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Machine Learning and Predictive Analytics in Mobile App Development

22 Feb 2021 Developer News
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What makes a great app? Have you come up with such threadbare clichés as high performance, visual appeal, responsive design, logic structure, or good marketing strategy? All this lies out in the open. Suppose you had a time-travelling machine sending you to the future, where you could see the fate of your app, count the ROI or talk to its users. Sounds like science fiction, yet you can make it real. Integrate Machine Learning in your mobile app and enjoy the tangible benefits accrued from Predictive Analytics.

Let us first attend to important definitions. Predictive Analytics is an area of study based on analyzing history and existing external data to unravel consistent patterns. As early as World War II it was used to decipher German intelligence. Machine Learning is an artificial intelligence technique employing algorithms that you feed with data. The algorithms are self-improved while processing big data. Therefore, Machine Learning powers Predictive Analytics.

AI and ML are predicted to drive a “Fourth Industrial Revolution”. What is so good about Machine Learning-based Predictive Analytics and the use of Machine Learning in mobile application development? We have made an overview of the bonuses it can give you during the mobile app development cycle and post-launch period.

Curbed User Churn Rate

No one and nothing is perfect. So, your brainchild is bound to have a number of flaws. The earlier you discover them the fewer chance users will kill it. Machine Learning in this case pictures user-app communication, where it flows smoothly and where it stumbles. What devices are most commonly used to run your mobile app? This knowledge helps your techs to ensure operating system compatibility.

Personalized Marketing

Developing a Machine-Learning mobile app, you cater to a universal desire to feel unique.

Andrei Kazialetski, develops AI-driven apps at InData Labs. He states:

‘By implementing the tech in a mobile app, you will be able to customize the product/service recommendations or push notifications a user gets. This is achieved thanks to analyzing the data on their spending/browsing habits, search queries, gender and geographical location. The algorithms can also sort your audience into active users, passive installers and those about to delete the app. The categorization also helps to make the user experience more personal. Thanks to ML, the search within your app becomes intuitive and more content-relevant.

Boosted User Engagement

Statistics on which features or screens make a user stay longer and which are totally ignored helps to make app minor improvements without changing the core. Using artificial neural networks (Deep Learning), you can embed face/text/speech/image recognition features to make your app rank higher among users thanks to its improved efficiency. Social media apps reap the most benefits from Predictive Analytics engine collecting likes/hashtag/group membership data to make relevant recommendations and encourage the user to take a longer journey through the app.

Enhanced Security

AI can serve as a watchdog for you tracking down any suspicious activity that might be associated with fraud or theft. This is widely exploited by banks to check on borrowers’ solvency. Moreover, Machine-Learning mechanisms provide for a secure and less mental energy-consuming authentication.

It is worthwhile mentioning that on-device ML algorithms are considerably better than cloud-based ones. With security and latency issues at stake, all doubts should be dispelled. Add here cut costs on external cloud providers and Internet connectivity independence.

By integrating Predictive Analytics in your development cycle, you can unlock its groundbreaking potential to its full.

Planning stage

As some projects are repetitive, so do developers’ mistakes. Let the engine find out what they are to avoid falling into the same trap. Moreover, analyze the number of code lines generally delivered by your developers and the standard time required. This will allow you to estimate whether you will meet a deadline.

Testing stage

Save on testing time and efforts by predicting common paths users take while executing your mobile app.

Wrapping Up

You write a success story of your mobile app yourself. Making Machine-Learning technology an essential part of your toolbox will make your head and shoulders above your rivals. Machine-Learning mobile applications can better not only user experience but also affect positively your development cycle. In the end, you get a fast, secure, debugged and engaging mobile app with lower costs incurred.

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