What is Machine Learning and How to Use it in Your Mobile App?

Imagine a mobile app intelligent enough to modify itself according to user’s needs without your permanent control. Does it sound fantastic? Nevertheless, it’s not a dream anymore!

Machine learning, which is the subfield of artificial intelligence, can tailor your app according to personal needs of every single user. By adding machine learning to an app, you will attract new users and build more stable and strong connection with old ones.

According to Allied Market Research, the global market of cognitive computing is expected to garner $13.7 billion by 2020 with 33.1% annual growth during 2015-2020. Impressive, but not surprising. Just look at the advantages of using machine learning in your business by Apttus:

Incredible improve of your buisness thanks to machine learning
(image from Apttus.com)

But how does it actually work? Machine learning algorithms let a mobile app analyze big sets of different information (text, visual, audio, biometric) in order to make decisions, which are the most suitable for this certain user. After investigating his behavior and other activity, a mobile app can improve its effectiveness by predicting subsequent user’s actions.

9 ways to use machine learning in your mobile app

Machine learning can solve a wide range of tasks and significantly improve the user experience. Here are 9 ideas about how you can implement this technology in your app and make it even better!

Customization: let the user feel special

One of the main purposes of machine learning is to make a mobile app convenient and wieldy for each user personally. No doubt, you had such experience with “People you might know” in your profile on Facebook or “Recommendations” field on YouTube. Such an individual approach has several advantages:

  • it helps users to get the most relevant and alluring content according to their interests;
  • your users will feel like your app is really communicating with them;
  • posting a targeted advertisement, which is also the part of customization, increases the probability of making a deal.
You can use machine learning to customize your app
(image by Rounded Rectangle)

Make searching faster and easier

Modern apps are smart enough to collect all available customer data (click-through and sell-through rates, user’s search history, typical actions), manage it and then turn it into your profit!

Do you remember the golden rule? People must have a quick and easy access to any content because they don’t tend to wait.

All you need is machine learning, which has a broad variety of tools to provide your clients with the most relevant information. You can add ranking, spelling correction, voice searches, suggestions, a list of related requests and the searching process for your users will become more intuitive and less troublesome.

Searching process optimization is a great example of machine learning usage
(image by Divan Raj)

Rising sales with machine learning in e-commerce apps

E-commerce apps are the ones, which can benefit from using machine learning the most. People download such apps in order to make shopping process fast and easy, so why don’t you help them?

Read Also Top 6 Ecommerce Mobile App Design Tips for Improving Sales

Be sure: your customers will definitely appreciate it. The last researches show that in 2016 every second user of the global internet (53% in general) have bought something online, including 62% of smartphone owners.

How to use machine learning in a mobile app? Add some recommendations
(image by ZZ Wang)

Machine learning brings more customers to your app by these means:

  • recommendations, selected according to your users’ purchase patterns, searching requests and the site content;
  • predictions about future trends, sales, and prices, based on information from open sources (like blogs, social media, news articles etc.);
  • optimization of the searching process, which we have previously mentioned.

Mining big data – find a needle in a haystack

Mining big data allows you to find non-obvious connections between huge sets of information, get useful statistics and find out behavior patterns of your users. It may be useful for a food delivery app development, for instance.

By analyzing different kinds of data (age, gender, location, search requests, the frequency of app usage etc.) you will get more precise information about your users’ preferences and typical behavior. It will allow you to keep different groups of people interested in your app.

Example: you may find out that your app is far more popular among male users under 30 years with Instagram profiles rather than female. In such a case, you can take some actions to attract female audience back.

Benefit from a visual and audio recognition

We bet there is no such a person, who hasn’t heard about Shazam or Snapchat. Designed only for entertainment, however, Snapchat has more than 150 million daily active users. Shazam, in turn, has over 120 million monthly active users. Impressing, isn’t it?

Without machine learning it would be impossible to create Shazam
(image by Duncan Riley)

But it would be unreal to create these and similar mobile apps without machine learning. It became possible because of image, video and sound recognition systems, which can interact with user and surroundings in real-time mode.

One more way to use text or audio recognition along with machine learning is to create conversational UX or chatbot. Remember Apple Siri and imagine how excited your users will become if you add something similar to your app.

Fast and protected authentication process

Moreover, you can apply machine learning in conjunction with the different types of recognition (including the newest one – biometric) to pass user identification and authentication processes. It’s a good decision for any kind of mobile apps, including e-commerce.

The easier authentication process you will make, the more thankful your users will be.

This technology is widely used in apps like ZoOm and BioID, which offer an easy way to log into other apps and websites. Nowadays people are tired of keeping in memory logins and typing lengthy passwords, so why not to make it easier and faster for them?

Fraud prevention: don’t let your money flow away

During last years, the security of users appeared to be one of the main priorities in mobile. According to ClicksMob’s data, the top-5 most targeted apps categories are:

  • Games;
  • Lifestyle;
  • Shopping;
  • Travel & Local;
  • Sports.

That’s why frauds are vexed problem. Is there a way to minimize frauds’ negative influence? Machine learning is the answer! Let your app monitor ongoing processes without your constant control and then detect and ban all suspicious activities.

One more advantage: traditional apps can resist only already known threats, while machine learning systems allow protecting users from previously unidentified malware attacks in real-time mode. Snapdragon Smart Protect is a good example of such a progressive technology.

Turn your fitness app into a personal coach

The modern world is nuts about fitness: we have a gym at every crossroad, tons of different wearables, Nike and Adidas fashion shows and so on. So designing a fitness app with machine learning is a win-win situation.

Use machine learning in your mobile fitness app and attract more users
(image by Ramotion)

It’s not enough just to create an app with an arsenal of exercises for anyone and, moreover, it’s not going to be popular among users. However, machine learning customizes your app for the personal needs, aims, and the physical state of every person. It makes possible to tailor a deeply individual program of workouts and turn an app into a personal virtual coach.

That’s just like Optimize Fitness works. By incorporating available sensor and genetic data, it creates a brand new and exciting user experience and gives its clients all tools for improving their physical form.

Machine learning in financial apps: enjoy several advantages at once

Technology giants as Google, Amazon, Facebook use machine learning in a finance sphere for a range of reasons. Apps, using machine learning, work faster than humans do, can rapidly analyze big sets of data and modify itself on purpose to make better and faster decisions in real time.

There are a few ways to implement machine learning in a financial sector.

Predictions

“As a general rule, the most successful man in life is the man who has the best information,” Benjamin Disraeli said. Machine learning systems analyze huge amounts of data, including clients’ financial status, their behavioral patterns, market changes, upcoming trends, the efficiency of an app and so on.

You are free to turn this information into your profit, so why don’t you do that?

Read Also Mobile Design Trends Expected in 2017

Security

Everyone wants to keep money safe and your clients aren’t an exception. Intelligent analysis of all ongoing activities in your app will protect users from frauds and give them 1 more reason to use your app.

Security is also a field, where machine learning can help
(image by Alexandr M.)

Customer service and assistance

Many people really bothered, when they have to make calls or write long letters, so you better offer them an alternative way to get all required information or ask questions. Friendly and intelligent digital assistant (like chatbot or virtual voice secretary) seems to be a nice decision.

Our own example of machine learning in the financial mobile app
Image from OvalMoney by Stormotion

Conclusion

As you can see, machine learning is an innovative technology, which can be useful for any kind of mobile app. Every year the market of machine learning grows, so we are going to watch it among the mobile UX trends in 2017 as well.

Do you want 2017 to be the year of your success? Use machine learning algorithms in the mobile app and engage even more satisfied users!

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