Applying Machine Learning In Mobile Applications

Artificial Intelligence (AI) and Machine Learning (ML) have completely transformed today’s mobile app development industry. This futuristic cognitive technology is being used in Kuwait mobile apps to better understand human behavior and assist and entertain users. We may not realize it, but machine learning has been incorporated by app developers in a variety of apps we use on a daily basis. From food delivery apps that advertise restaurants of our liking to on-demand taxi apps that use real-time location tracking, ML plays a huge role in making our everyday life easier. Moreover, due to its high accuracy, machine learning has the ability to predict and guide smart actions without the need for human intervention or any special programming.

In the next decade or so, Augmented and Virtual Reality apps, cloud-driven apps, intuitive m-commerce apps, and the integration of smartphones and iOT devices is expected to become the new standard. The main goal of implementing AI and ML in mobile apps is to leverage powerful features and provide a smooth user experience. It does this by constantly learning and re-learning based on the user’s daily activities, and then generating a personalized experience for each user.

How AI and ML techniques are implemented by app development companies in Kuwait in diverse fields:

Data Mining: Data mining is the process of collecting, storing, maintaining, and analyzing relevant data from large datasets in order to identify non-obvious patterns and connections. Such analysis of big data allows for the discovery of obscure customer behavior patterns, which is near impossible for a human to analyze. Client data based on gender, age, social media profiles, how often they visit your app, etc., can be collected and categorized, and then used to design custom applications and services. In order to facilitate the analysis of data and gain valuable insights about your app’s users, you can apply machine learning by either building your own custom solution or using the ones provided by Google, IBM, or Amazon.
Personalization: Even the simplest of mobile applications can transform customer experience when equipped with a machine learning algorithm. Customer recommendations are provided after analyzing various sources of information. These sources can range anywhere from social media activities to credit ratings. You will not only know who your customers are, but also what they like, how much of it they can afford, and what words they use to talk about different products. These insights will help you classify customer behavior and use the resulting classifications for target marketing. Target marketing allows clients to access content that is specifically relevant and enticing for them, giving an impression that the mobile app has been customized for them.

There are numerous apps today that use machine learning for personalization. Food delivery apps use bots to take orders and provide suggestions based on user preferences. Ride apps provide users with an estimated cost and time of arrival. Healthcare apps can forecast headaches and recommend prevention strategies. Fitness apps combine sensor and genetic data to customize individual workout programs.

Advanced Search: Apps that use machine learning learn to display the most relevant results by analyzing the different queries put up by customers and then prioritizing results based on those queries. User data like screen scrolls, clicks, and search histories are also gathered along with behavioral data. This aids in the delivery of a customized ranked search result.
This feature is all the more important for mobile e-commerce applications. Because handheld devices sport small screen sizes, only a few products can be displayed at a time. If users don’t like what they see, they will have to keep scrolling down. So, it’s pivotal for the displayed products to be highly relevant to the entered search query. Machine learning tools can also prove useful in providing spelling correction, voice search, and gestural search options.
Security: Machine learning can help streamline security and authentication for almost any app by implementing features such as audio/video/voice recognition, biometrics, face detection, and fingerprint access. These features aid in fraud detection and ensure secure access to confidential information. An application equipped as such can also be used for secure data transfer and file storage purposes.

An app like BioID for example, allows users to set up security locks to various websites and apps through their fingerprints and Face IDs. Machine learning applications also have the capability to detect and ban suspicious activity and prevent bad traffic from entering one’s mobile device. It can prevent and battle various fraudulent events by building a defense system that monitors online activity and triggers alarms.

Chatbots: ML-powered chatbots can help communicate ample amounts of information with a wide customer base in a very short span of time, all with zero manual errors. These chatbots run 24x7 and answer product questions right within the app. This way, they can simultaneously save time and money, and increase customer satisfaction. Moreover, HR can save on valuable time that was previously used to process repetitive employee requests.

Even though machine learning implemented by app development companies in Kuwait tremendously benefits mobile apps by making them far more efficient and effective, it comes with its own setbacks. If the size of the sample data is not large enough the machine learning algorithm is bound to be imperfect. However, solutions like hard sample mining, data augmentation, and data addition imitation, when implemented correctly, can avoid the risks of misinterpretation of digital information by mobile apps. In fact, there are quite a few diverse and user-friendly ML-powered mobile apps:

Snapchat: Snapchat filters combine machine learning algorithms with augmented reality to detect faces with all of its features and create 3D masks.
Google Maps: User’s location data is aggregated and applied to predict parking slots and gauge parking difficulty,
Netflix: Machine learning algorithms make decisions for the users through a recommendation system, based on explicit and implicit data. Once the algorithm gets acquainted with user reviews, ratings, search requests, and behavior, it can offer filtered and specialized content over time.
Spotify: Spotify’s machine learning model uses collaborative filtering to compare multiple playlists created by users. It also uses a natural language processing scheme and evaluates data from audio songs in order to make suggestions.

These machine learning processes, including mining, tracking, and predictions, play a crucial role in health, finance, entertainment, and m-commerce sectors. Mobile apps equipped with machine learning do not only drive innovation and functionality, but also help create meaningful and personalized experiences. It is undoubtedly a game-changer for the mobile app development industry. Design Master, skilled in Android and iOS application development, can help you reap the benefits of machine learning and get ahead with the times. Let one of the top mobile app development companies in Kuwait steer you towards the future: Contact us