In Part 1 of this series on how to design for mobile touchscreens, I told you all about the history of touchscreens, how capacitive touch works today, and the research I have been conducting to find out how people really interact with their touchscreen phones and tablets.
In Part 2, I discussed the first five of my ten heuristics for designing for touch in the real world, on any device:
Today, we’re experiencing a growing torrent of big data. Data for our retail purchases, Internet searches, social-media posts, and even our commutes to work reside somewhere. Not only do we cast a shadow on the ground when we walk in the sunlight, we all have data doppelgängers that show both our current state and the history of our lives. Our own data interacts with the data of other people—such as those who buy the same books on Amazon that we do or our friends on social media. All of this data interacts with the companies with whose products and services we engage.
Through machine learning and artificial intelligence, organizations can use big data to predict our next actions—sometimes even better than we can predict them ourselves. The implications of big data are enormous—enabling us to view suggested products while on a retailer’s Web site, receive recommendations to connect with people who we might know on social-media sites, and benefit from smart IoT devices that gather data from us and those who are similar to us, then act accordingly. Organizations in the healthcare and financial arenas use big-data systems to spot potential adverse events, while also pinpointing scenarios that can bring increased profits and positive outcomes. Read More
In the ideal interaction between humans and computers, technology handles the routine, mundane tasks at which it excels, allowing people to focus on higher-level, more important aspects of achieving their goals. Nevertheless, until recently, technology’s role in providing user assistance has been limited to providing traditional online Help and on screen instructions. However, as technology becomes ever more powerful, it increasingly has the ability to offer more proactive user assistance and even perform certain tasks automatically, easing the cognitive load on the user.
At its best, proactive user assistance can be very helpful. At its worst, it can be distracting, even annoying to users who receive either unwanted assistance or incorrect information. Remember Clippy, shown in Figure 1, the animated-paperclip assistant in Windows 95 that irritated legions of computer users? There’s nothing more annoying than a system’s automatically taking unwanted actions or constantly offering undesired suggestions. Read More
In Part 5.1 of this two-parter within my larger series on applied UX strategy, I covered the benefits of using a shared language between business and design, then began my discussion of a three-stage model for solving business problems through design that progresses through the following three stages:
When product designers keep in mind why a company chooses to solve particular user problems and how their solutions will impact the business—at every stage in this model—the focus of their work shifts from creating design deliverables to defining product strategy. Design becomes a strategic role whose goals are to increase key business metrics and drive innovation.
Now, in Part 5.2, I’ll delve further into this transformation of the product designer’s role, covering Stages 2 and 3 in depth. Read More
This is a sample chapter from Ben Coleman and Dan Goodwin’s new book Designing UX: Prototyping. 2017 SitePoint.
After reading this chapter, you should be able to make an informed decision as to what approach will suit you and your project. Read More
I’m going to open my new column Evolution of XD Principles with a quotation that actually contradicts my position:
“If you do it right, it will last forever.”—Massimo Vignelli
He’s wrong. Massimo is a very well-known, well-respected Italian designer who has impressed the world by successfully innovating products in a variety of disparate product spaces. But he’s wrong.
Design should always accomplish one key thing: demonstrate a thorough understanding of the people who will engage with a solution. A design should accommodate the well-defined mental model of those engaging with an experience. However, a challenge for UX designers is this: mental models represent collections of knowledge—and knowledge is never static. Forever is a fallacy.
With this premise in mind, my goal for this column is to write a series of articles that challenge traditional experience-design principles in a way that explores next-generation—and forgotten, last-generation—experience-design strategies.
Join me, as I explore such topics as why ugly products sometimes succeed, how some companies can dictate rather than accommodate usability patterns, and the hidden value of a user experience with a tinge of dishonesty. I’ll be leading you on a journey that will take us off the beaten path—one on which the only constant is change. Read More
In this edition of Ask UXmatters, our expert panel considers the contributions of UX designers that are most and least important to the product-development process. Can we generalize about the value of UX designers’ contributions to product teams? Or is the value a UX designer provides unique to that designer? How can UX designers exponentially increase the value of their skills and contributions by inculcating an experience-first culture into a multidisciplinary product team? How can product teams make meaningful work?
The panel discusses the importance of UX designers’ being involved in the product-development lifecycle from the very beginning of a project, engaging entire product teams in the UX research and design process, and applying discoveries from research throughout the design process. Our expert panel also contemplates how UX designers can take a more active role in the development process, as opposed to simply executing requirements from product management. Read More
If you’ve ever struggled to find user-research participants, you may have wished you had a list of people who have expressed an interest in taking part in future user-research activities. A user-research panel is exactly that: a list or database of potential research participants—who have given you their contact details and maybe some other information about themselves—that you can recruit for specific research activities as they come up.
We’re enthusiastic about user-research panels, but we’re also realistic about the amount of work they involve. So, in this column, we’ll briefly touch on the benefits of user-research panels, then present seven questions you should consider to ensure that your user-research panels are successful. Read More
You’ve just completed a readout of your latest ground-breaking research, presenting an hour-long slideshow, and hopefully, you’ve wowed your audience with what you’ve shown them. But all too often, after you’ve reported your research results, everyone returns to their workspace and develops a serious case of insight amnesia. Stakeholders quickly forget the juicy morsels of information that would make your company’s products better. Your insights remain stuck in your slide deck and may never again see the light of day.
There are two questions that arise from this dilemma: First, how can you make your research insights more readily available to product teams so they don’t have to slog through your deck to find them? There are multiple, well-known solutions to this problem. The second problem, which is the focus of this article, is how can you ensure that your product team uses your research insights? Read More
Envision coffee machines that start brewing just when you think it’s a good time for an espresso, office lights that dim when it’s sunny and workers don’t need them, your favorite music app playing a magical tune depending on your mood, or your car suggesting an alternative route when you hit a traffic jam.
Predictability is the essence of a sustainable business model. In a digital world, with millions of users across the globe, prediction definitely has the power to drive the future of interaction. Feeding a historical dataset into a system that uses machine-learning algorithms to predict outcomes makes prediction possible. Read More