How to Improve UX With Machine Learning?

Introduction

User experience (UX) is how a person or user interacts with a product, service, or system encompassing everything from ease of usage, and its usefulness to efficiency. Today, Machine Learning can provide an intuitive user experience through modeling, customization, clustering, and segregation. In this article, let's have a look at how Machine Learning is revolutionizing User Experience.

Why does User Experience Matters?

In the case of a business that needs to attract customers or to make sales via a website or mobile app UX is almost needed. The duration of time the user spends on these platforms, their search behaviors, buying trends, and many more are valuable data points for businesses. They want to increase user time on these websites and lure them to make purchases so that their business thrives. This can effectively happen if they have good UX on these platforms.

UX Increases ROI and Revenue.

By analyzing user data and its behavior UX can be improved. User data is how a user interacts with the website or mobile app. Any issues they face are highlighted for example.

• Issue while entering payment−related information such as credit card number, net banking, etc.

• Struggles to find searched product

• Difficulties with menu bar navigation

How Machine Learning is helping improve UX?

AI systems interpret human behavior and predict what customers would do next. These are all part of Predictive analysis which serve as data points for business to improve their platforms.

Let's see a few areas where ML is improving UX.

Provide Next-Level Personalization

ML−based personalization provides a scalable and unique experience to individual customers. The algorithms help organizations to provide a tailored experience to each individual rather than rule−based user segmentation. This enhances user engagement.

A few examples related to personalization are

• Personalized email targeting users based on their recent product searches

• Providing personalized rewards and discounts to customers.

Improved Recommendations

Businesses continuously want to grow. To achieve this they need to give good recommendations to the user to increase their revenue. A good recommendation can also help users as they have to spend less time searching for relevant products.

One such technique widely used is Collaborative Filtering which provides personalized content recommendations. One of the key highlights of this technique is to provide suggestions based on user activity and reviews or purchases on e-commerce platforms.

For example,

Let us suppose a student and fashion vlogger has rated a fashion outlet. Chances are likely that both of them share similar interests. So the organizations can recommend a product to the student that a fashion vlogger has given a very high rating say 9.5.

Improving Customer Service Speed.

Most users hate to wait in a queue to get their queries resolved. If organizations don't put up efficient customer service catering to the needs of the user their reputation is greatly affected especially in terms of after-sales service. To provide quick resolution most businesses engage chatbots and automated calls using AI in their websites or apps so that their valuable customers don't have to wait in queues to get queries resolved. Chatbots are much more scalable than humans and have an edge over humans in many cases when it comes to answering complex questions. Moreover, over time, the chatbot can learn from these questionnaires and evolve itself.

For example,

A chatbot powered by AI can answer simple questions and FAQs as well as the event can emulate a human being in terms of service.

Sentiment Analysis

Today, modern AI algorithms make use of facial recognition to understand human emotions through video analysis to deliver more relevant advertisements to users. Human emotions have always been complex to understand. But today ML algorithms are quite capable enough to understand basic as well as complex human behavior.

For example,

Advertisements targeted to particular customer groups by marketing agencies and media platforms.

Usability Testing

AI is a powerful tool for testing. It helps to evaluate various UX metrics.

Few of them are

• Users device

• Demographics

• Pages visited

• Session time

• Session length

Conclusion

No other technology can replace Machine Learning and AI in terms of enhancing and improving UX. Today AI is a boon to many such industries, which are in fact in awe of its capabilities. This is paced organizations' inclination towards AI and Machine Learning.