Just more than a couple of years ago, both artificial intelligence (AI) and machine learning used to be considered experimental technologies in the context of mobile apps. Only leading apps could be heard trying something new with these technologies. Since then, we have come a long way.
Now both artificial intelligence and machine learning as its subset technology have become integral parts of a multitude of mobile app projects across all niches. Do artificial intelligence and machine learning really make significant differences with mobile app projects? Are you looking for answers as to how to make an app from scratch for beginners? Well, then this post is not likely to provide any answers. We are here to discuss the impact of AI and ML technologies, instead of anything else.
The Impact of AI on Mobile App Development
The impact of AI technology is felt all over the world. From app developers in Belgium to that of the Far East, everyone is conscious about the AI impact in turning mobile apps smarter. Let’s look at a few of these advantages.
Personalized Suggestions & Recommendations
Providing personalized suggestions and recommendations to app users is a key advantage of AI-based app development. AI is used by many mobile commerce apps for converting business through highly personalized product recommendations based on customer preferences, online behavior, and purchase history.
Personalized suggestions and recommendations powered by AI algorithms are increasingly being used by all kinds of mobile apps to enhance traction, engagement, and business conversion.
Automated Reasoning
App analytics these days need to decipher a huge amount of user data to derive relevant insights that can be used in decision making, app user experience strategies, and in-app marketing measures and activities. For decoding the meaning and patterns inside a huge amount of data, automated reasoning techniques are used.
AI-powered automated reasoning allows developers to understand the user pain points and accordingly incorporate enhanced functions and changes for carrying out different tasks. The GPS map used by apps uses powerful intelligent automated reasoning to guide the vehicle with a route map that takes the least time to reach a destination.
User Behavior Insights
AI technology by accessing a lot of user behavior data can decipher significant patterns offering significant data-driven insights that can be used to improve app user experience.
The virtual smart assistants of mobile platforms by utilizing natural language processing techniques can easily decipher the verbal comments and spoken words of the users. The mobile virtual assistants can also detect anomalies in user behavior, draw certain patterns and understand various leanings and preferences. Based on these user behavior data, user experience can be enhanced or changed.
Optimising Voice Interactions
The increasing impact of voice interactions in shaping the mobile app user experience is a tested and tried fact that cannot be undermined. Since voice search has already become a popular mode of web search and voice commands have become a common way to interact with mobile devices, the impact of AI is also shaping voice search, voice command, and voice interactions.
Natural language processing, robotics Intelligence, and detection of user voice patterns in different contexts and moods are allowing developers to tweak the voice interactions and voice searches to hoist engagement and traction.
The Role of Machine Learning on Mobile App Development
Machine Learning (ML) is basically a subset of artificial intelligence technology that primarily focuses upon drawing data-driven insights and specific behavior patterns from the user data. The technology incorporated into powerful algorithms can easily be utilized by the mobile apps to learn from user behavior, user intents, and user interactions and accordingly tweak the app interactions and user experience of the app.
In more than one way, Machine Learning comes as the most powerful technology to deliver a highly user-centric experience and cater to user queries and concerns with user-focused data, content, and services. Bridging the gap between user expectations and original app user experience remains to be the most important promise of Machine Learning technology for mobile app projects.
Let’s have a brief look at the key advantages of machine learning on mobile apps. You can also read this checklist on mobile app development.
You can also read this checklist on mobile app development.
- Machine learning is a key technology to advance the accuracy and precision of predictive analysis. Machine learning technology can be used to process a humongous amount of data for drawing the right insight or prediction about the things to come.
- In the context of mobile apps, machine learning algorithms continue to learn about the user preferences and intent over time and accordingly adjust the user experience and app output for the users.
- Natural Language Processing technology which is capable to recognise people by their voices can also be utilized in combination with machine learning to understand the deeper intent behind the spoken word and the underlying insights behind every communication.
The Use of Machine Learning Across Diverse App Niches
As of now, Machine learning technology has become popular through its so-called utilization or real-life output across multiple industries. Over the years, Machine Learning technology has played a key role in turning business apps customer-centric. Let’s take a look at diverse industry niches where Machine Learning played an important role.
- Data Mining
When it comes to mining huge amounts of data to detect patterns and different connections, ML technology can be a great choice. Data mining covering all facets of data can draw relevant insights based on user data, behavior, past interactions, etc.
Real Time Monitoring and Tracking
Tracking the financial data of companies is a key value proposition of ML-based apps in different industries. Companies use the technology for tracking their goods in transit or for managing a remote workforce.
Data Analytics
A robust and powerful data analytics engine fed with the user data across different OS platforms can further boost the effectiveness of the data analytics engine. Thanks to ML, deriving relevant data-driven insights based on past, present, and future data become very easy.
Interactive Search Function
Thanks to the ML technique search engines, e-commerce stores, and content-rich websites, and mobile apps can provide results that are highly likely to gain traction. This individualized search function based on machine-learned data inputs took app efficiency in addressing user intents to a whole new level.
Proactive App Security
Another major area of machine learning implementation in mobile apps is enhancing security by detecting anomalies in user behavior and by strengthening security safeguards proactively based upon threat perception from the user behavior.
Conclusion
From the above discussion, it is clear that both AI and ML technologies are here to stay and grow across mobile apps. They have arrived as crucial technologies for transforming app user experience and consequent user engagement and business engagement.
