How Machine Learning will Enhance User Experience in Mobile Apps

Sep 20, 2017 6:03:12 PM


Can you imagine a mobile app automating and controlling a number of tasks for the users, without being explicitly programmed for it. Well, that might sound fanatic, but it’s a practicable model today. Thanks to Machine Learning!

Machine Learning (ML) is subset of Artificial Intelligence (AI) that analyses a set of data, builds an analytical model, and then predicts accordingly. By implementing ML to mobile apps, businesses can ensure better experience to the existing and anticipated users.

But, how does it work? How it can enhance the user experience with an app? Basically,  Machine learning work on the basis of cognitive learning, very similar to human brain. It’s based on the idea that machines too (like humans) learn with past experiences.

However, unlike humans, machines can analyze big data (which is large and complex). Machines analyze the data, understand behaviour through past activities, and then predict the most effective output or solution to the specific user. If you just look around, you will realize that you are already making the most of Machine Learning.

With an amazing pattern recognizing potential (that made data mining and Bayesian filters popular), Machine Learning is all set to disrupt the mobile app stores with some valuable functionalities. Considering this, we present some of the interesting ways in which ML could benefit the mobile apps, make them effective than ever.

a. Personalization and Recommendation

Machine Learning and its algorithms are all about data. However, the best part is, it will never make a user feel lost in it. One of the most valuable benefit of ML is in spite of working on so much data, it will serve every user personally.

You must have experienced this application of ML already in the form of “People You may Know” in Facebook and “Products You may Like” in eCommerce websites. Infact, social media and eCommerce are the most prominent spaces where you may experience ML benefits. Such an approach can help in a number of ways like:

  • Filtering the most relevant content for the users in accordance to their interest.
  • Making an app more interactive, as if it’s meant for a specific user only.
  • Targeting users for advertisements, thereby enhancing the possibility of making a deal.

Not only does ML set a coordination between the business and customers but also let the businesses stay updated with real time demands. For example: Keeping the warehouse inventory informed about dynamically changing requirements during sales and discounts (eCommerce).

b. Big Data Mining for User Analysis

Ever wondered how large scale applications like those suggesting restaurants for food ordering are so precise and near to your choice. How do they know your taste, your space preference, your budget, and recommend you what you actually accept.

Machine learning is all about pattern recognition. As you use an app, it keeps analyzing your information and preferences like location, search request, gender, age, frequency of app usage, the type of restaurants chosen so far etc. On the basis your past behavioural pattern, it makes the suggestion that is likely to be accepted.

Mining and analysing big data means you can find some useful statistics and interesting behaviour pattern about the user. As a result, continuous efforts to improve the user experience can be made.

c. Making the Search Smart and Fast

Users don’t want to wait. Thus speed is one of the crucial elements when it’s about a refined user experience. Mobile apps today are smart enough to collect the customer data like user’s search history, click-through and sell-through rate etc. and then manage this data. To turn it into a profitable experience, ML can help.

Machine Learning differentiates matching from predictions. Ever wondered how fast Google search predictions are or how the eCommerce search boxes make the customers connect with the product on their mind. That’s how ML is improving the user experience, by making the search faster and smarter.

d. Fraud Detection and Security

Digital wallets and mobile payments are the hottest trends in Fintech industry. However, these payment transfer platforms confront security challenges, which certainly needs to be worked out. By training the Machine Learning models to detect activities that could be a fraudulent, the monetary transactions can be shielded. Not only this, we could see ML helping in stock market prediction as well.

To make the services fast and secure, ML can collaborate with various user validation systems like biometrics to identify and authenticate the processes. It’s a great option for mobile apps that needs strict user consent before any transaction is processed.  

e.  Healthcare and Fitness Management

The world is crazy over health and fitness, like never before. Wearables connected with mobile apps are used for goals tracking, keeping a record of health issues at regular time interval. While the mobile app will have same set of features, Machine Learning can customize the app functionality in accordance to the data, user’s physical state, and previous health records. Depending upon how the results have been, the app will pass an alert, give recommendations, or take necessary actions. That is why, Machine Learning is one of the best suited technologies for healthcare app development.

ALSO READ: Machine Learning Framework for App Developers

What’s your Idea to Implement ML into Mobile App?

Machine Learning is a disruptive technology; intensifying functionality and possibilities in almost every industry. However, the biggest power and limitation of ML is data and the ML model. More precise they are, more efficient the output will be. Therefore, when opting to implement Machine Learning into mobile apps, always go for professional AI development services partner.

Kunwar Jolly

Written by Kunwar Jolly

Digital Consultant at Daffodil Software, Kunwar is an avid reader, tech enthusiast and generally keeps abreast on latest developments in the technology space and their future outlay.