How Can I Use Machine Learning in My Mobile App

In the present day and age where all things are becoming digital, Machine Learning or ML is making a huge impact on mobile applications giving programmers the advantage of being able to create smarter, more efficient, and customer-oriented applications. Whether it be for sales, dieting, or entertainment, incorporating machine learning into your app can mean a much better experience for your customers – and, in turn, a better performance for you and your company.

 1. Personalized User Experience

Machine learning means that individualized, pattern-recognizing methods are applied to the traversal of data by mobile applications. Many streaming services, like Netflix and Amazon Prime, and music streaming services like Spotify also employ ML algorithms to read users' view history, preferences, and interactions to recommend content to watch or listen to. For instance, an application that helps users listen to music could use ML to determine the kind of music that a user prefers to listen to and recommend the appropriate songs or playlists to listen to. 

That is why with the help of machine learning you can increase user satisfaction and retention to make your app more interesting for every user.

 2. Predictive Analytics

Mobile application predictive analytics is one of the most significant and useful benefits of machine learning. In some cases, ML models use a large amount of data to define a pattern of the future result or pattern of behavior. This is very workable in applications concerning m-commerce, health, or even money-related affairs.

For example, an e-commerce application may leverage the predictive model for customer demand and provide an application that suggests products that the consumer may be likely to purchase. For instance, a fitness app could well give a user’s forecast on how close or far they are from attaining their fitness goals based on the data from the workouts they have been having and offer tips on how to do it better.

Win-win is not limited to enhancing the quality of the utilized product, but it also offers quantifiable data to help in making various decisions regarding its further promotion.

 3. Enhanced Search Capabilities

Machine learning can enhance the search experience within an application by allowing applications to provide better search suggestions. Stern is closely related to regular system search algorithms rather than exact keyword-based algorithms because, using ML, can help find content based not only on keywords typed by the user but the context as well. 

For example, ML can be used by apps to deliver more relevant search results depending on the users’ choice, interaction, and NLP feedback. If you have ever used voice search options in apps like Google or Siri, you’ll know how machine learning can assist with increasing the efforts of more accurate search queries based on natural language.

You can also use ML in your app’s search since it will assist users locate what they are looking for, thereby improving engagement.

 4. Increased Security 

Today, mobile apps are integrating machine learning capabilities primarily for the identification of cases of fraud and the advancement of security systems. Through processing of large volumes of data in real-time, the ML models can detect spurious activity and meanderings. For instance, a banking or finance app might employ ML algorithms to track users’ monetary transactions compiling a profile of the user’s activities to detect discrepancies that may be on the part of fraudsters. It can notify the user or take precautions automatically if an unlawful activity is identified, for example, the account will be frozen.

The employment of AI in mobile applications creates additional layers of security, which keeps your users safe, with the additional advantage of increasing the trust in your app.

 5. Chatbots and Virtual Assistants

There is no doubt that one of the most widespread practices of using machine learning in the creation of mobile apps is the creation of chatbots and virtual assistants. They employ NLP and machine deep learning that allows them to read and answer the queries posed by the users without having to involve the customer support team.

For instance, Google Assistant, Amazon’s Alexa and Apple’s Siri are all products of machine learning that can analyze voice and perform biometric functions such as fixing an appointment, sending a message, or informing the user of the weather conditions. 

By incorporating machine learning-based chatbots or virtual assistants in your mobile app, you can offer users 24/7 customer support and enhance their overall app experience.

 6. Image and Voice Recognition

Automated technology has been able to develop and improve image and voice recognition thereby enhancing flexibility in the facility of mobile applications. They include document scanning, real-life object recognition, and providing AR in various applications.

Just like that, voice recognition becomes a necessity in most applications ranging from voice search to navigation. Such apps as Google Translate employ ML image recognition to translate texts from images right away. 

By the use of machine learning, especially in the identification of image and voice, your app shall be able to give its users a more creative interface as compared to the other apps, in addition to the convenience of the physical interface presented.

 7. Personalized Marketing and Push Notifications

Personalized marketing using machine learning can significantly improve the outcomes of mobile applications. Users’ actions and profile characteristics indicate the kinds of posts, materials, advertisements, or objects they would appreciate, and ML algorithms can show these predictions. This data makes it possible for marketers to send localized and topical push notifications, messages, and advertisements to customers.

For instance, an e-commerce mobile application may apply ML in that it will follow the user’s browsing history and notify the user through a push notification that similar to the product they were viewing has been reduced in price. The above level of targeted engagement thus improves the conversion possibilities and can impact the sales of the business.

Get a taste of Machine Learning with Quantum IT!

There are countless opportunities to improve the characteristics of applications, as well as their usage by a user or even their general performance via machine learning. From providing individualized content to enhancing safety measures and developing more convenient search options, incorporating ML in mobile applications can make you stand out in the current day world. Machine learning is a perfect way of creating tailored user experiences and even predictive analytics, improving the performance of business applications.

At Quantum IT, we have an array of professional services that we target to assist organizations to benefit from advanced technologies such as machine learning to app mobile. Currently, our team of professionals is ready to help you implement machine learning into your app more effectively to enhance users’ interest, boost sales or provide a better experience. Now here’s an opportunity for us to assist you in making the most out of your Mobile Application through the implementation of Machine Learning. Contact Us Now!