Make Apps Smarter with Machine Learning

Have you ever wondered how Spotify recommends the music you want to listen to? Or, how fitness applications offer tailored fitness solutions for your current goals? This “wow factor” on our mobile applications has been made possible in large part due to the rise of machine learning technology.


Let’s take a closer look at the role of Artificial Intelligence (AI) and Machine Learning (ML) in cutting-edge mobile application development and their impact on the mobile app industry.


Artificial Intelligence vs Machine Learning


First, let’s break down what artificial intelligence and machine learning mean.


Artificial Intelligence is the imitation of human behavior by computers and machines. The objective of AI is to make machines or computers exhibit the same level of smartness, intelligence, articulation, and artistic traits as humans. However, the level and applications of different AIs vary. For example, a hypothetical humanoid that can wash the dishes, clean the car, and cook meals, is a product of AI. On the other hand,  software that is able to understand speech and recognize whether the tone in the speech is angry, envious, or cheerful is also a product of AI. 


Machine Learning is a subset of AI which fulfills the objective of learning and training the programs that will be capable of making decisions. These programs are fed with enormous amounts of data, use algorithms to process the data, and train on that data so that when it is given a new input, the program is capable of taking a decision. 


What’s with All the Machine Learning Hype?


Until a few years ago, artificial intelligence and machine learning were just names for a unique innovation that could make machines perform tasks that were typically reserved for humans. Now AI and ML have become buzzwords in the market, especially in mobile app development.


As more and more AI products are encouraging professionals to boost their businesses, AI and its subset, machine learning are transforming every industry for better performance and cost effectiveness. With the economy leaning towards digitization, enterprises have shifted their focus to making themselves accessible on digital mediums as consumers spend most of their time on smartphones. As a result, the impact that artificial intelligence and machine learning could have on modern app development has caught the attention of many innovators.


This is why, as technology advances towards providing smart mobile-centric solutions, machine learning applications are rapidly entering our daily lives. Today, our mobile devices can do incredible things such as; recognize speech and image requests, help us stay on top of our complicated schedules, and even serve as a translator. Based on the early success of this tech, a growing number of startups and enterprises are investing in producing AI-based mobile applications. 


What Can Machine Learning Offer to Your Mobile App?


In today’s customer-driven economy, establishing yourself as a trusted partner for your target audience is critical. Businesses can engage customers by providing what they need at the right time. In return, users prefer companies that can predict their needs and requirements.


Machine learning can help you understand user behavior, then use this understanding to create personalized solutions. Mobile app developers integrate ML into their products to create tailored apps for each person. Using and studying the users’ daily activities, the application analyzes their behavior and learns to use this information to fabricate an improved and consistent user experience. In this way, machine learning allows product owners to make their mobile apps more responsive and user-friendly.


Machine learning has fairly quickly been recognized as promising and even revolutionary for mobile app development specializing in face, image, and speech recognition, data processing and predictive analysis, classification, and forecasting, etc. It is being used by companies working in different industries like retail, healthcare, e-commerce, logistics, entertainment, education, etc. 


Let’s dig deeper into some of the benefits of using machine learning in mobile app development: 


Personalized Experience


You can use machine learning to acquire user’s information and learn more about them. ML can breakdown some of the following answers for you;


  • Who are your customers?
  • What do they want?
  • What can they afford?
  • What preferences, hobbies, and pain points do they have?
  • Which words are they using to search about your products?


Once you get the desired user information mentioned above, you can use it to design marketing strategies for promoting the products. ML algorithms help to find an individual approach that helps to target each customer of varied groups. Machine learning provides creative and enticing content that makes your mobile app more interactive. Because of this, AI and subsequently, ML in app development are the preferred choices by app owners.


Advanced Search


Today, businesses aim to engage more and more users. And while attracting users is essential, retaining them is more. This can be done if companies offer them what they need. And what is it that every user needs?


To be able to identify the right product in a short turnaround time.


If the process to search for the products is made simple, more users will be engaged and get what they want. Machine learning solutions help to optimize search in the apps by analyzing and predicting user behavior, in turn delivering the better results. This makes searching more intuitive and less burdensome for your customers. ML algorithms based on customer’s queries, show refined results that matter most to a particular person.


Modern apps built using machine learning allows you to collect available data about your customers such as search histories, and typical actions. The data, coupled with behavioral data and search requests helps to categorize and rank products and services according to the best-matching search results. In turn, users are provided accurate search results, which helps you retain them. 


Improved Security


Machine learning can streamline app authentication, improving the security of your mobile app. This helps to make your app safer and robust. Features such as audio, video, and voice recognition make it possible for the customers to authenticate themselves using their biometric data such as face or fingerprint.


More so, it not only offers fast and secure login to the users but also helps to detect suspicious activities. ML algorithms can easily track unidentified malware attacks, protecting your app from threats. Robust security mobile applications built with ML are in great demand among large enterprises serving major industry verticals. For example, mobility service providers use ML to deal with customers with fraudulent behavior. Banking and financial companies also leverage ML algorithms to inspect customers’ previous transactions. 


Final Thoughts


The use of machine learning methods is becoming the new normal. Mobile apps are no exception. We live in an era of data mobility, and the volumes of this data are rapidly increasing. Mobile applications require acceleration of information processing, and machine learning is an indispensable tool for productive and high-quality functionality. However, it can be possible only if you know how to use machine learning in mobile app development as efficiently as possible. 


At Creative Chaos, we have extensive experience in implementing innovative solutions and developing custom mobile products using cutting edge technology. Our individual custom build approach to each client allows us to build the most effective applications for your business and easily integrate any elements of machine learning. 

Arsalan Ansari

Arsalan Ansari

Associate Software Architect

Over the past 13 years, Arsalan has accumulated extensive experience in all aspects of development, from design to application deployment. His strengths lie in API design and software architecture. Arsalan’s experience in mobile is primarily focused around Android and iOS application development.