User Experience Design Trends to Watch in 2016


“Experience” might qualify as the most influential buzzword of 2015—especially when modified by “user” and “customer.”

That’s a good thing. The most forward-thinking companies were focused on delivering an exceptional customer experience all along, of course, but now the rest of the business world has caught on to the importance of that practice.

Similarly, providing an excellent user experience was always the point of great design, but now more companies recognize the critical role UX plays in delivering products and services, driving company growth, and advancing innovation.

So what’s next for UX? As one year draws to a close and another begins, it’s a good time to reflect on questions like that. So here are 3 design trends to watch in 2016.


1. Designing beyond apps and screens

Apps and the development of other new ways to deliver interactions via screens have become a staple over the past few years, and they aren’t going away anytime soon. But driven in part by consumer demand from increasingly tech-savvy users, designers are already starting to think past apps and screens. In 2016 and beyond, post-app and post-screen design will become more prominent.

Technologies like wearables and advances in artificial intelligence signify new ways to interact, and as technology becomes more consumerized—both in real life and as the subject of entertainment (e.g., The Minority Report and Her)—the cycle between a groundbreaking concept and a marketable product will grow shorter.


2. Simplifying the already simple

Bringing new ideas from insight to action entails overcoming many obstacles. Often a user’s adoption of your product depends significantly on their ability to instantly recognize value. In a world where the most popular websites and apps have an incredible amount of complexity, yet shockingly simple user interaction, continuing to simplify is no longer a differentiation, it’s a necessity.


3. Design as a core skill

In a fundamental sense, everyone is a born designer. Early humans had to design a way to survive in a changing, challenging environment, and the history of humankind ever since has been a story of more complex design leading to innovation.

In the Design 2.0 world, businesses will increasingly recognize the centrality of design to the enterprise and treat it as a core skill. Design competency makes people better thinkers, facilitators, and storytellers. In the coming years, more enterprises will put a premium on design skills, and HR organizations will start to offer design training to employees across all business categories, much as presentation skills classes are typically accessible to all.


If there’s one common element in these 3 trends, it’s an acknowledgement of the centrality of design to both great UX and delivery of an excellent customer experience.

In 2016 and beyond, users will increasingly complete interactions without apps and screens, innovation will be advanced by more effective storytelling, and more people will recognize the need for basic design skills, no matter what their role is in the enterprise.

In many ways, that future is already here. There’s never been a more exciting time to be a UX designer.

Machine Learning & Why it’s Important


Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.

So, machine learning is the science of getting computers to act “without being explicitly programmed”. But there is still a lot of programming behind getting computers to program themselves. Computers and their applications are being designed for change and adaptability. Programmers want their applications to respond to more than just intentional input.

Applications should be able to respond to external stimuli, big data, crowdsourced social activity, and a legion of other sources. What are some applications of machine learning? Machine learning is showing up in more areas than we might realize.


Machine Learning Around Us

Right now, on a roadway somewhere, a self-driving car is making right-hand turns (using its signal), braking (gently), and pausing for pedestrians (considerately).

The self-driving car is a prime example of machine learning put into motion, literally.

The self-driving car is heavily programmed, but not by itself. A small army of very intelligent people have spent a long time creating virtual reality maps, developing vehicular adaptations, and forming a new industry in order to help the car drive safely and reliably.

Yet, at the same time, the car does program itself.

The job of the [car’s] software is to figure out how the world is different from that expectation.

As the car figures it out, it adapts.


Machine Learning on Websites We Use

But machine learning isn’t usually as sexy as most of its applications. Right now, in a Facebook newsfeed near you, machine learning is controlling what you do, see, and interact with.

During Facebook’s tumultuous 2011, they switched from an algorithm called EdgeRank to a more complicated one. The machine learning of Facebook’s advanced newsfeed algorithm tries to individualize your Facebook experience based on what it thinks you want. You teach it with every linger, look, click, query, and interaction that you perform.

Here’s how Time explains it:

To ensure that those 300 posts are more interesting than all the rest, Facebook says it uses thousands of factors to determine what shows up in any individual user’s feed. The biggest influences are pretty obvious. How close you are to a person is an increasingly important metric, as judged by how often you like their posts, write on their Timeline, click through their photos or talk with them on Messenger, Facebook’s chat service. The post-type is also a big factor, as Facebook hopes to show more links to people who click lots of links, more videos to people who watch lots of videos and so forth. The algorithm also assumes that content that has attracted a lot of engagement has wide appeal and will place it in more people’s feeds.

When you clicked on Joe’s picture, or searched for “Joe B—” in your search bar, the machine-learning algorithm picked up on it. Tomorrow morning, when you open up Facebook on your phone, guess who’s updated profile picture will top your newsfeed?


It’s not quite that starkly cause-and-effect, but the principle remains true. Facebook’s newsfeed algorithm operates on machine learning.


Machine Learning in SEO

Machine learning is expanding everywhere. Although Google’s machine learning is focused on search improvement, they also advance machine learning in a whole breadth of applications. To say that machine learning is changing SEO is a bit anachronistic. Why?

Because machine learning is already a major part of SEO.

In fact, it has been for a long time.

Right now, however, it’s growing in importance.