past purchases, past product/service page views, location and other user data. The end result is an advertisement that’s more relevant to the user, which means advertisers are apt to see a higher ROI. In turn, your app’s ad space becomes more valuable to the advertiser.

Notably, the PA engine will become more accurate over time, as it collects more and more data that can be used to make predictions. This means the sooner you implement PA technology, the better. And over time, you will likely see increasing returns on your investment.

Using Predictive Analytics to Promote Engagement and Interactions

Predictive analytics is effective at making recommendations based on user behavior, making it particularly well-suited to mobile apps that rely on engagement or interactions, such as a social media platform or a dating app.

A predictive app can offer recommendations for who to follow, which groups to join and what pages to like. Your users can also get recommendations for friends (or matches, in the case of a dating app). Social platforms such as Facebook and Twitter already use this technology to make friend and follower recommendations, so users have come to expect it to some degree.

What’s more, predictive analytics apps can be engineered to collect user data, such as a user’s location, friends, likes, hashtags and the keywords in posts. Based on this data, the social platform can maximize the appeal of the content in a user’s feed.

Even navigation and entertainment/activity-related mobile apps can leverage PA technology to collect user data and then recommend locations or activities. This type of data can include everything from the user’s online search queries and browsing history to the device’s GPS position and past travels. These kinds of mobile apps are also seeing advancements in the realm of augmented reality, as developers integrate virtual overlays such as maps and signage over the input from a device’s camera.

Using Predictive Analytics to Minimize Risk and Prevent Fraud

Predictive analytics are helpful for predicting risk levels for security breaches, theft incidents, fraud and other losses. Therefore, many financial institutions, identity theft monitoring companies and cyber security groups leverage PA technology to identify trends that can indicate higher-than-average risk.

A predictive analytics engine collects data over time, identifying trends surrounding areas of high risk. Then, the PA engine can monitor for those trends, which could range from monitoring a customer’s account activity to monitoring login attempts to an enterprise app containing sensitive information. Developers can even configure your system so it freezes account activity or prevents additional app login attempts pending identity verification.

There are many ways in which developers are using predictive analytics for mobile apps, and these possibilities are only expanding. But finding the right development team for your project is essential to its success. 7T is a front-runner in this arena, as we have pioneered some innovative predictive analytics solutions for our clients.

7T is based in Dallas, with regional offices in Houston and Austin. But our client base extends across the United States and beyond. We invite you to contact us to discuss integrating predictive analytics into your mobile app development project.

Reach out to our team today!

Shane Long

As COO and President of 7T, Shane Long brings experience in mobility that pre-dates the term “smartphone” and the release of the first iPhone. His work has helped revolutionize the growth of mobility by bringing to market one of the first graphics processors used in mobile phones, technology that after being acquired by Qualcomm lived well into the 4th generation of smartphones, as well as helped pioneer the first GPS implementations in the segment. With a strong engineering and business background, Shane understands how the rise of mobility and Predictive Analytics is crucial to greater business strategies geared toward attaining competitive advantage, accelerating revenue, and realizing new efficiencies. As the leader of a B2B mobility solutions provider, he partners with business leaders including marketers and product developers to leverage enterprise mobile applications, big data and analytics, and mobile strategy.

Shane earned a B.S. at Texas A&M (whoop!) and studied mathematics as a graduate student at Southern Methodist University.


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