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
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
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
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
What are they
What do they
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
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:
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!