Our senses are overloaded with information, and it’s only going to get worse. So how do we build experiences that communicate the right information without overtaxing users’ mental capacities? Media technology companies are battling for people’s attention, and so far the solution has been about showing them more of the right information vs. showing them the right amount of the correct information. The former is a strategy of making content available whereas the latter is an exercise of restraint.
Apple, Google Facebook (AGF) have all announced new features that will help limit and/or monitor their users’ screen time. The issue with this approach is that it moves the problem up a layer. Instead of showing users the right amount of information, screen time monitoring creates an additional task to check how much information users are consuming. In the end, this is extra taxation for the user.
I recently worked on a project that required us to design a dashboard to visualize competitive intelligence. The final design was intuitive and supported the essential tasks and features. The issue, however, wasn’t whether the intended audience could use it, but rather how much effort they had to invest to get something valuable in return. Even with the right information displayed correctly, our end users weren’t getting the correct answers right away. Good design is about providing the right answers, not more information.
Here are some suggestions on how to design for answers
1. How to approach: Categorization
Categorization is the foundation of intelligence. The ability of humans (and machines) to organize text, objects, and situations into a specific category is a good judge of how well he or she will perform at a task. We use multiple models to organize objects into categories, whether that is using exemplars (memories), prototypes or a rules-based system. More importantly, how we categorize is dependent on what we are expecting. We have thousands of expectations that are changing all the time, and all are dependent on the context. For instance, if I’m on the couch watching TV, there are a range of stimuli that I might be expecting, i.e., my phone might ring, my dogs might start barking, etc. What I’m not expecting is a bear to walk across my living or my lights to suddenly flicker between blue and green.
In Experience Design, categorization is the most critical step in the discovery process. We categorize pages, the information on the pages and functionality to create usable websites or e-commerce experiences. However, we need to take categorization a step further, especially when it comes to designing dashboards.
As I mentioned above most dashboards fall short on providing the right answers. This limits them to a unique set of users with a background in analytics. Even consumer-facing websites and apps like the Apple Health Dashboard lacks the right answers. I don’t care about steps, kcal, flights climbed. What I really want to know was whether I did enough exercise for that day and how I can improve. Yes, I can set goals within the app, but again this requires additional effort.
Providing the right answers requires an additional layer of intelligence that machine learning (ML) can solve. By understanding how each user categorizes objects (exemplars, prototypes, and rules) and what categories or answers are most important to them, an ML driven experience can get to the right answers. For example, instead of showing me kcals, steps, stairs, and floors, what if the dashboard over time learned what I classify or categorize as healthy. Then it could only tell me whether I reached that state for each day. With analytics dashboards, what if instead of showing every business and marketing KPI relevant to their goals, we understood the one problem that keeps clients awake at night and designed a dashboard experience that only told them that one thing in the right context.
How to show and tell the answers:
a. Card UI
Google was the first big brand to lead in this space. Over the past few years, they have been evolving their SERP results page to prioritize answers over links and focusing on their material design based card UI for native mobile behaviors. The advantage of Card UI is two-fold: cards are modular and can fit any screen, and they chunk content into a single piece of information. Mainly, I like to think of the rise of Card UI as the perfect form factor for the aggregation of content (See Ben Thompson Aggregation Theory).
Websites are the perfect form factor for how to present content on a desktop. Users are stationary with zero to a few additional situational factors influencing his or her behavior.
Mobile, on the other hand, is more random and spontaneous. The ability to consume content on the go makes the behavior more susceptible to an infinite amount of situational influences. Users are more likely to need quick answers to problems or contextual stimuli they didn’t foresee earlier.
I see websites as the equivalent of distributors, where the homepage and main pages control which content gets shown, when and how to the end user. Pages force users to search or scan a page for the right content. Card UI (and machine learning) unshackles content, packaging it into answers and bringing it directly to the consumer where he or she is in the moment of intention or need.
b. Conversational Design
There is a spectrum of intentions. At one end you have users that just want to explore and discover new things. At the other end are the users who have a specific goal in mind, seeking specific answers and knowing what actions to take to achieve their goal.
Visual design is excellent for the exploratory mind, while conversational design is meant to answer the goal orientated mindset. When designing for answers, we want to make sure to have a conversational layer to solve the needs of the goal orientated users. The only role for screens is to show visual feedback, whether that’s data or some error feedback.
When we put these elements together (categorization, card UI and conversation), we can see a beginning formula on how to design for answers.
Putting it together:
1. You need to understand what categories matter most to users and translate that into mental representations.
2. Map that category to a conversational entity, whether that’s an skill or an action
3. And support that conversational entity with card UI to show visual feedback.
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