5 Tips on How to Use Machine Learning in Mobile Apps


Using machine learning in mobile apps offers a way to provide distinctive features, simpler operations, and an enhanced user experience.

The impact of machine learning in everyday human lives is hard to ignore. Today, we have intelligent mechanisms all around us. From voice assistants that help you navigate a route to high-tech coffee pots, you are practically dealing with miniature robots everywhere! Increasingly, mobile apps are making use of machine learning to provide additional benefits and services to their users.

Does it ever surprise you how Netflix has on-point suggestions in the “recommendation” section or how Facebook automatically tags you on your friends’ photos? The answer lies in machine learning. These companies use it in apps to match a user’s taste and maintain the influx of a decent user base.

According to StatWolf, Netflix saved $1 billion this year due to its machine-learning algorithm, which recommends personalized TV shows and movies.

Reasons to implement machine learning in mobile apps

Think of all the apps you get exposed to each day. From morning to the time we crash in our beds – apps surround us. Integration of machine learning in mobile apps has enhanced the maturity of this domain even more.

Notable names like Apple and Microsoft are on their toes, infusing machine learning in their apps to enhance user experiences. In case you are still fuzzed up on whether to use machine learning in mobile apps, here are a few perks to convince you:

  • It brings a touch of personalization to the app
  • It creates an efficient search experience for the users
  • It will support the app for visual and audio detection
  • Advanced data mining improves app effectiveness

Statista shows how 25% of IT leaders plan to use ML for security reasons. Meanwhile, 16% want to use it in sales and marketing.

Given the diverse uses of machine learning, it is imperative to learn how to integrate it into your apps.

How to do it right

If you are an app developer or drawing towards this landscape, it is essential to acknowledge how machine learning is transforming the mobile app industry. Working on the futuristic trends will ensure that you gain significant long-term benefits from your application.

Here are five use cases of machine learning in mobile apps:

1 – Use ML to provide online customer support

You can’t have human operators sitting behind the desk, responding to every query made by millions of users. Using machine learning in your app enables you to deploy chatbots as your online customer support.

At its core, a chatbot is where the customer asks a question and gets an answer right away in the mobile app. Suppose the problem is complex; the chatbot will then connect the user with a live agent. Such prompt responsiveness will compel the users to keep coming back.

As per the BusinessInsider, the chatbot market size shall grow from $2.6 billion in 2019 to $9.4 billion by 2024. It will increase at a compound annual growth rate (CAGR) of 29.7%.

An exciting feature of ML chatbots is recognizing the customers’ writing style and understanding their queries. It then works to offer relevant solutions to the customers.

2 – Use ML for advanced search

Machine learning can optimize search in your application. You can deliver better and more contextual results, making searching more intuitive and less tiring for your customers.

Applying the ML algorithm allows the app to learn from customer browsing activities and prioritize results that seem most relevant. With the constant stream of information, the algorithm deduces:

  • Who are your customers?
  • What are they looking for?
  • What do they reject?

The ML framework analyzes this data, forms a logic based on user preferences. This way, you can temptingly promote your products. For example, Reddit is using the ML algorithm to improve search performance for hundreds of millions of its clients.

Cognitive technology ingrained in the machine learning algorithm helps to respond to FAQs, sort articles, documents, DIY videos, and scripts into a knowledge graph to provide smart solutions and instant answers. You can shift between formal and informal narratives. Or use various emotional cues to provide a better experience.

Tinder, the globally famous dating app, has broken all records of user satisfaction and engagement. It deploys machine learning to understand the user’s intent and preferences.

You can also upgrade your mobile app with spelling corrections and voice search. According to a report, 72% of people who use voice-search devices say they have become a part of their life. Therefore, if you want users to keep sailing in your boat, provide them voice-search convenience through the ML framework.

3 – Use an ML-powered virtual PA (personal assistant)

Virtual Personal Assistants tend to interact with an end-user naturally. The idea is to make them complete tasks that were historically performed by a secretary. It includes reading text or an email, taking dictation, scheduling, looking for the phone numbers, or reminding users about their appointments.

Essentially, when you integrate a VPA in your app, you allow your customers to access the features of your app with voice commands. Since the launch of Siri by Apple in 2011, virtual personal assistants’ have grown exponentially. Alexa and Google Now are some names worth mentioning.

(Source: FinancesOnline)

Pew Research claims that over 46% of US residents regularly use virtual assistants (and 42% of them use mobile devices). If you want your ML-powered PA to be usable, pay attention to the target audience. Figure out the problems you want to solve and make it the foundation of your voice strategy.

4 – Use ML for accurate fraud detection

Machine learning can streamline and secure the overall app framework. It allows app developers to determine access rights for their users.

There is no need to monitor the app constantly. The ML algorithm will detect and ban suspicious activities within a jiffy. According to the Global Fraud Index, account takeovers escalated by 45% in the past year. This resulted in a $3.3 billion loss for online retailers in Asia, Europe, and North America.

Apps like Uber are using the machine-learning algorithm to inspect customers’ previous transactions. It also uses face recognition technology to determine customers who are using stolen cards. The enhanced security is possible because ML has enabled developers to integrate features like:

  • Wallet management
  • Business intelligence
  • Image recognition
  • Shipping cost evaluation
  • Logistics optimization

Through machine learning, an app can adhere to and implement high-security standards through continuous learning and automation. Mitigating security risks is one of the most notable digital transformation trends that will continue in 2021.

5 – Use ML for face and object recognition

The incredible facial recognition technology of Snapchat has never failed to amaze the users. It analyzes a gazillion faces to start recognizing a face with all its features. Then, through a machine-learning algorithm, it can overlay lenses, filters, and masks via the phone’s front-face camera.

Your app can get highly reliable with face-recognition technology. Some reputable medical applications use face recognition to identify medical problems as they scan conditions like swelling and inflammation. A bunch of apps also read the mental status of the users by facial recognition technology.

Apps such as BioID and ZoOm Login use machine learning to allow customers to log into other apps and websites with secure, selfie-style face authentication. Besides securing the app, it also makes it easy to log in to the application.

According to Markets and Markets, the global facial recognition market could generate $7 billion in revenue by 2024. Hence, there is no doubt that if you dive into this arena, your app could bring in massive returns.

The bottom line

Statista sheds light on the number of apps available in digital marketplaces. There are nearly 1.96 million apps in the Apple App Store and more than 2.87 in the Google Play Store. By using machine learning in your app, there is a chance for you to stand out among the crowd.

In case you want to ensure you never drown in the sea of apps, always stay on the edge of transition. Empower your apps with machine learning and watch how the number of users takes a hike!

Mehul Rajput

About Mehul Rajput

Mehul Rajput is a CEO and co-founder of MindInventory, a mobile app development company that provides web and mobile app solutions and UI/UX design services from startup to enterprise-level company. His role involves heading the operations related to business and delivery with strategic planning and defining the roadmap for the future.

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