DON'T MISS A UX THING

Fabricio Teixeira reminds us of the basic evolution of products.
Successful products usually start from a good solution to a small and focused problem. Over time, the product team realizes that additional problems need to be solved. In response, they develop additional product components and try to answer additional usage scenarios until the product becomes too complex. Interestingly, we almost never download components from the product.

The need to expand the product parameters usually stems from customer demands, the identification of additional business opportunities and the assumption that if we expand the scope of the product, the business opportunity will also expand.

After we set out with broadening the product scope by adding capabilities, it becomes very difficult to go back. From that point, when we reduce the product's capabilities, it becomes very difficult to answer the question of how many people use the component and whether its change will produce resistance. In addition, it becomes very difficult to prioritize the removal of existing capabilities higher that are higher than the creation of new capabilities.

As usual, a new and fresh product comes up, which can do exactly what is needed in a simple and precise way. In the process taking all of our customers…

When thinking of successful products such as Dropbox, Trello, Slack, Lyft and even Twitter, every successful product has been able to focus on one precise business value that guides every other product components including design, branding, communication, components and structure.

The idea of a targeted product that does less and better is not a new idea. For example, Dieter Rams, the German industrial designer who is recognized as one of the most influential designers of the 20th century, is famously quoted for saying "Less, but Better".

This is an opportunity to emphasize the principles that help to maintain a focused product:

• Identify the essential features that your product needs. Now cut them in half and cut them in half again. This way of thinking will help you identify and distill what really matters.

• By analyzing product usage information, you can identify your significant components and locate the components that are not being used.

• Set the maximum number of components that you should not exceed.

• Most importantly, you must learn to say "No!". Try it a few times and you'll find that over time it gets easier.

Fabrizio recommends that you stop for a moment to look at your product and see if you are still focused.

You'll find that this way of thinking will help you make better products.

Eugen Eşanu revisited the legendary book Don’t Make Me Think by Steve Krug and checked if we were able to implement the ideas brought up in the book.

The article in UX Planet begins with an important quote by Jacob Nielsen. He said that the human brain's ability does not change every year. Therefore, the insights we have about human behavior have a long shelf life. Things that were difficult for us twenty years ago are still difficult for us today.

• We continue to invent new solutions
While we try to making exciting new innovations and feel that doing what others do is wrong, users are impatient and want to learn as little as possible. Before you invent the next thing, it is important to ask yourself whether your users need anything new in the context of the product in question.

• We continue to write a lot of text
Today websites tend to include a lot of text; however, users tend to scan rather than read. Users view written content as a task they are trying to complete. As such, they will read only what they must.

• How do I get users to read?
Using lots of headers, short paragraphs, bullet lists, and carefully highlighting important terms on the page (do not overdo it) will help encourage users to read.

• We continue to write instructions for users
Self-explanatory product design is no longer an option. It is the only option. We need to minimize instructions and explanations to zero and design products that are obvious to use. It is important to remember that our users do not care how our product works. They just want to be able to complete the task at hand and move on.

• We continue to let opinions and emotions lead us
It's hard to avoid the moment of thinking that you are the users too and know what is good or bad.

• We continue to ask the wrong questions
In everyday conversation, we tend to ask - "Do people like drop-down menus?” instead of checking whether it is the best way to help people navigate the current task. Instead of asking what people what they like or prefer, we need to look at our solution in the context of the task at hand and see if people can successfully perform it.

• We continue to make people think...
Effective product planning should make sure that people who use it will never have to ask questions like –
o Where am I?
o Where do we start?
o Where the hell is the button?
o What does this term mean?
o Is this an advertisement or part of the page?
o Should I click on it or not?

People might like challenges; however, not in the context of trying to complete the task at hand.

The principles that Steve Krug presents in the book "Don’t Make Me Think" remain relevant and it is worthwhile to go back sometimes and remind ourselves not to miss the basics.

A study was conducted to test eye-tracking and comparing different kinds of click-ability cues. The results show that week signifiers have an impact on the way that users comprehend web pages. 71 General web users were recruited to take part in the experiment. Nine different sites were tested. A "strong" and a "weak" version of the same sight were compared side by side. The participants were asked to find a specific UI element that was meant to be clicked on. Once found they would call out that they found it and then stop. During the study, the participants eye movements were tracked. The two key components that were measured were "fixations": when the eyes linger on one area of interest, and the amount of time it took to complete a task. Essentially, it took longer to find the click-able parts of the weaker designs this increases the likelihood that people click on something other than what they were looking for. Likewise, there were more fixations associated with the weaker design. In general terms, this means that users were less engaged with the web page. Less engagement means bad news. This article offers a significant amount of visual data representations to back up the research that was conducted. The fastest way to summarize this article would be to say that: the weaker the clickability, the more time is wasted for the user and the more uncertain users feel; this leads to depleted traffic over time.

This article by author Fabien Girardin discusses the duties in his position as a designer at "B.B.V.A. Data and Analytics Company. He assists in the design of systems to provide enhanced user interactions of various software products by using comprehensive machine learning methods with teams of fellow data scientists with the company. What data scientist and designers in his company like others are doing in the new are of machine learning is upgrading their design systems into systems that uses machine learning by use of artificial intelligence, to have software in items like online shopping, banking and decision recommending predict the user's product preferences, selections, and purchase decisions based on data the machines learn from the users. In this article, Mr. Girardin’s goal is to improve on user experience with design systems his company is responsible for. He believes the new practice of providing the enhanced user satisfaction will beneficial for the customer and the company for 3 reasons. The first is that it will create new types of customer interactions. This means as new technology and software comes out, it will be the"systems designers plus data scientists" jobs to work together to create ways for the machine to learn from the user so that data can be gathered to better understand user preferences for example, movie and music genres. In other words, the more the customer uses the machine more the machine learns. The second reason is the evolution of the relationship between the user and the machine. In this article, Alexa is used as an example, and in its marketing it is used to do everything from turning on the lights to ordering pizza. It has a human and machine helper relationship and this is done by machine learning as the user continues to use the Alexa device. The third reason is in the teamwork relationship between the designers and data scientists." In the field of machine learning, "systems designers plus data scientists will be need to work together to create the system. The designers to create the user friendly interface and the data scientists enable the system to learn from the data it collects. In conclusion, the subject of machine learning is clearly explained in this article.

After delivering a world-shattering report on usability that was fully hyperlinked with reproducible methodology, the expectation was that academics would be praising it all over the world but instead, nothing happened and a full two years afterward, none of the original findings had translated into product action.

This newly-minted researcher asked himself ‘why didn’t that work??’ and it set him on a six year journey that is still going forward. This question has been transformed into something more complex. It has gone from why didn’t it work to what does it take for research to positively influence product and design and how do I do that? Of course, a complex question has a complex answer.

It is a broad interplay of dynamics of people, structure and processes. Success lies in the mastery of the research process, technique and ability to influence the team.

This can be done by thinking which means process mastery and ownership of the research process, execution which means technical competences in technique, method and output, and impact which means organizational influence, empowerment, alignment and direction. The research process is somewhat linear: first comes figuring out what to learn, then deciding how to learn it, followed by uncovering and observing evidence, then making sense of what was learned (synthesis or insights) and deciding how to act on what was learning and finally ensuring consistent action.

There are three levels of researchers. At a junior level, the process mastery focuses on who, what and when, the technical competence focuses on recruiting, interviewing and interview notetaking and debriefing, observation, data collection, surveying, user testing, and simple reporting. Influence should be making a credible report, a fair and honest judgment of product and design and interaction level and usability authority. A mid-level researcher should add what and why to the process, project planning and management, structured design and research methods and complex synthesis to technique and embed and partner with functional teams, empower other project teams, reframe and focus research questions and develop a respected point of view on product level decisions.

A senior level researcher should add why and the ability to shape, reframe or reject the entire process accordingly to process mastery, centralizing customer feedback, roadmap planning, framing and storytelling, and workshop facilitation to technique and strategic partner to product design and functions, reshaping higher order processes, centralizing and unlocking existing knowledge and direct organizational research focus to influence.