Predictive Analytics for Mobile App Dev

In the history of the human race, all leaders have longed for the ability to see into the future. Today in business, that wish is still there. But with predictive analytics, we may be very close to seeing that wish fulfilled.

When it comes to business, the ability to predict the future would be invaluable. Knowing what consumers want now and will want in the months to come would be a gold mine to business owners and marketers. Knowing what’s coming as far as external factors, trends, and potential problems could guide them in making their businesses highly successful.

The Power of Artificial Intelligence

The power of artificial intelligence (AI) in technology today can’t be overstated. As it grows and improves, it’s not beyond the realm of possibility that it will soon allow us to predict the future.

Projects like Virginia Tech’s EMBER Project have been working for a few years now with that goal in mind. Using publicly accessible data from news venues, social media platforms, financial market intel, meteorological information, satellite images, transit bus ticket sales and more, the EMBER Project has been predicting social incidents since 2012. Several times, the project has been spot on with their predictions, giving us just a preview of the potential of predictive analytics.

When predictive analytics are integrated with mobile applications, the results are dynamic. Using data from traditional metrics, user data, data mining, and other AI sources, predictive analytics can be used to offer consumers product recommendations or make other predictions for many purposes.

Predictive analytics can boost consumer engagement to drive sales and provide businesses with information to make solid informed business decisions. When it comes to marketing, predictive analytics allows for incredibly accurate and fine-tuned marketing strategies.

How do Predictive Analytics Work?

Predictive analytics programs collect data on actual sales and consumer habits to find out which products are performing well and picking up steam. Using complex algorithms, they can pull data from other sources to recognize popularity trends on business websites, social media platforms, and elsewhere on the internet.

Predictive analytics programs analyze current and historical data sets and uses algorithms, machine learning, and statistical data techniques to predict results. They can predict development issues, quality issues, and identify undiscovered opportunities. It takes a close look at how the end-users behave on after the launch. The end results offer nearly limitless potential for business owners and entrepreneurs.

The ability to predict which products or services will be more lucrative not only helps the business but could also provide highly useful features within the mobile app for users. Likewise, apps can boost sales by tracking user activity and recommending books, games, movies, music, and more. After incorporating product recommendations at various points in the shopping and buying process, Amazon saw a 29% increase in sales.

Predictive analytics can do the same thing for e-commerce apps and apps for luxury brands. When users receive product recommendations, studies show a notable increase in conversions. Studies show e-commerce apps perform 120% better than standard mobile websites and 20% higher than desktop websites.

Tracking User Activity and Recommendations

Many industries are already using predictive analytics for many reasons such as boosting sales, identifying risks, and reducing losses. When you combine predictive analytics with your mobile app, there are many advantages such as the following:

Improved user engagement:

  • With the help of the predictive analytics engine, it’s easy to determine which content or design elements within the app are appealing. It can also tell which elements aren’t a hit with users so app developers can make adjustments. The engine provides a wealth of extra data too. It can tell which devices and operating systems visitors use most often with your app. Armed with that information, the app can be better tailored to users’ preferences to encourage increased activity on them.

Enhanced retention:

  • Predictive analytics can also help build loyalty to your brand. By identifying broad pain points that need revamping, you’ll know what features to improve on your app. The engine takes the guesswork out of fine-tuning your app and eliminating any issues. You’ll provide your customers with an easy-to-use streamlined experience that will allow them to browse and buy as many times as they wish. If your app is easy to use, you’ll keep customers happy. Happy customers come back.

Keeping up with the updates:

  • Android and Apple update their operating systems often which is a good thing. The challenge that it presents is having consumers with several versions of an OS using your app. Will the app be able to support all versions? You want to avoid a situation where users have an OS that your app isn’t compatible with. Predictive analytics can identify which OS versions are most used on your app so developers can tailor the app so it supports them. That will prevent the loss of users who wouldn’t be able to use your app otherwise.

Reducing risk:

  • Another great use of predictive analytics is the ability to foresee risk levels. It predicts the chance of breach, fraud, or theft by perpetually collecting data and tracking app usage trends. By monitoring all activity, the engine is immediately aware of break-in attempts and other fraudulent usages. It can freeze involved accounts for hours and announce any app-wide issues to users quickly.

Predictive analytics has a lot to offer businesses within their mobile applications.

What Predictive Analytics Offer Various Industries

Banking:

  • The banking industry has made good use of predictive analytics to help them address consumer needs, boost product sales, and increase their profits. Predictive analytics help with application screening, cross-selling products, fraud detection, and more in order to meet customer’s needs and earn their loyalty.

E-commerce:

  • Being able to predict the future in retail is a recipe for success. Brands will have access through predictive analytics to a wealth of information about their customers. They can use that data to offer consumers personalized recommendations and tailor the app experience of each customer using automation. The use of predictive analytics gives brands insight into customer experience and can help them keep customers engaged. Consumer retention rates will increase.

Entertainment:

  • The sheer volume of streaming music and movies, videos, and other entertainment-related content has increased in the last decade. And there are a lot of apps in this industry so in order to be successful, your app has to have an edge. To be competitive, you have to keep up with consumers and be spot on with marketing and the newest trends. Predictive analytics can help you do just that. Larger entertainment companies have a good handle on how predictive analytics can best be used. When you use a music streaming service, AI tailors recommendations to the individual user. The app can recommend new music that’s trending and is similar to what they normally enjoy. App users enjoy the convenience of not having to search through thousands of songs to find something new.

Healthcare:

  • Predictive analytics offers endless benefits in the healthcare arena. When it comes to patient care, predictive analytics can aid in improving the accuracy of diagnosis, assisting hospital administration, improving chronic illness management, helping manage staff needs, and reducing cost and waste.

Many other industries are taking advantage of the benefits that predictive analytics have to offer. More and more it appears that a predictive context-aware app will be the way of the future. But it will have to be well executed to be effective.

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Irina Sidorenko

Irina Sidorenko

Irina Sidorenko is lead content and marketing manager at yalantis.com. She has many years of experience in IT and Business industry, which include establishing and running her own small business.

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