How Machine Learning Modeling Improves User Experience
How to win more customers and develop your company? That's a burning question for many business owners, especially during crisis times. Every business starts to search for new ways to improve user experience to attract customers, increase sales, and boost customer loyalty.
Many companies are already using machine learning modeling to achieve their business goals. If you do not want to stay behind your competitors, you should also implement this technology. In this post, we are going to tell you how ML can help you improve user experience.
What is machine learning?
Before we start talking about how machine learning is used to influence user experience, you need to understand this technology.
Machine learning is a branch of artificial intelligence science. The main idea of ML is to study the methods of creating programs that allow computers to learn. "Intelligent machines" can analyze information, perform high-accuracy operations, and make predictions without human interference.
All of the ML methods can be used to make your user experience better. If you wish to discover more about different machine learning algorithms, read the ML software engineers' blog.
Top 5 ways to apply ML modeling to improving user experience
Machine learning modeling can and should be applied to make your customers happy. This technology indeed demands an investment: you will have to hire a professional or purchase one of the off-the-shelf solutions like the ones from Amazon or IBM. Nonetheless, this investment will pay off. For example, the research made by McKinsey predicts that ML will help manufacturing businesses reduce material delivery times by 30% and achieve 12% fuel savings by optimizing their processes.
1) User recommendation systems to offer relevant content
It is impossible to imagine an e-commerce website or news portal without a feed of recommended content.
A recommendation system is an ML-powered software that learns to predict whether the user will enjoy a movie, book, or a selling item based on their interests and interests of other people in the same target group. Recommendation systems can be used anywhere: on entertainment portals, social networks, e-shops, dating websites and so on.
Some entrepreneurs believe that this software is too expensive and complex to implement; only world largest brands like Instagram and Netflix. Netflix can afford it.
Netflix software is indeed one of the best recommendation systems for today. It divides users into more than a thousand different groups according to their demographics, interests, likes, and dislikes. The company's founders do not fully disclose how the algorithm works and continue improving it every day. The importance of the Netflix recommendation engine is hard to overestimate. Over the past five years, they grew from a small chain of video rental stores to the world's most massive video entertainer (after Youtube).
However, any e-business can win by implementing a recommendation system.
People that come to your app or website would like to get the most out of their experience. Regardless, it can be hard for them to navigate through a variety of items. Based on users' experience with similar interests, you can help them find what they are looking for.
2) Discover new audience segments
You can significantly improve the user experience if you manage to segment the audience more accurately and offer relevant content to each of these groups.
Before the internet and ML, target audience research was done manually by human marketers. However, today, when there can be hundreds of thousands of users on the website daily and millions of transactions, even a whole department of business analysts cannot cope with that amount of data without specialized software.
Specialized software can help you to sort out the groups that you had no idea existed. Knowing about them, you can offer them some content to make them happy.
3) Look for hidden insights into the users' data
What will happen if you give all the users' information to a smart algorithm and let it process it? Most likely, you will discover something interesting.
The machine observes all the events in your data, such as all the purchases of one client. When they buy something, the program tracks how often, what items were in the last purchase, and compares the list to other customers' similar assets. This way, the computer learns to uncover some patterns that a human would miss, but that can improve user experience.
A couple of years ago, the world was struck by Target's AI system. Their software managed to predict a customer's pregnancy before she and her family knew it based on her purchases. Today every other company benefits from machine learning modeling, improving their user experience.
4) Uncover and prevent potential problems
Neural networks and other algorithms for unsupervised learning allow searching for patterns in the unlabeled data in real-time. This ability of ML is actively used in banking and healthcare to search for weird anomalies that can contain important information. For example, this helps to uncover fraudulent transactions. Such systems are implemented at many large banks, for example, Bank of America. If the system discovers suspicious activity, it will block the card and report it to the human employee. This measure allows them to boost their systems' security. A similar solution can be applied in any other niche where the security of transactions is essential.
5) Form personalized user experience
Using machine learning, you can avoid old-fashioned A/B testing in favor of something more interesting. Imagine an algorithm that offers every user an interface that is more likely to interest them. The striking truth is that it is possible to do even today.
Forming a personalized experience will allow you to satisfy the needs of your audience better.
Where to start learning ML?
Suppose you have a desire to learn machine learning technologies. In that case, to deeply study the subject, you should understand mathematical analysis, linear algebra, and optimization methods. Also, it is desirable to know the basics of computer science and some programming language. Machine learning usually uses R, Python, or Matlab.
To start with, choose the classic Andrew Ng course on Coursera. Its main feature is its exceptional practical orientation. Extensive mathematical knowledge is not required.
If you watch all the lectures and solve all the tasks, then it is guaranteed that you will put all these things into practice. In particular, you will master linear regression algorithms, logistic regression, support vector machine, neural networks (for classification), K-means (for clustering), PCA (for dimensionality reduction), anomaly detection, and learn how to build a recommender system. In the last week of the course, you will also be taught how to build a system that will find and decipher lettering in pictures or identify people.
Another great resource is a Harvard-based course taught by David Malan called CS50 Introduction to Artificial Intelligence. During this entertaining course, you will learn all the basic concepts behind machine learning solutions and will solve plenty of practical problems.
Machine learning is a powerful tool that can significantly improve your user experience. Regardless of how small your business is, you can win by integrating a recommendation system or a real-time data analytics tool. Learn machine learning yourself or hire an expert who will take care of ML integration for you.
About the author:
Yulia Gavrilova is a machine-learning enthusiast from software development company Serokell. She enjoys diving deeper into complex matters and breaking them down for any reader to enjoy.