by socialadmin 3 years
Nowadays, we live in a world full of information but it’s hard to sort and find just the appropriate or credible details especially when it comes to the term “user experience”. But the real question is; how we can apply or at least achieve it to the fullest for which it’s important to understand at least some of the limitations before building a design. It’ll further help in reducing the gap between data scientist and designers, making the entire process more streamlined.
A data scientist is skilled in analyzing the most critical data set and extract some meaningful values. On the other hand, UX designers use the outputs generated by the data scientists and create workflows to enhance user experience as well as to generate the content of overall website.
It matters because ease of use and more appropriate content on the website and mobile application helps build more users and apparently extend sales. But to achieve personalized experience, we have to understand users first and without proper research, any decision can backfire or perform totally opposite to what you perceive about the target audience, their behavior on landing to your website. Create user profiles or personas of real people who will likely use the application and then think about the ways to minimize the gap between user and content.
Identify bottlenecks like Jeff Sauro has discussed in his blog “THE UX OF RESTAURANT WEBSITES” where he highlighted pin points which users face while placing their online orders on specific restaurant websites.
Machine learning is very helpful in understanding user groups and through grouping; you can determine which feature is suitable for particular group. This way, you can achieve more personalized rich experience for instance your website’s target audience is above 40, it’s obvious your design shouldn’t have colorful animations, rather clean background with solid colors.
Types of Learning Algorithms
There are three types of algorithms in machine learning to train your data.
Supervised means to guide when you know the output or what you are trying to predict. It is further classified into classification (outcome in the form of categories e.g. teenager, adult etc. on the basis of user information) and regression (outcome in the form of real values e.g. sale of product in future on the basis of current purchase history).
Unsupervised learning is based on unlabeled data. The results are determined by the closeness of actual outcome e.g. in a typical ecommerce website, a feature of recommended products is based on the purchase history.
Reinforcement learning is the process where an agent takes action to meet its complex objective or cumulative goal where agent improves through trial and error until cumulative goal has been achieved. In robotics, a robot learns to complete a certain task e.g. pick and place any object whether it fails or succeeds but every time it trains to get better results.
I have just peeled the first layer of onion in fact my keynote is to give introductory knowledge about how web development is getting revolutionized with the use of machine learning.