How to Use Machine Learning in Mobile Apps

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The first thing you do before you start your journey or commute is probably open Google Maps and see how long it takes to get there. You also reach your destination almost exactly by the time deducted by Google Maps. Ever wondered how it calculates accurately?

As a Machine Learning application, Google Maps use historical traffic data and real-time information to effectively predict how long the commute is, and the best route to reach.

Machine Learning in Mobile Apps

The technology is not limited to Google and other giants of the industry. You can readily integrate Machine Learning into your mobile application, leverage it, and gain a competitive edge, irrespective of your industry.

But before we see how you can do that here is a sneak-peek into Machine Learning.

What is Machine Learning?

Machine Learning is an application of Artificial Intelligence that enables computers to read, analyze, and understand huge collections of data to identify patterns, predict and provide insights on what comes next. That is, instead of having a programmer write every command for the machine, the machine analyzes the data, develops a logic based on the data, and acts accordingly. In simple words, the machine learns.

Use Cases of Machine Learning in Mobile Applications

#1. Machine Learning Powered Virtual Personal Assistant

Virtual Personal Assistants are voice-activated bots that listen, understand, and execute your commands.

You are essentially allowing your customers to access the features of your mobile application software with just their voice commands.

As you may already know, Siri, Alexa, and Google Now are some of the prominent examples of Virtual Personal Assistants. Ever since their introduction, they have gained a huge reception for their usage.

Virtual Personal Assistants process natural language, contextualize the command and complete the tasks improving overall customer experience and engagement of your mobile app.

#2. Accurate Fraud Detection and Management with Machine Learning

Fraud detection and prevention are crucial for every industry. But, it takes the utmost priority for financial institutions and applications where transactions are involved. One example of Machine Learning for Finance Applications is the identification of credit card frauds, loan defaults, and fraudulent cheques with predictive analysis before they actually happen in real-time.

A person’s integrity and the probability of loan repayment can also be identified based on his/her data.

Machine Learning enabled E-Commerce applications can identify and prevent the abuse of promotional discounts and offers so that they reach more people. With Machine Learning, your mobile software can track if your customers’ actions are completely legit or if it requires your action to prevent harm.

#3. Online Customer Support with Machine Learning Chatbots

You can’t have a dedicated support team to answer every query of your mobile application users. After all, your application can be accessed by millions at the same time. To solve the problem, you can deploy chatbots that act as your online customer support in your mobile software.

Chatbots do not ask your customers to wait. Powered by Machine Learning, they understand your customers’ queries and provide solutions in the easiest way possible.

Over time, the chatbots understand every customers’ unique writing style and provide them a personalized solution increasing their engagement in the process.

#4. Customized Product Recommendations with Machine Learning

The machine learning algorithm records the browsing data of your customers within your mobile application.

With the continuous stream of information, the algorithm deducts

  1. Who your customers are

  2. What your customers want

  3. What your customers do not like

The Machine Learning Algorithm analyzes the data and forms a logic based on the users’ preferences so that you can promote your product in a way that appeals to them.

You can switch between formal and informal language, choose from a series of narratives and emotions to provide a better experience.

Netflix uses Machine Learning to recommend shows to its viewers. And approximately, 80 percent of the shows watched are chosen from the recommendations.

#5. Face & Object Recognition with Machine Learning

Your application can get highly secured and reliable with face recognition. But it simply doesn’t end there.

Machine Learning enabled Medical Applications can use face recognition and scanning to identify medical problems scanning the symptoms like swelling and inflammation.

The mental status can also be known when the Machine Learning algorithm is allowed to read facial cues: sad, happy, annoyed, etc.

Real Estate Applications with Machine Learning can use image processing and object recognition to know what fits in the given area: furniture, electronic appliances, and other equipment.

Lately, Facial Recognition is gaining a lot of attention for its implementations. It can be used in applications and ATMs to authorize transactions, limit access to sensitive areas, in Casinos to identify fraudsters, among other places.

Wrapping Up

Google Maps make use of yours and everyone else’s phone GPS to determine how fast the traffic is moving in real-time. The information, together with your vehicle speed and destination is fed to the Machine Learning algorithm to calculate the time taken.

Machine Learning has already become an everyday part of life. If you think otherwise, you just haven’t noticed it yet.

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