Introduction to Machine Learning Technology
Technology, which has gigantic promises for bringing computing machines closer to human intelligence, is machine learning. Machine Learning is an area in the field of Artificial Intelligence that can solve a broad range of computational tasks and can significantly improve the user experience. As computers are getting smarter and efficient in interacting with humans, we are slowly entering into a new era where computational machines would have more control on human actions as compared to past.
Machine Learning is a completely new idea for future. We are already getting benefit every day from ML. By paying attention we will observe its use in the daily tasks we complete especially from mobile devices. We can define ML in simple terms as software created with the purpose for improving its behavior based on previous activities.
By incorporating machine learning, Mobile Apps allow better user experience and interactions. Learning from user behavior and use patterns and accordingly adjusting to the user situations and preferences is paving the way for customization in mobile apps. In fact machine learning is part of artificial intelligence but it refers to algorithms that help the computer to learn from previous activities.
Machine learning is paving the way for better utilization of resources for app marketers as well. With advanced device sensors and relocation capabilities of today’s machines, mobile apps are continuing to get smarter. So, this is high time we evaluate the future scope of machine learning for mobile apps.
What Machine Learning Technology brings to Mobile Apps
Taking into account that machine learning is suitable for predictive actions we will take a closer look at what makes this technology a good fit for mobile apps.
It is very hard to match Mobile App’s functionalities with different groups of users. Think about transportation mobile apps which deal with both clients and drivers or kids mobile apps where it is need to convince parents and children about the benefits provided by Mobile App. The answer is to analyze the data with the help of machine learning and to offer everybody what they really want.
- Product Search
We already experience it many top e-commerce sites. Just when we search or buy a product, based on our previous purchases and browsing history, some products are suggested. The same is happening across other business websites as well. Some of the tools that make an app understand the user behavior include query understanding, ranking, user favorite determination, etc. Knowing the user intent also require knowing the user situation, location and constraints if there is any.
Offering relevant most results corresponding to product search and offering most succinct suggestions related to the search is most important for e-commerce mobile apps, just because small screen real estate make users more impatient and less attentive to scroll down all the way to the last result. So, just to prevent your visitors go away without finding what they are looking for, you need to allow machine learning to fine tune the search results and suggestions
- Product Recommendation
Recommendations are built around filtering methods, site content analysis, purchase patterns, user behavior and also the business logic, a brand implements. Recommendations using this will surely make the answers more relevant.
- Machine Learning for Healthcare Apps
Healthcare apps already understand the huge potential of machine learning for the sector and respective apps. If a machine learning algorithm has access to millions of healthcare database corresponding to every different disease, it can actually suggest a perfect path of treatment and medication. For example, IBM Watson having such a robust database of cancer patients can actually make a better diagnosis of the disease than even the qualified medical professionals.
Similarly, fitness tracking and consumer healthcare apps by tracking the regular health and fitness data of millions of people can offer valuable trends concerning lifestyle related diseases and accordingly can recommend treatments.
- Fraud Control and Security
Machine Learning can really help making security arrangements and fraud control mechanism better and stronger. Machine learning algorithm within crucial apps can evaluate user behavior and all sorts of irregularities to assess the most probable frauds and security vulnerabilities in the making. While a whopping $32 billion worth of frauds occur every year making an increasing number of financial transactions vulnerable, an app with machine learning algorithm can detect such frauds and help to build a better defense system.
- Trend Forecasting
Every e-commerce brand needs to continuously understand changing trends and react quickly with matching products and services. However, between past season sales and the upcoming trends, there lies a huge difference. Big Data and Machine learning can however aggregate these trends and use sales information from different sources (social media, digital reports, blogs, etc.) to make predictions in real-time.
- Fast and Protected Authentication Process
Applying machine learning in conjunction with the different types of recognition (including the newest one – biometric) to pass user identification and authentication processes. It’s a good decision for any kind of mobile apps, including e-commerce.
This technology is widely used in apps like ZoOm and BioID, which offer an easy way to log into other apps and websites. Nowadays people are tired of keeping in memory logins and typing lengthy passwords, so why not to make it easier and faster for them?
As we have discussed, machine learning is an innovative technology, which can be useful for any kind of mobile app. So, the promise of machine learning for mobile apps will continue to grow and nothing can stop it from becoming the trend to have the biggest influence on the mobile apps of future. The spectrum of usability for machine learning will encompass every app niches in the time to come.
Every year the market of machine learning grows, so we are going to watch it among the mobile UX trends in upcoming years as well.
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