What happens when Artificial Intelligence meets mobile apps

What happens when Artificial Intelligence meets mobile apps

Here are the Use Cases of Artificial Intelligence Application in Mobile Apps

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Automated reasoning

The feature is a blend of the science and art of making apps that uses logical and analytical reasoning to solve issues, it was what help machines prove theorems, win chess matches, and solve puzzles. It is through this feature that AI machines are able to judge the number of patients that will check-in in the hospital, are able to do stock trading and even play Jeopardy. There are a number of mobile app companies that have also incorporated the feature. One such company is Uber. The ride sharing app uses logical reasoning so as to optimize the drivers’ routes and help the riders reach their destinations sooner. The reasoning algorithm studies trillions of portions of data collected from the Drivers who have used the routes – both time wise and directions wise – and take the time to reach information.

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Recommendation services

It is probably the most effective and simplest application of AI technology in mobile apps, something that is seen in almost all mCommerce applications. The number one reason behind app failures only within a year from its launch is the failure to offer relevant content that would continuously engage the users. Even though you must be continuously adding new products in your site, until and unless users see the ‘Customers who bought this also bought’ option, the chances are you will continue seeing a low app session and conversion rate. By gauging the users’ choices and putting in the data in your learning algorithm, mobile apps make the recommendations, which the users are most likely to be appealed to buy. It is one strong stream of revenue for a number of mCommerce apps like Amazon and entertainment mobile app like Prime Video and Netflix. Even though, the AI type is mostly used by mCommerce and Entertainment industry, any business which indulge in upselling or cross-selling of content can use this AI type.

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Decoding the App Sentiments

By tracking what is been talked about your app everywhere – on the stores, on social media, on forums, or even on messaging platforms, AI’s Sentiment Analysis feature gives you an insight into how users are interacting with your app, with what competitors are they comparing you, etc. Sentiment Analysis gives you a direct information of what feature needs to be added and which needs to be removed from your suite of app features. In addition to getting you information on how users are interacting with your app, AI will also help you get access to information that is related to your users’ behaviour across different platform. You’ll then get to know which platform your users frequent, at what time, for what purpose etc.

If you are just starting, you can use the features in two ways – either base the whole app on predictive analysis or use it to keep rolling out product or discount information, to keep the active in your mobile app. Or, you can also launch an extension in your messaging app, which would make use of neural network to send automated replies, like what Google does. If you are confused with the many options, contact our company for some clarity.

Till now we have talked about the ways AI makes common Mobile Apps a game changing apps and then we looked into some tips that app developers should consider when working around with AI.

What next? Contact our team of our expertise to know the best way to incorporate artificial intelligence in your next mobile app.


Posted in Mobile Applications on Jan 22, 2019

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