Our lives today are heavily influenced by the mobile apps on our phones. From things as basic as transportation to as complicated as financial asset management, mobile apps are there to make our lives easy. With a massive influx of apps in almost all categories and genres, the market is competitive, more than ever. With the race now in full action, competitors are trying to gain a larger share of the audience’s attention than others. In the attempt to get a slightly bigger share of the pie, the app developers and publishers are exploring ways they can enhance the experience for their app users. This is where Artificial Intelligence (AI) and Machine Learning (ML) come into the picture.
AI and ML have been at the very foundation of the recent development and progress in technology. Mobile apps with embedded ML and the maneuvers of AI are driving the evolution of customer experience.
ML initiates and facilitates the system’s functioning during user engagement and learning processes while AI is used in mobile apps to learn about the user’s behavior, habits, and other personal aspects. As a result, apps become more interactive and offer an intuitive experience to users. This tracking and learning of user’s behavior enables the apps of today to perform one of the most desired features, both by developers and users – tailored recommendations.
With the diminishing attention spans of the users, continuous engagement is becoming a gigantic challenge for business owners. To put out the right kind of content, at the right time, in the right frequency is essential to garner and hold the user’s attention. Today, mobile app developers design AI algorithms that can monitor the choices users make and then insert them into a learning algorithm with which the app attracts far more engagement by recommending the right personalized content that users are most likely to interact with. From leading e-commerce platforms like Amazon and Alibaba to mega entertainment and mass media platforms like Netflix and YouTube they all rely heavily on this science for their user engagement. If you have ever wondered how Netflix could possibly know which genre best fits the tastes of its users, it all happens thanks to ultra-modern recommendation engines. A recommendation engine is a futuristic AI & ML based data filtering tool that uses data and algorithms to filter its existing catalog of offerings predicting relevant items and products to the active or logged-in user.
Virtual assistants based on voice searches have now become an almost essential component for any digital ecosystem. Products like Google Assistant, Siri and Cortona are results of the now basic demand of modern users to get the required information as quickly and efficiently as possible. Much of these assistants are possible because of AI and its subsets like ML and natural language processing. Voice optimization has a huge impact on modern app development mainly because of the development in the Internet of Things (IoT) sector of tech. The IoT is not just limited to smartphones and computers, but now there are smart TVs, watches, lights, thermostats and whatnot. As homes become smart, consumers prefer all their applications in one device instead of switching from one device to another.
These days, businesses are focused on influencing decisions with experiences and ads that are targeted to trigger very micro levels of human behavior. With advanced user experiences created with AI and ML embedded apps, both mobile and web apps, businesses have the opportunity to garner much better returns on their investment. The power of 5G combined with AI and ML is expected to make this combination far more superior.