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Why John Jantsch’s book,” Thought Leadership is the New PR,” is available at Duct Tape Marketing.

Talk to the full season: Overview On this season of the Duct Tape Marketing Podcast, John Jantsch interviews Amy Rosenberg—seasoned PR planner, company leader, and author of” A Practical Guide to Public Relations for Businesses, Nonprofits, and PR Leaders”. Amy dissects the current PR surroundings and explains why online PR is crucial for Google and AI.

Why John Jantsch’s book,” Thought Leadership is the New PR,” is available at Duct Tape Marketing.

Spoilers for winter 3 show 10 were included in this Star Trek: Weird New Worlds review. Star Trek: Odd New Worlds isn’t a present that frequently explores the darker side of story, despite the fact that it centers on a character whose existence we are certain will close in agony and paralysis. Yes, there are shows occasionally …]]…

Den of Geek‘s show 10 review of Star Trek: Odd New Worlds, New Life, and New Civilizations first appeared.

Although it’s popular these days to condemn Dwayne” The Rock” Johnson’s acting choices, there was a time not long ago when the wrestler-turned-actor’s natural charisma attracted slightly different attentions. Although he was never acknowledged as a potential nominee for an award, he was also praised on SNL as “franchise viagra,” and if critics did not, he was undoubtedly respected for his infinite charisma and a nature thus beautiful that, even when he’s not grinning, his facial muscles carry the ghost of one. &#8221,

That is what makes Benny Safdie’s latest trailer for The Smashing Machine‘s latest trailer seem strange and haunted.

In the fresh trailer, Johnson’s Mark Kerr states,” For me, a moment without pain is like a day without sunshine.” A day without pain or sunshine feels like anathema to Johnson &#8217, the onscreen persona of the past 20 or so years, which may be why the position is already receiving rave reviews from Venice, where the movie also received the Silver Lion for Safdie&#8217, s way. It seems that the moon has apparently set in that laugh. It has been replaced by a piece of metal unlike anything since Pain &amp, Gain. &nbsp,

In the 2002 HBO video of the same name, Johnson stars as Kerr, a two-time UFC Heavyweight champion and past fighter. Kerr appears to be a favorable subject matter for Safdie, a mixed martial artist who admitted to dealing with substance misuse and specialized losses. Good Time and Uncut Gems, two of the movies he co-directed with his brother Josh, were unflaggingly dark in their dealings with the sports industry, and together they deliver a picture that is likely to be more complex than the standard sports movie trailer Crushing System offers.

Johnson undoubtedly exhibits a part in this sparkle clip, which we haven’t seen from him in over a century. The strong and eccentric character actor in this scene could be the protagonist of the more intriguing Southland Tales and take John Travolta’s scenes from popular television shows like Be Cool and Be Cool. In Pain & Gain, Michael Bay would paint his obscene painting of American ambition and haughtiness using clay that he had already given himself. &nbsp,

Johnson has shown to have deep pockets of personality and a more confusing reserve of skill that has only been often tapped since the beginning of his transition from wrestling to film acting. Yet in humorous but ferocious smut like The Rundown, Arnold Schwarzenegger seemed to identify him as the heir visible. &nbsp,

Johnson has chosen not to fully exploit that ability over the past 12 years, but it was there in the first place in Jumanji‘s lyric on Big and could have been when acting against Emily Blunt. In fact, the pair collaborated on Jungle Cruise in 2021, a formulaic theater production that requested that Johnson and Blunt revamp Bogie and Hepburn, but in reality amounted to celebrated cast members on a theme garden ride. &nbsp,

However, the piece in this new Smashing System video suggest substantial chemistry when Blunt’s Dawn tenderly treats a maimed boyfriend with compassion but even a controlled weariness. Johnson and Blunt both make appearances outside of their particular milieus in dark drama or harsh gyms. Given that we’ve seen Blunt switch roles and genres before, including Benny Safdie in Christopher Nolan’s Oppenheimer, a film with a completely different cast, Blunt’s authentic, painful sparks with Johnson is something different. With any luck, it will strike the hexagon with as much force as Kerr.

Smashing System is available on A24 on October 3rd.

The Smashing System Video featured Dwayne Johnson and Emily Blunt like never before, according to Den of Geek.

Feedback, in whichever form it takes, and whatever it may be called, is one of the most effective soft skills that we have at our disposal to collaboratively get our designs to a better place while growing our own skills and perspectives.

Feedback is also one of the most underestimated tools, and often by assuming that we’re already good at it, we settle, forgetting that it’s a skill that can be trained, grown, and improved. Poor feedback can create confusion in projects, bring down morale, and affect trust and team collaboration over the long term. Quality feedback can be a transformative force. 

Practicing our skills is surely a good way to improve, but the learning gets even faster when it’s paired with a good foundation that channels and focuses the practice. What are some foundational aspects of giving good feedback? And how can feedback be adjusted for remote and distributed work environments? 

On the web, we can identify a long tradition of asynchronous feedback: from the early days of open source, code was shared and discussed on mailing lists. Today, developers engage on pull requests, designers comment in their favorite design tools, project managers and scrum masters exchange ideas on tickets, and so on.

Design critique is often the name used for a type of feedback that’s provided to make our work better, collaboratively. So it shares a lot of the principles with feedback in general, but it also has some differences.

The content

The foundation of every good critique is the feedback’s content, so that’s where we need to start. There are many models that you can use to shape your content. The one that I personally like best—because it’s clear and actionable—is this one from Lara Hogan.

While this equation is generally used to give feedback to people, it also fits really well in a design critique because it ultimately answers some of the core questions that we work on: What? Where? Why? How? Imagine that you’re giving some feedback about some design work that spans multiple screens, like an onboarding flow: there are some pages shown, a flow blueprint, and an outline of the decisions made. You spot something that could be improved. If you keep the three elements of the equation in mind, you’ll have a mental model that can help you be more precise and effective.

Here is a comment that could be given as a part of some feedback, and it might look reasonable at a first glance: it seems to superficially fulfill the elements in the equation. But does it?

Not sure about the buttons’ styles and hierarchy—it feels off. Can you change them?

Observation for design feedback doesn’t just mean pointing out which part of the interface your feedback refers to, but it also refers to offering a perspective that’s as specific as possible. Are you providing the user’s perspective? Your expert perspective? A business perspective? The project manager’s perspective? A first-time user’s perspective?

When I see these two buttons, I expect one to go forward and one to go back.

Impact is about the why. Just pointing out a UI element might sometimes be enough if the issue may be obvious, but more often than not, you should add an explanation of what you’re pointing out.

When I see these two buttons, I expect one to go forward and one to go back. But this is the only screen where this happens, as before we just used a single button and an “×” to close. This seems to be breaking the consistency in the flow.

The question approach is meant to provide open guidance by eliciting the critical thinking in the designer receiving the feedback. Notably, in Lara’s equation she provides a second approach: request, which instead provides guidance toward a specific solution. While that’s a viable option for feedback in general, for design critiques, in my experience, defaulting to the question approach usually reaches the best solutions because designers are generally more comfortable in being given an open space to explore.

The difference between the two can be exemplified with, for the question approach:

When I see these two buttons, I expect one to go forward and one to go back. But this is the only screen where this happens, as before we just used a single button and an “×” to close. This seems to be breaking the consistency in the flow. Would it make sense to unify them?

Or, for the request approach:

When I see these two buttons, I expect one to go forward and one to go back. But this is the only screen where this happens, as before we just used a single button and an “×” to close. This seems to be breaking the consistency in the flow. Let’s make sure that all screens have the same pair of forward and back buttons.

At this point in some situations, it might be useful to integrate with an extra why: why you consider the given suggestion to be better.

When I see these two buttons, I expect one to go forward and one to go back. But this is the only screen where this happens, as before we just used a single button and an “×” to close. This seems to be breaking the consistency in the flow. Let’s make sure that all screens have the same two forward and back buttons so that users don’t get confused.

Choosing the question approach or the request approach can also at times be a matter of personal preference. A while ago, I was putting a lot of effort into improving my feedback: I did rounds of anonymous feedback, and I reviewed feedback with other people. After a few rounds of this work and a year later, I got a positive response: my feedback came across as effective and grounded. Until I changed teams. To my shock, my next round of feedback from one specific person wasn’t that great. The reason is that I had previously tried not to be prescriptive in my advice—because the people who I was previously working with preferred the open-ended question format over the request style of suggestions. But now in this other team, there was one person who instead preferred specific guidance. So I adapted my feedback for them to include requests.

One comment that I heard come up a few times is that this kind of feedback is quite long, and it doesn’t seem very efficient. No… but also yes. Let’s explore both sides.

No, this style of feedback is actually efficient because the length here is a byproduct of clarity, and spending time giving this kind of feedback can provide exactly enough information for a good fix. Also if we zoom out, it can reduce future back-and-forth conversations and misunderstandings, improving the overall efficiency and effectiveness of collaboration beyond the single comment. Imagine that in the example above the feedback were instead just, “Let’s make sure that all screens have the same two forward and back buttons.” The designer receiving this feedback wouldn’t have much to go by, so they might just apply the change. In later iterations, the interface might change or they might introduce new features—and maybe that change might not make sense anymore. Without the why, the designer might imagine that the change is about consistency… but what if it wasn’t? So there could now be an underlying concern that changing the buttons would be perceived as a regression.

Yes, this style of feedback is not always efficient because the points in some comments don’t always need to be exhaustive, sometimes because certain changes may be obvious (“The font used doesn’t follow our guidelines”) and sometimes because the team may have a lot of internal knowledge such that some of the whys may be implied.

So the equation above isn’t meant to suggest a strict template for feedback but a mnemonic to reflect and improve the practice. Even after years of active work on my critiques, I still from time to time go back to this formula and reflect on whether what I just wrote is effective.

The tone

Well-grounded content is the foundation of feedback, but that’s not really enough. The soft skills of the person who’s providing the critique can multiply the likelihood that the feedback will be well received and understood. Tone alone can make the difference between content that’s rejected or welcomed, and it’s been demonstrated that only positive feedback creates sustained change in people.

Since our goal is to be understood and to have a positive working environment, tone is essential to work on. Over the years, I’ve tried to summarize the required soft skills in a formula that mirrors the one for content: the receptivity equation.

Respectful feedback comes across as grounded, solid, and constructive. It’s the kind of feedback that, whether it’s positive or negative, is perceived as useful and fair.

Timing refers to when the feedback happens. To-the-point feedback doesn’t have much hope of being well received if it’s given at the wrong time. Questioning the entire high-level information architecture of a new feature when it’s about to ship might still be relevant if that questioning highlights a major blocker that nobody saw, but it’s way more likely that those concerns will have to wait for a later rework. So in general, attune your feedback to the stage of the project. Early iteration? Late iteration? Polishing work in progress? These all have different needs. The right timing will make it more likely that your feedback will be well received.

Attitude is the equivalent of intent, and in the context of person-to-person feedback, it can be referred to as radical candor. That means checking before we write to see whether what we have in mind will truly help the person and make the project better overall. This might be a hard reflection at times because maybe we don’t want to admit that we don’t really appreciate that person. Hopefully that’s not the case, but that can happen, and that’s okay. Acknowledging and owning that can help you make up for that: how would I write if I really cared about them? How can I avoid being passive aggressive? How can I be more constructive?

Form is relevant especially in a diverse and cross-cultural work environments because having great content, perfect timing, and the right attitude might not come across if the way that we write creates misunderstandings. There might be many reasons for this: sometimes certain words might trigger specific reactions; sometimes nonnative speakers might not understand all the nuances of some sentences; sometimes our brains might just be different and we might perceive the world differently—neurodiversity must be taken into consideration. Whatever the reason, it’s important to review not just what we write but how.

A few years back, I was asking for some feedback on how I give feedback. I received some good advice but also a comment that surprised me. They pointed out that when I wrote “Oh, […],” I made them feel stupid. That wasn’t my intent! I felt really bad, and I just realized that I provided feedback to them for months, and every time I might have made them feel stupid. I was horrified… but also thankful. I made a quick fix: I added “oh” in my list of replaced words (your choice between: macOS’s text replacement, aText, TextExpander, or others) so that when I typed “oh,” it was instantly deleted. 

Something to highlight because it’s quite frequent—especially in teams that have a strong group spirit—is that people tend to beat around the bush. It’s important to remember here that a positive attitude doesn’t mean going light on the feedback—it just means that even when you provide hard, difficult, or challenging feedback, you do so in a way that’s respectful and constructive. The nicest thing that you can do for someone is to help them grow.

We have a great advantage in giving feedback in written form: it can be reviewed by another person who isn’t directly involved, which can help to reduce or remove any bias that might be there. I found that the best, most insightful moments for me have happened when I’ve shared a comment and I’ve asked someone who I highly trusted, “How does this sound?,” “How can I do it better,” and even “How would you have written it?”—and I’ve learned a lot by seeing the two versions side by side.

The format

Asynchronous feedback also has a major inherent advantage: we can take more time to refine what we’ve written to make sure that it fulfills two main goals: the clarity of communication and the actionability of the suggestions.

Let’s imagine that someone shared a design iteration for a project. You are reviewing it and leaving a comment. There are many ways to do this, and of course context matters, but let’s try to think about some elements that may be useful to consider.

In terms of clarity, start by grounding the critique that you’re about to give by providing context. Specifically, this means describing where you’re coming from: do you have a deep knowledge of the project, or is this the first time that you’re seeing it? Are you coming from a high-level perspective, or are you figuring out the details? Are there regressions? Which user’s perspective are you taking when providing your feedback? Is the design iteration at a point where it would be okay to ship this, or are there major things that need to be addressed first?

Providing context is helpful even if you’re sharing feedback within a team that already has some information on the project. And context is absolutely essential when giving cross-team feedback. If I were to review a design that might be indirectly related to my work, and if I had no knowledge about how the project arrived at that point, I would say so, highlighting my take as external.

We often focus on the negatives, trying to outline all the things that could be done better. That’s of course important, but it’s just as important—if not more—to focus on the positives, especially if you saw progress from the previous iteration. This might seem superfluous, but it’s important to keep in mind that design is a discipline where there are hundreds of possible solutions for every problem. So pointing out that the design solution that was chosen is good and explaining why it’s good has two major benefits: it confirms that the approach taken was solid, and it helps to ground your negative feedback. In the longer term, sharing positive feedback can help prevent regressions on things that are going well because those things will have been highlighted as important. As a bonus, positive feedback can also help reduce impostor syndrome.

There’s one powerful approach that combines both context and a focus on the positives: frame how the design is better than the status quo (compared to a previous iteration, competitors, or benchmarks) and why, and then on that foundation, you can add what could be improved. This is powerful because there’s a big difference between a critique that’s for a design that’s already in good shape and a critique that’s for a design that isn’t quite there yet.

Another way that you can improve your feedback is to depersonalize the feedback: the comments should always be about the work, never about the person who made it. It’s “This button isn’t well aligned” versus “You haven’t aligned this button well.” This is very easy to change in your writing by reviewing it just before sending.

In terms of actionability, one of the best approaches to help the designer who’s reading through your feedback is to split it into bullet points or paragraphs, which are easier to review and analyze one by one. For longer pieces of feedback, you might also consider splitting it into sections or even across multiple comments. Of course, adding screenshots or signifying markers of the specific part of the interface you’re referring to can also be especially useful.

One approach that I’ve personally used effectively in some contexts is to enhance the bullet points with four markers using emojis. So a red square 🟥 means that it’s something that I consider blocking; a yellow diamond 🔶 is something that I can be convinced otherwise, but it seems to me that it should be changed; and a green circle 🟢 is a detailed, positive confirmation. I also use a blue spiral 🌀 for either something that I’m not sure about, an exploration, an open alternative, or just a note. But I’d use this approach only on teams where I’ve already established a good level of trust because if it happens that I have to deliver a lot of red squares, the impact could be quite demoralizing, and I’d reframe how I’d communicate that a bit.

Let’s see how this would work by reusing the example that we used earlier as the first bullet point in this list:

• 🔶 Navigation—When I see these two buttons, I expect one to go forward and one to go back. But this is the only screen where this happens, as before we just used a single button and an “×” to close. This seems to be breaking the consistency in the flow. Let’s make sure that all screens have the same two forward and back buttons so that users don’t get confused.
• 🟢 Overall—I think the page is solid, and this is good enough to be our release candidate for a version 1.0.
• 🟢 Metrics—Good improvement in the buttons on the metrics area; the improved contrast and new focus style make them more accessible.
•  🟥  Button Style—Using the green accent in this context creates the impression that it’s a positive action because green is usually perceived as a confirmation color. Do we need to explore a different color?
• 🔶Tiles—Given the number of items on the page, and the overall page hierarchy, it seems to me that the tiles shouldn’t be using the Subtitle 1 style but the Subtitle 2 style. This will keep the visual hierarchy more consistent.
• 🌀 Background—Using a light texture works well, but I wonder whether it adds too much noise in this kind of page. What is the thinking in using that?

What about giving feedback directly in Figma or another design tool that allows in-place feedback? In general, I find these difficult to use because they hide discussions and they’re harder to track, but in the right context, they can be very effective. Just make sure that each of the comments is separate so that it’s easier to match each discussion to a single task, similar to the idea of splitting mentioned above.

One final note: say the obvious. Sometimes we might feel that something is obviously good or obviously wrong, and so we don’t say it. Or sometimes we might have a doubt that we don’t express because the question might sound stupid. Say it—that’s okay. You might have to reword it a little bit to make the reader feel more comfortable, but don’t hold it back. Good feedback is transparent, even when it may be obvious.

There’s another advantage of asynchronous feedback: written feedback automatically tracks decisions. Especially in large projects, “Why did we do this?” could be a question that pops up from time to time, and there’s nothing better than open, transparent discussions that can be reviewed at any time. For this reason, I recommend using software that saves these discussions, without hiding them once they are resolved. 

Content, tone, and format. Each one of these subjects provides a useful model, but working to improve eight areas—observation, impact, question, timing, attitude, form, clarity, and actionability—is a lot of work to put in all at once. One effective approach is to take them one by one: first identify the area that you lack the most (either from your perspective or from feedback from others) and start there. Then the second, then the third, and so on. At first you’ll have to put in extra time for every piece of feedback that you give, but after a while, it’ll become second nature, and your impact on the work will multiply.

Thanks to Brie Anne Demkiw and Mike Shelton for reviewing the first draft of this article.

” Any opinion”? is perhaps one of the worst ways to ask for opinions. It’s obscure and unfocused, and it doesn’t give us a sense of what we’re looking for. Getting good opinions starts sooner than we might hope: it starts with the demand.

Starting the process of receiving feedback with a question may seem counterintuitive, but it makes sense if we consider that receiving feedback can be considered a form of pattern research. In the same way that we wouldn’t perform any studies without the correct questions to get the insight that we need, the best way to ask for feedback is also to build strong issues.

Design criticism is not a one-time procedure. Sure, any great comments process continues until the project is finished, but this is especially true for layout because architecture work continues iteration after iteration, from a high level to the finest details. Each stage requires its unique set of questions.

And suddenly, as with any great research, we need to review what we got up, get to the base of its perspectives, and take activity. Problem, generation, and analysis. This look at each of those.

The query

Being available to input is important, but we need to be specific about what we’re looking for. Any comments,” What do you think,” or” I’d love to hear your mind” at the conclusion of a presentation are likely to garner a lot of different ideas, or worse, to make everyone follow the lead of the first speaker. And next… we get frustrated because vague issues like those you turn a high-level moves review into folks rather commenting on the borders of buttons. Which theme may be significant, so it might be difficult to get the team to choose the one you wanted to concentrate on.

But how do we get into this scenario? It’s a combination of various components. One is that we don’t often consider asking as a part of the input method. Another is how healthy it is to keep the question open and assume that everyone else will agree. Another is that in nonprofessional conversations, there’s usually no need to be that exact. In summary, we tend to undervalue the value of the concerns, so we don’t work to make them better.

The work of asking good questions guidelines and focuses the criticism. It also serves as a form of acceptance, outlining your willingness to make comments and the types of responses you want to receive. It puts people in the right emotional state, especially in situations when they weren’t expecting to give opinions.

There isn’t a second best way to ask for opinions. It simply needs to be certain, and sensitivity can take several shapes. The concept of stage than level is a design for design criticism that I’ve found to be particularly helpful in my coaching.

” Stage” refers to each of the actions of the process—in our event, the design process. The type of input changes as the customer research moves forward to the final design. But within a single stage, one might also examine whether some assumptions are correct and whether there’s been a suitable language of the amassed input into updated designs as the task has evolved. The layers of user experience could serve as a starting point for potential questions. What do you want to know: Project objectives? User requirements? Functionality? Content? Interaction design? a system of information architecture UI design? design of navigation Visual design? branding?

Here’re a few example questions that are precise and to the point that refer to different layers:

• Functionality: Is it desirable to automate account creation?
• Interaction design: Take a look through the updated flow and let me know whether you see any steps or error states that I might’ve missed.
• Information architecture: This page contains two competing pieces of information. Is the structure effective in communicating them both?
• User interface design: What do you think about the error counter at the top of the page, which makes sure you see the next error even if it is outside the viewport?
• Navigation design: From research, we identified these second-level navigation items, but once you’re on the page, the list feels too long and hard to navigate. Exist any recommendations for resolving this?
• Visual design: Are the sticky notifications in the bottom-right corner visible enough?

How much of a presentation’s depth would be on the other axis of specificity. For example, we might have introduced a new end-to-end flow, but there was a specific view that you found particularly challenging and you’d like a detailed review of that. This can be especially helpful from one iteration to the next when it’s crucial to highlight the areas that have changed.

There are other things that we can consider when we want to achieve more specific—and more effective—questions.

A quick fix is to get rid of the generic qualifiers from questions like “good”, “well,” “nice,” “bad,” “okay,” and” cool.” For example, asking,” When the block opens and the buttons appear, is this interaction good”? is it possible to look specific, but you can spot the “good” qualifier and make the question” When the block opens and the buttons appear, is it clear what the next action is” look like?

Sometimes we actually do want broad feedback. Although that’s uncommon, it can occur. In that sense, you might still make it explicit that you’re looking for a wide range of opinions, whether at a high level or with details. Or perhaps just say,” At first glance, what do you think”? so that it’s clear that what you’re asking is open ended but focused on someone’s impression after their first five seconds of looking at it.

Sometimes the project is particularly broad, and some areas may have already been thoroughly explored. In these situations, it might be useful to explicitly say that some parts are already locked in and aren’t open to feedback. Although it’s not something I’d recommend in general, I’ve found it helpful in avoiding falling into rabbit holes like those that could lead to further refinement but aren’t what’s important right now.

Asking specific questions can completely change the quality of the feedback that you receive. People with less refined criticism will now be able to provide more actionable feedback, and even expert designers will appreciate the clarity and effectiveness gained from concentrating solely on what’s needed. It can save a lot of time and frustration.

The iteration

Design iterations are probably the most visible part of the design work, and they provide a natural checkpoint for feedback. Many design tools have inline commenting, but many of them only display changes as a single fluid stream in the same file. These types of design tools cause conversations to end after they are resolved, update shared UI components automatically, and require designers to always display the most recent version unless these would-be useful features were manually disabled. The implied goal that these design tools seem to have is to arrive at just one final copy with all discussions closed, probably because they inherited patterns from how written documents are collaboratively edited. That approach to design critiques is probably not the best approach, but some teams might benefit from it even if I don’t want to be too prescriptive.

The asynchronous design-critique approach that I find most effective is to create explicit checkpoints for discussion. I’m going to use the term iteration post for this. It refers to a write-up or presentation of the design iteration followed by a discussion thread of some kind. This can be used on any platform that can accommodate this structure. By the way, when I refer to a “write-up or presentation“, I’m including video recordings or other media too: as long as it’s asynchronous, it works.

There are many benefits to using iteration posts:

• It creates a rhythm in the design work so that the designer can review feedback from each iteration and prepare for the next.
• Decisions are made immediately available for future review, and conversations are also always available.
• It creates a record of how the design changed over time.
• Depending on the tool, it might also make it simpler to collect and act on feedback.

These posts of course don’t mean that no other feedback approach should be used, just that iteration posts could be the primary rhythm for a remote design team to use. And from there, there can develop additional feedback techniques ( such as live critique, pair designing, or inline comments ).

I don’t think there’s a standard format for iteration posts. However, there are a few high-level components that make sense as a baseline:

1. The goal
2. The layout
3. The list of changes
4. The querys

Each project is likely to have a goal, and hopefully it’s something that’s already been summarized in a single sentence somewhere else, such as the client brief, the product manager’s outline, or the project owner’s request. In every iteration post, I would copy and paste this, so I could do it again. The idea is to provide context and to repeat what’s essential to make each iteration post complete so that there’s no need to find information spread across multiple posts. The most recent iteration post will have everything I need if I want to know about the most recent design.

This copy-and-paste part introduces another relevant concept: alignment comes from repetition. Therefore, repeating information in posts helps to ensure that everyone is on the same page.

The design is then the actual series of information-architecture outlines, diagrams, flows, maps, wireframes, screens, visuals, and any other kind of design work that’s been done. In essence, it’s any design work. For the final stages of work, I prefer the term blueprint to emphasize that I’ll be showing full flows instead of individual screens to make it easier to understand the bigger picture.

It might also be helpful to have clear names on the artifacts so that it is easier to refer to them. Write the post in a way that helps people understand the work. It’s not much different from creating a strong live presentation.

For an efficient discussion, you should also include a bullet list of the changes from the previous iteration to let people focus on what’s new, which can be especially useful for larger pieces of work where keeping track, iteration after iteration, could become a challenge.

Finally, as mentioned earlier, a list of the questions must be included in order to help you guide the design critique. Doing this as a numbered list can also help make it easier to refer to each question by its number.

Not every iteration is the same. Earlier iterations don’t need to be as tightly focused—they can be more exploratory and experimental, maybe even breaking some of the design-language guidelines to see what’s possible. Then, later, the iterations begin coming to a decision and improving it until the feature development is complete.

I want to highlight that even if these iteration posts are written and conceived as checkpoints, by no means do they need to be exhaustive. A post might be a draft, just a concept to start a discussion, or it might be a cumulative list of all the features that have been added over the course of each iteration until the full picture is achieved.

Over time, I also started using specific labels for incremental iterations: i1, i2, i3, and so on. Although this may seem like a minor labeling tip, it can be useful in many ways:

• Unique—It’s a clear unique marker. Everyone knows where to go to review things, and it’s simple to say” This was discussed in i4″ with each project.
• Unassuming—It works like versions ( such as v1, v2, and v3 ) but in contrast, versions create the impression of something that’s big, exhaustive, and complete. Exploratory, incomplete, or partial should be the definition of an argument.
• Future proof—It resolves the “final” naming problem that you can run into with versions. No more files with the title “final final complete no-really-its-done” Within each project, the largest number always represents the latest iteration.

The wording release candidate (RC ) could be used to indicate when a design is finished enough to be worked on, even if there are some areas that still need improvement and, in turn, require more iterations, such as” with i8 we reached RC” or “i12 is an RC” to indicate when it is finished.

The review

What typically occurs during a design critique is an open discussion that can be very productive between two people. This approach is particularly effective during live, synchronous feedback. However, when we work asynchronously, using a different approach is more effective: we can adopt a user-research mindset. Written feedback from teammates, stakeholders, or others can be treated as if it were the result of user interviews and surveys, and we can analyze it accordingly.

Asynchronous feedback is particularly effective because of this shift, especially around these friction points:

1. It removes the pressure to reply to everyone.
2. It lessens the annoyance caused by swoop-by comments.
3. It lessens our personal stake.

The first friction point is having to feel pressured to respond to each and every comment. Sometimes we write the iteration post, and we get replies from our team. It’s just a few of them, it’s simple, and there isn’t much to worry about. But other times, some solutions might require more in-depth discussions, and the amount of replies can quickly increase, which can create a tension between trying to be a good team player by replying to everyone and doing the next design iteration. This might be especially true if the respondent is a stakeholder or a person who is directly involved in the project and whom we feel we need to speak with. We need to accept that this pressure is absolutely normal, and it’s human nature to try to accommodate people who we care about. When we treat a design critique more like user research, we realize that we don’t need to respond to every comment, and there are alternatives: In asynchronous spaces, responding to all comments can be effective.

One is to let the next iteration speak for itself. The response is received when the design changes and a follow-up iteration is made. You might tag all the people who were involved in the previous discussion, but even that’s a choice, not a requirement.
• Another tactic is to formally acknowledge each comment in a brief response, such as” Understood. Thank you”,” Good points— I’ll review”, or” Thanks. These will be included in the upcoming iteration. In some cases, this could also be just a single top-level comment along the lines of” Thanks for all the feedback everyone—the next iteration is coming soon”!
• Another option is to quickly summarize the comments before moving on. Depending on your workflow, this can be particularly useful as it can provide a simplified checklist that you can then use for the next iteration.

The swoop-by comment, which is the kind of feedback that comes from a member of a team or non-project who might not be aware of the context, restrictions, decisions, or requirements, or of the discussions from earlier iterations, is the second friction point. On their side, there’s something that one can hope that they might learn: they could start to acknowledge that they’re doing this and they could be more conscious in outlining where they’re coming from. Swoop-by comments frequently prompt the simple thought,” We’ve already discussed this,” and it can be frustrating to have to keep coming back and forth.

Let’s begin by acknowledging again that there’s no need to reply to every comment. However, a brief response with a link to the previous discussion for additional information is typically sufficient if responding to a previously litigated point might be helpful. Remember, alignment comes from repetition, so it’s okay to repeat things sometimes!

Swoop-by commenting has two benefits: first, they might point out something that isn’t clear, and second, they might serve as a reference point for someone who is first viewing the design. Sure, you’ll still be frustrated, but that might at least help in dealing with it.

The personal stake we might have in the design could be the third friction point, which might cause us to feel defensive if the review turned into a discussion. Treating feedback as user research helps us create a healthy distance between the people giving us feedback and our ego ( because yes, even if we don’t want to admit it, it’s there ). In the end, presenting everything in aggregated form helps us to prioritize our work more.

Always remember that while you need to listen to stakeholders, project owners, and specific advice, you don’t have to accept every piece of feedback. You must examine it and come up with a rationale for your choice, but sometimes “no” is the best choice.

As the designer leading the project, you’re in charge of that decision. In the end, everyone has their area of specialization, and the designer has the most background and knowledge to make the best choice. And by listening to the feedback that you’ve received, you’re making sure that it’s also the best and most balanced decision.

Thanks to Mike Shelton and Brie Anne Demkiw for their contributions to the initial draft of this article.

I’m never sure when I first heard this offer, but it’s something that has stayed with me over the centuries. How do you generate solutions for scenarios you can’t think? Or pattern items that work on devices yet to be invented?

Flash, Photoshop, and flexible pattern

When I first started designing sites, my go-to technology was Photoshop. I created a 960px paint and set about creating a design that I would eventually lose information in. The growth phase was about attaining pixel-perfect precision using set widths, fixed levels, and absolute setting.

Ethan Marcotte’s speak at An Event Off and later content” Responsive Web Design” in A List Off in 2010 changed all this. I was sold on responsive pattern as soon as I heard about it, but I was even terrified. The pixel-perfect models full of special figures that I had formerly prided myself on producing were no longer good enough.

The dread wasn’t helped by my first experience with flexible design. My second project was to get an active fixed-width website and make it reactive. What I learned the hard way was that you can’t really put sensitivity at the end of a job. To make smooth design, you need to prepare throughout the style stage.

A new way to style

Developing flexible or smooth sites has always been about removing limitations, producing material that can be viewed on any system. It relies on the use of percentage-based design, which I immediately achieved with local CSS and power groups:

.column-span-6 { width: 49%; float: left; margin-right: 0.5%; margin-left: 0.5%;}.column-span-4 { width: 32%; float: left; margin-right: 0.5%; margin-left: 0.5%;}.column-span-3 { width: 24%; float: left; margin-right: 0.5%; margin-left: 0.5%;}

Then with Sass so I could take advantage of @includes to re-use repeated slabs of script and walk up to more semantic premium:

.logo { @include colSpan(6);}.search { @include colSpan(3);}.social-share { @include colSpan(3);}

Media concerns

The next ingredient for reactive design is press queries. Without them, glad may shrink to fit the available area regardless of whether that information remained readable ( The exact same difficulty occurred with the introduction of a mobile-first view ).

Media concerns prevented this by allowing us to add breakpoints where the design could adapt. Like most people, I started out with three breakpoints: one for desktop, one for tablets, and one for mobile. Over the years, I added more and more for phablets, wide screens, and so on. 

For years, I happily worked this way and improved both my design and front-end skills in the process. The only problem I encountered was making changes to content, since with our Sass grid system in place, there was no way for the site owners to add content without amending the markup—something a small business owner might struggle with. This is because each row in the grid was defined using a div as a container. Adding content meant creating new row markup, which requires a level of HTML knowledge.

String premium was a mainstay of early flexible design, present in all the frequently used systems like Bootstrap and Skeleton.

 
1 of 7
 
2 of 7
 
3 of 7
 
4 of 7
 
5 of 7
 
6 of 7
 
7 of 7

Another difficulty arose as I moved from a design firm building websites for smaller- to medium-sized companies, to larger in-house teams where I worked across a collection of related sites. In those responsibilities I started to work many more with reusable components.

Our rely on multimedia queries resulted in parts that were tied to frequent window sizes. If the goal of part libraries is modify, then this is a real problem because you can just use these components if the devices you’re designing for correspond to the viewport sizes used in the pattern library—in the process never really hitting that “devices that don’t already occur” goal.

Then there’s the problem of space. Media concerns allow components to adapt based on the viewport size, but what if I put a component into a sidebar, like in the figure below?

Container queries: our savior or a false dawn?

Container queries have long been touted as an improvement upon media queries, but at the time of writing are unsupported in most browsers. There are JavaScript workarounds, but they can create dependency and compatibility issues. The basic theory underlying container queries is that elements should change based on the size of their parent container and not the viewport width, as seen in the following illustrations.

One of the biggest arguments in favor of container queries is that they help us create components or design patterns that are truly reusable because they can be picked up and placed anywhere in a layout. This is an important step in moving toward a form of component-based design that works at any size on any device.

In other words, responsive components to replace responsive layouts.

Container queries will help us move from designing pages that respond to the browser or device size to designing components that can be placed in a sidebar or in the main content, and respond accordingly.

My concern is that we are still using layout to determine when a design needs to adapt. This approach will always be restrictive, as we will still need pre-defined breakpoints. For this reason, my main question with container queries is, How would we decide when to change the CSS used by a component?

A component library removed from context and real content is probably not the best place for that decision.

As the diagrams below illustrate, we can use container queries to create designs for specific container widths, but what if I want to change the design based on the image size or ratio?

In this example, the dimensions of the container are not what should dictate the design, rather, the image is.

It’s hard to say for sure whether container queries will be a success story until we have solid cross-browser support for them. Responsive component libraries would definitely evolve how we design and would improve the possibilities for reuse and design at scale. But maybe we will always need to adjust these components to suit our content.

CSS is changing

Whilst the container query debate rumbles on, there have been numerous advances in CSS that change the way we think about design. The days of fixed-width elements measured in pixels and floated div elements used to cobble layouts together are long gone, consigned to history along with table layouts. Flexbox and CSS Grid have revolutionized layouts for the web. We can now create elements that wrap onto new rows when they run out of space, not when the device changes.

.wrapper { display: grid; grid-template-columns: repeat(auto-fit, 450px); gap: 10px;}

The repeat() function paired with auto-fit or auto-fill allows us to specify how much space each column should use while leaving it up to the browser to decide when to spill the columns onto a new line. Similar things can be achieved with Flexbox, as elements can wrap over multiple rows and “flex” to fill available space. 

.wrapper { display: flex; flex-wrap: wrap; justify-content: space-between;}.child { flex-basis: 32%; margin-bottom: 20px;}

The biggest benefit of all this is you don’t need to wrap elements in container rows. Without rows, content isn’t tied to page markup in quite the same way, allowing for removals or additions of content without additional development.

This is a big step forward when it comes to creating designs that allow for evolving content, but the real game changer for flexible designs is CSS Subgrid.

Remember the days of crafting perfectly aligned interfaces, only for the customer to add an unbelievably long header almost as soon as they’re given CMS access, like the illustration below?

Subgrid allows elements to respond to adjustments in their own content and in the content of sibling elements, helping us create designs more resilient to change.

.wrapper { display: grid; grid-template-columns: repeat(auto-fit, minmax(150px, 1fr)); grid-template-rows: auto 1fr auto; gap: 10px;}.sub-grid { display: grid; grid-row: span 3; grid-template-rows: subgrid; /* sets rows to parent grid */}

CSS Grid allows us to separate layout and content, thereby enabling flexible designs. Meanwhile, Subgrid allows us to create designs that can adapt in order to suit morphing content. Subgrid at the time of writing is only supported in Firefox but the above code can be implemented behind an @supports feature query.

Intrinsic layouts

I’d be remiss not to mention intrinsic layouts, the term created by Jen Simmons to describe a mixture of new and old CSS features used to create layouts that respond to available space.

Responsive layouts have flexible columns using percentages. Intrinsic layouts, on the other hand, use the fr unit to create flexible columns that won’t ever shrink so much that they render the content illegible.

frunits is a way to say I want you to distribute the extra space in this way, but… don’t ever make it smaller than the content that’s inside of it.

—Jen Simmons,” Designing Intrinsic Layouts”

Intrinsic layouts can also utilize a mixture of fixed and flexible units, allowing the content to dictate the space it takes up.

What makes intrinsic design stand out is that it not only creates designs that can withstand future devices but also helps scale design without losing flexibility. Components and patterns can be lifted and reused without the prerequisite of having the same breakpoints or the same amount of content as in the previous implementation.

We can now create designs that adapt to the space they have, the content within them, and the content around them. With an intrinsic approach, we can construct responsive components without depending on container queries.

Another 2010 moment?

This intrinsic approach should in my view be every bit as groundbreaking as responsive web design was ten years ago. For me, it’s another “everything changed” moment.

But it doesn’t seem to be moving quite as fast, I haven’t yet had that same career-changing moment I had with responsive design, despite the widely shared and brilliant talk that brought it to my attention.

One reason for that could be that I now work in a large organization, which is quite different from the design agency role I had in 2010. In my agency days, every new project was a clean slate, a chance to try something new. Nowadays, projects use existing tools and frameworks and are often improvements to existing websites with an existing codebase.

Another could be that I feel more prepared for change now. In 2010 I was new to design in general, the shift was frightening and required a lot of learning. Also, an intrinsic approach isn’t exactly all-new, it’s about using existing skills and existing CSS knowledge in a different way.

You can’t framework your way out of a content problem

Another reason for the slightly slower adoption of intrinsic design could be the lack of quick-fix framework solutions available to kick-start the change.

Responsive grid systems were all over the place ten years ago. With a framework like Bootstrap or Skeleton, you had a responsive design template at your fingertips.

Intrinsic design and frameworks do not go hand in hand quite so well because the benefit of having a selection of units is a hindrance when it comes to creating layout templates. The beauty of intrinsic design is combining different units and experimenting with techniques to get the best for your content.

And then there are design tools. We probably all, at some point in our careers, used Photoshop templates for desktop, tablet, and mobile devices to drop designs in and show how the site would look at all three stages.

How do you do that now, with each component responding to content and layouts flexing as and when they need to? This type of design must happen in the browser, which personally I’m a big fan of.

The debate about “whether designers should code” is another that has rumbled on for years. When designing a digital product, we should, at the very least, design for a best- and worst-case scenario when it comes to content. To do this in a graphics-based software package is far from ideal. In code, we can add longer sentences, more radio buttons, and extra tabs, and watch in real time as the design adapts. Does it still work? Is the design too reliant on the current content?

Personally, I look forward to the day intrinsic design is the standard for design, when a design component can be truly flexible and adapt to both its space and content with no reliance on device or container dimensions.

Content first

Content is not constant. After all, to design for the unknown or unexpected we need to account for content changes like our earlier Subgrid card example that allowed the cards to respond to adjustments to their own content and the content of sibling elements.

Thankfully, there’s more to CSS than layout, and plenty of properties and values can help us put content first. Subgrid and pseudo-elements like ::first-line and ::first-letter help to separate design from markup so we can create designs that allow for changes.

Instead of old markup hacks like this —

 First line of text with different styling...

—we can target content based on where it appears.

.element::first-line { font-size: 1.4em;}.element::first-letter { color: red;}

Much bigger additions to CSS include logical properties, which change the way we construct designs using logical dimensions (start and end) instead of physical ones (left and right), something CSS Grid also does with functions like min(), max(), and clamp().

This flexibility allows for directional changes according to content, a common requirement when we need to present content in multiple languages. In the past, this was often achieved with Sass mixins but was often limited to switching from left-to-right to right-to-left orientation.

In the Sass version, directional variables need to be set.

$direction: rtl;$opposite-direction: ltr;$start-direction: right;$end-direction: left;

These variables can be used as values—

body { direction: $direction; text-align: $start-direction;}

—or as properties.

margin-#{$end-direction}: 10px;padding-#{$start-direction}: 10px;

However, now we have native logical properties, removing the reliance on both Sass ( or a similar tool ) and pre-planning that necessitated using variables throughout a codebase. These properties also start to break apart the tight coupling between a design and strict physical dimensions, creating more flexibility for changes in language and in direction.

margin-block-end: 10px;padding-block-start: 10px;

There are also native start and end values for properties like text-align, which means we can replace text-align: right with text-align: start.

Like the earlier examples, these properties help to build out designs that aren’t constrained to one language, the design will reflect the content’s needs.

Fixed and fluid

We briefly covered the power of combining fixed widths with fluid widths with intrinsic layouts. The min() and max() functions are a similar concept, allowing you to specify a fixed value with a flexible alternative. 

For min() this means setting a fluid minimum value and a maximum fixed value.

.element { width: min(50%, 300px);}

The element in the figure above will be 50 % of its container as long as the element’s width doesn’t exceed 300px.

For max() we can set a flexible max value and a minimum fixed value.

.element { width: max(50%, 300px);}

Now the element will be 50 % of its container as long as the element’s width is at least 300px. This means we can set limits but allow content to react to the available space.

The clamp() function builds on this by allowing us to set a preferred value with a third parameter. Now we can allow the element to shrink or grow if it needs to without getting to a point where it becomes unusable.

.element { width: clamp(300px, 50%, 600px);}

This time, the element’s width will be 50 % ( the preferred value ) of its container but never less than 300px and never more than 600px.

With these techniques, we have a content-first approach to responsive design. We can separate content from markup, meaning the changes users make will not affect the design. We can start to future-proof designs by planning for unexpected changes in language or direction. And we can increase flexibility by setting desired dimensions alongside flexible alternatives, allowing for more or less content to be displayed correctly.

Situation first

Thanks to what we’ve discussed so far, we can cover device flexibility by changing our approach, designing around content and space instead of catering to devices. But what about that last bit of Jeffrey Zeldman’s quote,”… situations you haven’t imagined”?

It’s a very different thing to design for someone seated at a desktop computer as opposed to someone using a mobile phone and moving through a crowded street in glaring sunshine. Situations and environments are hard to plan for or predict because they change as people react to their own unique challenges and tasks.

This is why choice is so important. One size never fits all, so we need to design for multiple scenarios to create equal experiences for all our users.

Thankfully, there is a lot we can do to provide choice.

Responsible design

” There are parts of the world where mobile data is prohibitively expensive, and where there is little or no broadband infrastructure”.

” I Used the Web for a Day on a 50 MB Budget“

Chris Ashton

One of the biggest assumptions we make is that people interacting with our designs have a good wifi connection and a wide screen monitor. But in the real world, our users may be commuters traveling on trains or other forms of transport using smaller mobile devices that can experience drops in connectivity. There is nothing more frustrating than a web page that won’t load, but there are ways we can help users use less data or deal with sporadic connectivity.

The srcset attribute allows the browser to decide which image to serve. This means we can create smaller ‘cropped’ images to display on mobile devices in turn using less bandwidth and less data.

The preload attribute can also help us to think about how and when media is downloaded. It can be used to tell a browser about any critical assets that need to be downloaded with high priority, improving perceived performance and the user experience. 

There’s also native lazy loading, which indicates assets that should only be downloaded when they are needed.

With srcset, preload, and lazy loading, we can start to tailor a user’s experience based on the situation they find themselves in. What none of this does, however, is allow the user themselves to decide what they want downloaded, as the decision is usually the browser’s to make. 

So how can we put users in control?

The return of media queries

Media concerns have always been about much more than device sizes. They allow content to adapt to different situations, with screen size being just one of them.

We’ve long been able to check for media types like print and speech and features such as hover, resolution, and color. These checks allow us to provide options that suit more than one scenario, it’s less about one-size-fits-all and more about serving adaptable content.

As of this writing, the Media Queries Level 5 spec is still under development. It introduces some really exciting queries that in the future will help us design for multiple other unexpected situations.

For example, there’s a light-level feature that allows you to modify styles if a user is in sunlight or darkness. Paired with custom properties, these features allow us to quickly create designs or themes for specific environments.

@media (light-level: normal) { --background-color: #fff; --text-color: #0b0c0c; }@media (light-level: dim) { --background-color: #efd226; --text-color: #0b0c0c;}

Another key feature of the Level 5 spec is personalization. Instead of creating designs that are the same for everyone, users can choose what works for them. This is achieved by using features like prefers-reduced-data, prefers-color-scheme, and prefers-reduced-motion, the latter two of which already enjoy broad browser support. These features tap into preferences set via the operating system or browser so people don’t have to spend time making each site they visit more usable. 

Media concerns like this go beyond choices made by a browser to grant more control to the user.

Expect the unexpected

In the end, the one thing we should always expect is for things to change. Devices in particular change faster than we can keep up, with foldable screens already on the market.

We can’t design the same way we have for this ever-changing landscape, but we can design for content. By putting content first and allowing that content to adapt to whatever space surrounds it, we can create more robust, flexible designs that increase the longevity of our products.

A lot of the CSS discussed here is about moving away from layouts and putting content at the heart of design. From responsive components to fixed and fluid units, there is so much more we can do to take a more intrinsic approach. Even better, we can test these techniques during the design phase by designing in-browser and watching how our designs adapt in real-time.

When it comes to unexpected situations, we need to make sure our products are usable when people need them, whenever and wherever that might be. We can move closer to achieving this by involving users in our design decisions, by creating choice via browsers, and by giving control to our users with user-preference-based media queries.

Good design for the unexpected should allow for change, provide choice, and give control to those we serve: our users themselves.

We’ve been having conversations for thousands of years. Whether to convey information, conduct transactions, or simply to check in on one another, people have yammered away, chattering and gesticulating, through spoken conversation for countless generations. Only in the last few millennia have we begun to commit our conversations to writing, and only in the last few decades have we begun to outsource them to the computer, a machine that shows much more affinity for written correspondence than for the slangy vagaries of spoken language.

Computers have trouble because between spoken and written language, speech is more primordial. To have successful conversations with us, machines must grapple with the messiness of human speech: the disfluencies and pauses, the gestures and body language, and the variations in word choice and spoken dialect that can stymie even the most carefully crafted human-computer interaction. In the human-to-human scenario, spoken language also has the privilege of face-to-face contact, where we can readily interpret nonverbal social cues.

In contrast, written language immediately concretizes as we commit it to record and retains usages long after they become obsolete in spoken communication (the salutation “To whom it may concern,” for example), generating its own fossil record of outdated terms and phrases. Because it tends to be more consistent, polished, and formal, written text is fundamentally much easier for machines to parse and understand.

Spoken language has no such luxury. Besides the nonverbal cues that decorate conversations with emphasis and emotional context, there are also verbal cues and vocal behaviors that modulate conversation in nuanced ways: how something is said, not what. Whether rapid-fire, low-pitched, or high-decibel, whether sarcastic, stilted, or sighing, our spoken language conveys much more than the written word could ever muster. So when it comes to voice interfaces—the machines we conduct spoken conversations with—we face exciting challenges as designers and content strategists.

Voice Interactions

We interact with voice interfaces for a variety of reasons, but according to Michael McTear, Zoraida Callejas, and David Griol in The Conversational Interface, those motivations by and large mirror the reasons we initiate conversations with other people, too (). Generally, we start up a conversation because:

• we need something done (such as a transaction),
• we want to know something (information of some sort), or
• we are social beings and want someone to talk to (conversation for conversation’s sake).

These three categories—which I call transactional, informational, and prosocial—also characterize essentially every voice interaction: a single conversation from beginning to end that realizes some outcome for the user, starting with the voice interface’s first greeting and ending with the user exiting the interface. Note here that a conversation in our human sense—a chat between people that leads to some result and lasts an arbitrary length of time—could encompass multiple transactional, informational, and prosocial voice interactions in succession. In other words, a voice interaction is a conversation, but a conversation is not necessarily a single voice interaction.

Purely prosocial conversations are more gimmicky than captivating in most voice interfaces, because machines don’t yet have the capacity to really want to know how we’re doing and to do the sort of glad-handing humans crave. There’s also ongoing debate as to whether users actually prefer the sort of organic human conversation that begins with a prosocial voice interaction and shifts seamlessly into other types. In fact, in Voice User Interface Design, Michael Cohen, James Giangola, and Jennifer Balogh recommend sticking to users’ expectations by mimicking how they interact with other voice interfaces rather than trying too hard to be human—potentially alienating them in the process ().

That leaves two genres of conversations we can have with one another that a voice interface can easily have with us, too: a transactional voice interaction realizing some outcome (“buy iced tea”) and an informational voice interaction teaching us something new (“discuss a musical”).

Transactional voice interactions

Unless you’re tapping buttons on a food delivery app, you’re generally having a conversation—and therefore a voice interaction—when you order a Hawaiian pizza with extra pineapple. Even when we walk up to the counter and place an order, the conversation quickly pivots from an initial smattering of neighborly small talk to the real mission at hand: ordering a pizza (generously topped with pineapple, as it should be).

Alison: Hey, how’s it going?

Burhan: Hi, welcome to Crust Deluxe! It’s cold out there. How can I help you?

Alison: Can I get a Hawaiian pizza with extra pineapple?

Burhan: Sure, what size?

Alison: Large.

Burhan: Anything else?

Alison: No thanks, that’s it.

Burhan: Something to drink?

Alison: I’ll have a bottle of Coke.

Burhan: You got it. That’ll be $13.55 and about fifteen minutes.

Each progressive disclosure in this transactional conversation reveals more and more of the desired outcome of the transaction: a service rendered or a product delivered. Transactional conversations have certain key traits: they’re direct, to the point, and economical. They quickly dispense with pleasantries.

Informational voice interactions

Meanwhile, some conversations are primarily about obtaining information. Though Alison might visit Crust Deluxe with the sole purpose of placing an order, she might not actually want to walk out with a pizza at all. She might be just as interested in whether they serve halal or kosher dishes, gluten-free options, or something else. Here, though we again have a prosocial mini-conversation at the beginning to establish politeness, we’re after much more.

Alison: Hey, how’s it going?

Burhan: Hi, welcome to Crust Deluxe! It’s cold out there. How can I help you?

Alison: Can I ask a few questions?

Burhan: Of course! Go right ahead.

Alison: Do you have any halal options on the menu?

Burhan: Absolutely! We can make any pie halal by request. We also have lots of vegetarian, ovo-lacto, and vegan options. Are you thinking about any other dietary restrictions?

Alison: What about gluten-free pizzas?

Burhan: We can definitely do a gluten-free crust for you, no problem, for both our deep-dish and thin-crust pizzas. Anything else I can answer for you?

Alison: That’s it for now. Good to know. Thanks!

Burhan: Anytime, come back soon!

This is a very different dialogue. Here, the goal is to get a certain set of facts. Informational conversations are investigative quests for the truth—research expeditions to gather data, news, or facts. Voice interactions that are informational might be more long-winded than transactional conversations by necessity. Responses tend to be lengthier, more informative, and carefully communicated so the customer understands the key takeaways.

Voice Interfaces

At their core, voice interfaces employ speech to support users in reaching their goals. But simply because an interface has a voice component doesn’t mean that every user interaction with it is mediated through voice. Because multimodal voice interfaces can lean on visual components like screens as crutches, we’re most concerned in this book with pure voice interfaces, which depend entirely on spoken conversation, lack any visual component whatsoever, and are therefore much more nuanced and challenging to tackle.

Though voice interfaces have long been integral to the imagined future of humanity in science fiction, only recently have those lofty visions become fully realized in genuine voice interfaces.

Interactive voice response (IVR) systems

Though written conversational interfaces have been fixtures of computing for many decades, voice interfaces first emerged in the early 1990s with text-to-speech (TTS) dictation programs that recited written text aloud, as well as speech-enabled in-car systems that gave directions to a user-provided address. With the advent of interactive voice response (IVR) systems, intended as an alternative to overburdened customer service representatives, we became acquainted with the first true voice interfaces that engaged in authentic conversation.

IVR systems allowed organizations to reduce their reliance on call centers but soon became notorious for their clunkiness. Commonplace in the corporate world, these systems were primarily designed as metaphorical switchboards to guide customers to a real phone agent (“Say Reservations to book a flight or check an itinerary”); chances are you will enter a conversation with one when you call an airline or hotel conglomerate. Despite their functional issues and users’ frustration with their inability to speak to an actual human right away, IVR systems proliferated in the early 1990s across a variety of industries (, PDF).

While IVR systems are great for highly repetitive, monotonous conversations that generally don’t veer from a single format, they have a reputation for less scintillating conversation than we’re used to in real life (or even in science fiction).

Screen readers

Parallel to the evolution of IVR systems was the invention of the screen reader, a tool that transcribes visual content into synthesized speech. For Blind or visually impaired website users, it’s the predominant method of interacting with text, multimedia, or form elements. Screen readers represent perhaps the closest equivalent we have today to an out-of-the-box implementation of content delivered through voice.

Among the first screen readers known by that moniker was the Screen Reader for the BBC Micro and NEEC Portable developed by the Research Centre for the Education of the Visually Handicapped (RCEVH) at the University of Birmingham in 1986 (). That same year, Jim Thatcher created the first IBM Screen Reader for text-based computers, later recreated for computers with graphical user interfaces (GUIs) ().

With the rapid growth of the web in the 1990s, the demand for accessible tools for websites exploded. Thanks to the introduction of semantic HTML and especially ARIA roles beginning in 2008, screen readers started facilitating speedy interactions with web pages that ostensibly allow disabled users to traverse the page as an aural and temporal space rather than a visual and physical one. In other words, screen readers for the web “provide mechanisms that translate visual design constructs—proximity, proportion, etc.—into useful information,” writes Aaron Gustafson in A List Apart. “At least they do when documents are authored thoughtfully” ().

Though deeply instructive for voice interface designers, there’s one significant problem with screen readers: they’re difficult to use and unremittingly verbose. The visual structures of websites and web navigation don’t translate well to screen readers, sometimes resulting in unwieldy pronouncements that name every manipulable HTML element and announce every formatting change. For many screen reader users, working with web-based interfaces exacts a cognitive toll.

In Wired, accessibility advocate and voice engineer Chris Maury considers why the screen reader experience is ill-suited to users relying on voice:

From the beginning, I hated the way that Screen Readers work. Why are they designed the way they are? It makes no sense to present information visually and then, and only then, translate that into audio. All of the time and energy that goes into creating the perfect user experience for an app is wasted, or even worse, adversely impacting the experience for blind users. ()

In many cases, well-designed voice interfaces can speed users to their destination better than long-winded screen reader monologues. After all, visual interface users have the benefit of darting around the viewport freely to find information, ignoring areas irrelevant to them. Blind users, meanwhile, are obligated to listen to every utterance synthesized into speech and therefore prize brevity and efficiency. Disabled users who have long had no choice but to employ clunky screen readers may find that voice interfaces, particularly more modern voice assistants, offer a more streamlined experience.

Voice assistants

When we think of voice assistants (the subset of voice interfaces now commonplace in living rooms, smart homes, and offices), many of us immediately picture HAL from 2001: A Space Odyssey or hear Majel Barrett’s voice as the omniscient computer in Star Trek. Voice assistants are akin to personal concierges that can answer questions, schedule appointments, conduct searches, and perform other common day-to-day tasks. And they’re rapidly gaining more attention from accessibility advocates for their assistive potential.

Before the earliest IVR systems found success in the enterprise, Apple published a demonstration video in 1987 depicting the Knowledge Navigator, a voice assistant that could transcribe spoken words and recognize human speech to a great degree of accuracy. Then, in 2001, Tim Berners-Lee and others formulated their vision for a Semantic Web “agent” that would perform typical errands like “checking calendars, making appointments, and finding locations” (, behind paywall). It wasn’t until 2011 that Apple’s Siri finally entered the picture, making voice assistants a tangible reality for consumers.

Thanks to the plethora of voice assistants available today, there is considerable variation in how programmable and customizable certain voice assistants are over others (Fig 1.1). At one extreme, everything except vendor-provided features is locked down; for example, at the time of their release, the core functionality of Apple’s Siri and Microsoft’s Cortana couldn’t be extended beyond their existing capabilities. Even today, it isn’t possible to program Siri to perform arbitrary functions, because there’s no means by which developers can interact with Siri at a low level, apart from predefined categories of tasks like sending messages, hailing rideshares, making restaurant reservations, and certain others.

At the opposite end of the spectrum, voice assistants like Amazon Alexa and Google Home offer a core foundation on which developers can build custom voice interfaces. For this reason, programmable voice assistants that lend themselves to customization and extensibility are becoming increasingly popular for developers who feel stifled by the limitations of Siri and Cortana. Amazon offers the Alexa Skills Kit, a developer framework for building custom voice interfaces for Amazon Alexa, while Google Home offers the ability to program arbitrary Google Assistant skills. Today, users can choose from among thousands of custom-built skills within both the Amazon Alexa and Google Assistant ecosystems.

As corporations like Amazon, Apple, Microsoft, and Google continue to stake their territory, they’re also selling and open-sourcing an unprecedented array of tools and frameworks for designers and developers that aim to make building voice interfaces as easy as possible, even without code.

Often by necessity, voice assistants like Amazon Alexa tend to be monochannel—they’re tightly coupled to a device and can’t be accessed on a computer or smartphone instead. By contrast, many development platforms like Google’s Dialogflow have introduced omnichannel capabilities so users can build a single conversational interface that then manifests as a voice interface, textual chatbot, and IVR system upon deployment. I don’t prescribe any specific implementation approaches in this design-focused book, but in Chapter 4 we’ll get into some of the implications these variables might have on the way you build out your design artifacts.

Voice Content

Simply put, voice content is content delivered through voice. To preserve what makes human conversation so compelling in the first place, voice content needs to be free-flowing and organic, contextless and concise—everything written content isn’t.

Our world is replete with voice content in various forms: screen readers reciting website content, voice assistants rattling off a weather forecast, and automated phone hotline responses governed by IVR systems. In this book, we’re most concerned with content delivered auditorily—not as an option, but as a necessity.

For many of us, our first foray into informational voice interfaces will be to deliver content to users. There’s only one problem: any content we already have isn’t in any way ready for this new habitat. So how do we make the content trapped on our websites more conversational? And how do we write new copy that lends itself to voice interactions?

Lately, we’ve begun slicing and dicing our content in unprecedented ways. Websites are, in many respects, colossal vaults of what I call macrocontent: lengthy prose that can extend for infinitely scrollable miles in a browser window, like microfilm viewers of newspaper archives. Back in 2002, well before the present-day ubiquity of voice assistants, technologist Anil Dash defined microcontent as permalinked pieces of content that stay legible regardless of environment, such as email or text messages:

A day’s weather forcast [sic], the arrival and departure times for an airplane flight, an abstract from a long publication, or a single instant message can all be examples of microcontent. ()

I’d update Dash’s definition of microcontent to include all examples of bite-sized content that go well beyond written communiqués. After all, today we encounter microcontent in interfaces where a small snippet of copy is displayed alone, unmoored from the browser, like a textbot confirmation of a restaurant reservation. Microcontent offers the best opportunity to gauge how your content can be stretched to the very edges of its capabilities, informing delivery channels both established and novel.

As microcontent, voice content is unique because it’s an example of how content is experienced in time rather than in space. We can glance at a digital sign underground for an instant and know when the next train is arriving, but voice interfaces hold our attention captive for periods of time that we can’t easily escape or skip, something screen reader users are all too familiar with.

Because microcontent is fundamentally made up of isolated blobs with no relation to the channels where they’ll eventually end up, we need to ensure that our microcontent truly performs well as voice content—and that means focusing on the two most important traits of robust voice content: voice content legibility and voice content discoverability.

Fundamentally, the legibility and discoverability of our voice content both have to do with how voice content manifests in perceived time and space.

Some wealthy runners had come to the conclusion that it was impossible to run a mile in less than four hours in the 1950s. Riders had been attempting it since the later 19th century and were beginning to draw the conclusion that the human body just wasn’t built for the job.

However, on May 6, 1956, Roger Bannister caught people by shock. It was a cold, damp morning in Oxford, England—conditions no one expected to give themselves to record-setting—and but Bannister did really that, running a mile in 3: 59.4 and becoming the first people in the history books to run a mile in under four hours.

The world presently knew that the four-minute hour could be accomplished thanks to this change in the standard. Bannister’s history lasted just forty-six days, when it was snatched aside by American sprinter John Landy. Finally, a year later, three runners all managed to cross the four-minute hurdle in the same culture. Since therefore, over 1, 400 walkers have actually run a mile in under four days, the current document is 3: 43.13, held by Moroccan performer Hicham El Guerrouj.

We do a lot more when we think something is possible, and we only think it can be done when we see someone else doing it once more. As for man running speed, we also think there are strict guidelines for how a website should do.

Establishing requirements for a green website

The key indicators of climate performance in most big companies are very well established, such as power per square metre for homes and miles per gallon for cars. The tools and methods for calculating those measures are standardized as well, which keeps everyone on the same site when doing economic evaluations. However, we aren’t held to any specific environmental standards in the world of websites and apps, and we only recently have access to the tools and strategies we need to do so.

The main objective in green web layout is to reduce carbon emissions. However, it’s nearly impossible to accurately assess the amount of CO₂ that a website merchandise produces. We can’t measure the pollutants coming out of the exhaust valves on our devices. Our sites produce far-away, invisible, and unremarkable emissions when they leave fuel and gas-burning power plants. We have no way to track the particles from a website or app up to the power station where the light is being generated and really know the exact amount of house oil produced. What then do we do?

If we can‘t measure the actual carbon emissions, then we need to get what we can estimate. The following are the main elements that could be used as coal pollution gauges:

1. Transfer of data
2. Electricity’s coal power

Let’s take a look at how we can use these indicators to calculate the energy use, and in turn the carbon footprint, of the sites and web applications we create.

Transfer of data

Most researchers use kilowatt-hours per gigabyte (k Wh/GB ) as a metric of energy efficiency when measuring the amount of data transferred over the internet when a website or application is used. This serves as a reliable indicator of how much energy is being consumed and how much carbon is being released. As a rule of thumb, the more data transferred, the more energy used in the data center, telecoms networks, and end user devices.

The easiest way to calculate data transfer for a single visit for web pages is to measure the page weight, which is the page’s transfer size in kilobytes when someone first visits the page. It’s fairly easy to measure using the developer tools in any modern web browser. Statistics for the total data transfer of any web application are frequently included in your web hosting account ( Fig. 2.1 ).

The nice thing about page weight as a metric is that it allows us to compare the efficiency of web pages on a level playing field without confusing the issue with constantly changing traffic volumes.

A large scope is required to reduce page weight. By early 2020, the median page weight was 1.97 MB for setups the HTTP Archive classifies as “desktop” and 1.77 MB for “mobile”, with desktop increasing 36 percent since January 2016 and mobile page weights nearly doubling in the same period ( Fig 2.2 ). Image files account for roughly half of this data transfer, making them the single biggest contributor to carbon emissions on a typical website.

History clearly shows us that our web pages can be smaller, if only we set our minds to it. While the majority of technologies, including the underlying technology of the web like data centers and transmission networks, become more and more energy efficient, websites themselves become less effective as time goes on.

You may be aware of the idea of performance budgeting as a method for directing a project team to deliver faster user experiences. For example, we might specify that the website must load in a maximum of one second on a broadband connection and three seconds on a 3G connection. Performance budgets are upper limits rather than hazy ideas, much like speed limits while driving. As a result, the goal should always be to stay within budget.

Designing for fast performance does often lead to reduced data transfer and emissions, but it isn’t always the case. Page weight and transfer size are more objective and reliable benchmarks for sustainable web design, whereas web performance often depends more on the user’s perception of load times than it does on how effective the underlying system is.

We can set a page weight budget in reference to a benchmark of industry averages, using data from sources like HTTP Archive. We can also use competitor page weight to compare the new website to the old one. For example, we might set a maximum page weight budget as equal to our most efficient competitor, or we could set the benchmark lower to guarantee we are best in class.

If we want to take it to the next level, we could start looking at how much more popular our web pages are when people visit them frequently. Although page weight for the first time someone visits is the easiest thing to measure, and easy to compare on a like-for-like basis, we can learn even more if we start looking at transfer size in other scenarios too. For instance, visitors who load the same page more frequently will likely have a high percentage of the files cached in their browser, which means they won’t need to move all the files on subsequent visits. Likewise, a visitor who navigates to new pages on the same website will likely not need to load the full page each time, as some global assets from areas like the header and footer may already be cached in their browser. Moving away from the first visit and allowing us to determine page weight budgets for scenarios other than this one can help us learn even more about how to optimize efficiency for users who regularly visit our pages.

Page weight budgets are easy to track throughout a design and development process. Although they don’t directly disclose carbon emissions and energy consumption data, they do provide a clear indicator of efficiency in comparison to other websites. And as transfer size is an effective analog for energy consumption, we can actually use it to estimate energy consumption too.

In summary, less data transfer leads to more energy efficiency, which is a crucial component of reducing web product carbon emissions. The more efficient our products, the less electricity they use, and the less fossil fuels need to be burned to produce the electricity to power them. However, as we’ll see next, it’s important to take into account the source of that electricity because all web products require some power.

Electricity’s coal power

Regardless of energy efficiency, the level of pollution caused by digital products depends on the carbon intensity of the energy being used to power them. The term” carbon intensity” is used to describe how many grams of carbon are produced for every kilowatt-hour of electricity (gCO₂/k Wh ). This varies widely, with renewable energy sources and nuclear having an extremely low carbon intensity of less than 10 gCO₂/k Wh ( even when factoring in their construction ), whereas fossil fuels have very high carbon intensity of approximately ₂00–400 gCO₂/k Wh.

The majority of electricity is produced by national or state grids, where different levels of carbon intensity are combined with energy from a variety of sources. The distributed nature of the internet means that a single user of a website or app might be using energy from multiple different grids simultaneously, a website user in Paris uses electricity from the French national grid to power their home internet and devices, but the website’s data center could be in Dallas, USA, pulling electricity from the Texas grid, while the telecoms networks use energy from everywhere between Dallas and Paris.

Although we don’t have complete control over the energy supply of web services, we do have some control over where our projects are hosted. With a data center using a significant proportion of the energy of any website, locating the data center in an area with low carbon energy will tangibly reduce its carbon emissions. Danish startup Tomorrow reports and maps the user-provided data, and a look at their map demonstrates how, for instance, choosing a data center in France will have significantly lower carbon emissions than choosing a data center in the Netherlands ( Fig. 2.3 ).

However, we don’t want to move our servers too far away from our users because it requires a lot of energy to transmit data through the telecom’s networks, and the more energy is used, the further the data travels. Just like food miles, we can think of the distance from the data center to the website’s core user base as “megabyte miles” —and we want it to be as small as possible.

We can use website analytics to determine the country, state, or even city where our core user group is located and measure the distance from that location to the data center used by our hosting company by using the distance itself as a benchmark. This will be a somewhat fuzzy metric as we don’t know the precise center of mass of our users or the exact location of a data center, but we can at least get a rough idea.

For instance, if a website is hosted in London but the main audience is on the United States ‘ West Coast, we could calculate the distance between San Francisco and London, which is 5,300 miles. That’s a long way! We can see how significantly lessening the distance and energy needed to transmit the data would be if it was hosted somewhere in North America, ideally on the West Coast. In addition, locating our servers closer to our visitors helps reduce latency and delivers better user experience, so it’s a win-win.

Reverting it to carbon emissions

If we combine carbon intensity with a calculation for energy consumption, we can calculate the carbon emissions of our websites and apps. A tool my team created accomplishes this by measuring the data transfer over the wire when a web page is loaded, calculating the associated electricity consumption, and then converting that data into a CO₂ figure ( Fig. ₂.4). It also factors in whether or not the web hosting is powered by renewable energy.

The Energy and Emissions Worksheet that comes with this book teaches you how to take it one step further and tailor the data more precisely to the unique aspects of your project.

With the ability to calculate carbon emissions for our projects, we could even set up carbon budgets as well. CO₂ is not a metric commonly used in web projects, we’re more familiar with kilobytes and megabytes, and can fairly easily look at design options and files to assess how big they are. Although translating that into carbon adds an air of abstraction, carbon budgets do focus our minds on the main issue we’re trying to reduce, which also supports the main goal of sustainable web design: reducing carbon emissions.

Browser Energy

Transfer of data might be the simplest and most complete analog for energy consumption in our digital projects, but by giving us one number to represent the energy used in the data center, the telecoms networks, and the end user’s devices, it can’t offer us insights into the efficiency in any specific part of the system.

One part of the system we can look at in more detail is the energy used by end users ‘ devices. The computational load is increasingly shifting from the data center to users ‘ devices, whether they are phones, tablets, laptops, desktops, or even smart TVs, as front-end web technologies advance. Modern web browsers allow us to implement more complex styling and animation on the fly using CSS and JavaScript. Additionally, JavaScript libraries like Angular and React allow us to create applications where the” thinking” process is performed either partially or completely in the browser.

All of these advances are exciting and open up new possibilities for what the web can do to serve society and create positive experiences. However, more data is processed in a web browser, which means more energy is used by the user’s devices. This has implications not just environmentally, but also for user experience and inclusivity. Applications that put a lot of processing power on a user’s device unintentionally exclude users with older, slower devices and make the batteries on phones and laptops drain more quickly. Furthermore, if we build web applications that require the user to have up-to-date, powerful devices, people throw away old devices much more frequently. This not only hurts the environment, but it also places a disproportionate financial burden on society’s poorest.

In part because the tools are limited, and partly because there are so many different models of devices, it’s difficult to measure website energy consumption on end users ‘ devices. The Energy Impact monitor inside the developer console of the Safari browser is one of the tools we currently have ( Fig. 2.5 ).

You are aware of the moment your computer’s cooling fans start spinning so frantically that you mistakenly believe it might take off when you load a website? That’s essentially what this tool is measuring.

It uses these figures to create an energy impact rating and shows the percentage of CPU used and how long the CPU used when loading the web page last. It doesn’t give us precise data for the amount of electricity used in kilowatts, but the information it does provide can be used to benchmark how efficiently your websites use energy and set targets for improvement.

This book will provide you with that plan of action. It covers how to incorporate safety principles into your design work in order to make tech that’s secure, how to persuade your stakeholders that this work is important, and how to respond to the critique that what we really need is more diversity. ( Spoiler: We do, but diversity alone cannot solve unethical, unsafe technology. )

The procedure for diverse safety

Your objectives when designing for protection are to:

• discover ways your solution can be used for abuse,
• style ways to prevent the maltreatment, and
• provide assistance for customers who are prone to regain control and power.

The Process for Inclusive Safety is a tool to help you reach those goals ( Fig 5.1 ). I developed this strategy in 2018 to better understand the different methods I used to create products that were designed with safety in mind. Whether you are creating an entirely new product or adding to an existing element, the Process can help you produce your product secure and diverse. The Process includes five public areas of action:

• conducting studies
• Creating tropes
• Pondering issues
• Designing options
• Testing for security

It is intended to be flexible, so teams might not want to utilize every action in all circumstances. Use the parts that are related to your special function and environment, this is meant to be something you can put into your existing style process.

And once you use it, if you have an idea for making it better or simply want to give perspective of how it helped your group, please get in touch with me. It’s a living document, and I want to use it as a practical and useful application for engineers in their day-to-day tasks.

If you’re working on a product especially for a resilient team or survivors of some form of injury, such as an application for survivors of domestic violence, sexual abuse, or drug addiction, be sure to read Section 7, which covers that position directly and should be handled a bit different. The purpose of this design is to prioritize safety when creating a more general product with a broad user base ( which, as we already know from statistics, will include some groups who need to be protected from harm ). Chapter 7 is focused on products that are specifically for vulnerable groups and people who have experienced trauma.

Step 1: Conduct research

Design research should include a thorough analysis of how your technology might be used for abuse as well as specific insights into the experiences of those who have witnessed and perpetrated that kind of abuse. At this stage, you and your team will investigate issues of interpersonal harm and abuse, and explore any other safety, security, or inclusivity issues that might be a concern for your product or service, like data security, racist algorithms, and harassment.

broad analysis

Your project should begin with broad, general research into similar products and issues around safety and ethical concerns that have already been reported. For example, a team building a smart home device would do well to understand the multitude of ways that existing smart home devices have been used as tools of abuse. If your product involves artificial intelligence, be aware of the potential for racism and other issues that have been reported in other AI products. Nearly all types of technology have some kind of potential or actual harm that’s been reported on in the news or written about by academics. Google Scholar is a useful resource for locating these studies.

Specific research: Survivors

When possible and appropriate, include direct research ( surveys and interviews ) with people who are experts in the forms of harm you have uncovered. In order to have a better understanding of the subject and be better positioned to prevent retraumatize survivors, you should interview advocates working in the area of your research first. If you’ve uncovered possible domestic violence issues, for example, the experts you’ll want to speak with are survivors themselves, as well as workers at domestic violence hotlines, shelters, other related nonprofits, and lawyers.

It is important to pay people for their knowledge and lived experiences, especially when interviewing survivors of any kind of trauma. Don’t ask survivors to share their trauma for free, as this is exploitative. While some survivors may not want to be paid, you should always make the offer in the initial ask. Alternative to paying is to donate to a cause fighting the kind of violence the interviewee experienced. We’ll talk more about how to appropriately interview survivors in Chapter 6.

Abusers specifically: research

It’s unlikely that teams aiming to design for safety will be able to interview self-proclaimed abusers or people who have broken laws around things like hacking. Don’t make this a goal, rather, try to get at this angle in your general research. Attempt to understand how abusers or bad actors use technology to harm others, how they use it against others, and how they justify or explain the abuse.

Step 2: Create archetypes

Use your research’s findings to create the archetypes of abuser and survivor once you’ve finished your research. Archetypes are not personas, as they’re not based on real people that you interviewed and surveyed. Instead, they’re based on your research into likely safety issues, much like when we design for accessibility: we don’t need to have found a group of blind or low-vision users in our interview pool to create a design that’s inclusive of them. Instead, we base those designs on existing research and what this group requires. Personas typically represent real users and include many details, while archetypes are broader and can be more generalized.

The abuser archetype is someone who views a product as a means of harm ( Fig. 5.2 ). They may be trying to harm someone they don’t know through surveillance or anonymous harassment, or they may be trying to control, monitor, abuse, or torment someone they know personally.

The survivor archetype refers to a person who is being abused with the product. There are various situations to consider in terms of the archetype’s understanding of the abuse and how to put an end to it: Do they need proof of abuse they already suspect is happening, or are they unaware they’ve been targeted in the first place and need to be alerted ( Fig 5.3 )?

You may want to make multiple survivor archetypes to capture a range of different experiences. They may be aware of the abuse being occurring but not be able to stop it, such as when a stalker keeps tracing their whereabouts or when an abuser locks them out of IoT devices ( Fig. 5.4). Include as many of these scenarios as you need to in your survivor archetype. These will be used later when you create solutions to help your survivor archetypes achieve their goals of preventing and ending abuse.

It may be useful for you to create persona-like artifacts for your archetypes, such as the three examples shown. Focus on their objectives rather than the demographic details we frequently see in personas. The goals of the abuser will be to carry out the specific abuse you’ve identified, while the goals of the survivor will be to prevent abuse, understand that abuse is happening, make ongoing abuse stop, or regain control over the technology that’s being used for abuse. Later, you’ll think about how to help the survivor’s goals and the abuser’s goals.

And while the “abuser/survivor” model fits most cases, it doesn’t fit all, so modify it as you need to. For example, if you uncovered an issue with security, such as the ability for someone to hack into a home camera system and talk to children, the malicious hacker would get the abuser archetype and the child’s parents would get survivor archetype.

Step 3: Brainstorm issues

After creating archetypes, brainstorm novel abuse cases and safety issues. You’re trying to identify completely new safety issues that are unique to your product or service by using the term” Novel” in terms of things that are not found in your research. The goal with this step is to exhaust every effort of identifying harms your product could cause. You aren’t worrying about how to prevent the harm yet—that comes in the next step.

What other uses could your product be used for besides what you’ve already identified in your research? I recommend setting aside at least a few hours with your team for this process.

Try conducting a Black Mirror brainstorming if you’re looking for a place to start. This exercise is based on the show Black Mirror, which features stories about the dark possibilities of technology. Try to figure out how your product would be used in an episode of the show—the most wild, awful, out-of-control ways it could be used for harm. Participants in Black Mirror brainstorming typically end up having a lot of fun ( which I believe is great because having fun when designing for safety! ). I recommend time-boxing a Black Mirror brainstorm to half an hour, and then dialing it back and using the rest of the time thinking of more realistic forms of harm.

You may still not feel confident that you have found every possible source of harm after identifying as many opportunities for abuse as possible. A healthy amount of anxiety is normal when you’re doing this kind of work. It’s common for teams designing for safety to worry,” Have we really identified every possible harm? What if something is missing, then? If you’ve spent at least four hours coming up with ways your product could be used for harm and have run out of ideas, go to the next step.

It’s impossible to say 100 % assurance that you’ve done everything right, but instead of aiming for 100 % assurance, acknowledge that you’ve taken this step and have done everything you can, and pledge to keep putting safety first in the future. Once your product is released, your users may identify new issues that you missed, aim to receive that feedback graciously and course-correct quickly.

Step 4: Design solutions

You should now be able to identify potential harm-causing uses for your product as well as survivor and abuser archetypes describing opposing user objectives. The next step is to identify ways to design against the identified abuser’s goals and to support the survivor’s goals. This is a good addition to existing areas of your design process where you’re making recommendations for solutions to the various issues your research has identified.

Some questions to ask yourself to help prevent harm and support your archetypes include:

• Can you design your product in such a way that the identified harm cannot happen in the first place? If not, what barriers can you place to stop the harm from occurring?
• How can you make the victim aware that abuse is happening through your product?
• How can you explain to the victim what they must do to stop the problem?
• Can you identify any types of user activity that would indicate some form of harm or abuse? Could your product help the user access support?

It’s possible to anticipate harm from occurring in some products. For example, a pregnancy app might be modified to allow the user to report that they were the victim of an assault, which could trigger an offer to receive resources for local and national organizations. Although it’s not always possible to be this proactive, it’s worthwhile to spend an hour discussing whether any kind of user activity would indicate harm or abuse and how your product could help them in a secure manner.

That said, use caution: you don’t want to do anything that could put a user in harm’s way if their devices are being monitored. If you do offer some kind of proactive help, always make it voluntary, and think through other safety issues, such as the need to keep the user in-app in case an abuser is checking their search history. In the next chapter, we’ll walk through a good illustration of this.

Step 5: Test for safety

The final step is to evaluate the prototypes against the perspectives of your archetypes, who wants to harm the product or the victim of the harm who needs to regain control of the technology. Just like any other kind of product testing, at this point you’ll aim to rigorously test out your safety solutions so that you can identify gaps and correct them, validate that your designs will help keep your users safe, and feel more confident releasing your product into the world.

Ideally, safety testing happens along with usability testing. If you work for a company that doesn’t conduct usability testing, you might be able to use safety testing to deftly perform both. A user who uses your design while trying to use it against someone else can also be encouraged to point out interactions or other design details that don’t make sense to them.

You’ll want to conduct safety testing on either your final prototype or the actual product if it’s already been released. There is no harm in testing an existing product that wasn’t created with safety goals in mind right away; “etrofitting” it for safety is a wise thing to do.

Remember that testing for safety involves testing from the perspective of both an abuser and a survivor, though it may not make sense for you to do both. Alternatively, if you made multiple survivor archetypes to capture multiple scenarios, you’ll want to test from the perspective of each one.

You as the designer are most likely too closely connected to the product and its design at this point, just like other types of usability testing, and you know the product too well. Instead of doing it yourself, set up testing as you would with other usability testing: find someone who is not familiar with the product and its design, set the scene, give them a task, encourage them to think out loud, and observe how they attempt to complete it.

Abuse testing

The goal of this testing is to understand how easy it is for someone to weaponize your product for harm. Unlike with usability testing, you want to make it impossible, or at least difficult, for them to achieve their goal. Use your product to try to accomplish the objectives in the abuser archetype you created earlier.

For example, for a fitness app with GPS-enabled location features, we can imagine that the abuser archetype would have the goal of figuring out where his ex-girlfriend now lives. With this in mind, you’d make every effort to discover the location of a different user who has their privacy settings turned on. You might try to see her running routes, view any available information on her profile, view anything available about her location ( which she has set to private ), and investigate the profiles of any other users somehow connected with her account, such as her followers.

If by the end of this you’ve managed to uncover some of her location data, despite her having set her profile to private, you know now that your product enables stalking. Reverting to step 4 and figuring out how to stop this from occurring is your next step. You may need to repeat the process of designing solutions and testing them more than once.

testing for a Survivor

testing for a Survivor involves identifying how to give information and power to the survivor. It might not always make sense based on the product or context. Thwarting the attempt of an abuser archetype to stalk someone also satisfies the goal of the survivor archetype to not be stalked, so separate testing wouldn’t be needed from the survivor’s perspective.

However, there are cases where it makes sense. A survivor archetype’s goal would be to discover who or what causes the temperature change when they aren’t doing it themselves, for instance. You could test this by looking for the thermostat’s history log and checking for usernames, actions, and times, if you couldn’t find that information, you would have more work to do in step 4.

Another goal might be regaining control of the thermostat once the survivor realizes the abuser is remotely changing its settings. Are there any instructions that explain how to remove a user and change the password, and are they simple to find? For your test, you would need to try to figure out how to do this. This might again reveal that more work is needed to make it clear to the user how they can regain control of the device or account.

stress testing

To make your product more inclusive and compassionate, consider adding stress testing. This concept comes from Design for Real Life by Eric Meyer and Sara Wachter-Boettcher. The authors noted that personas typically focus on happy people, but happy people are frequently anxious, stressed, unhappy, or even tragic. These are called” stress cases”, and testing your products for users in stress-case situations can help you identify places where your design lacks compassion. More information about how to incorporate stress cases into your design can be found in Design for Real Life, as well as in many other effective methods for compassionate design.

But how can a content management system ( CMS ) be set up to reach your current and future audience? I learned the hard way that creating a content model—a concept of information types, attributes, and relationships that let people and systems understand content—with my more comfortable design-system wondering would collapse my patient’s holistic information strategy. By developing glad versions that are conceptual and that also connect related information, you can avoid that result.

I just had the opportunity to lead a Fortune 500 company’s CMS execution. The customer was excited by the benefits of an holistic information plan, including material modify, multichannel marketing, and robot delivery—designing content to be comprehensible to bots, Google knowledge panels, snippets, and voice user interfaces.

For our information to be understood by many systems, the unit needed semantic types, which are names given based on their meaning rather than their presentation. This is crucial for an multichannel content strategy. Our goal was to allow writers to create original content that could be used wherever they felt was most useful. But as the job proceeded, I realized that supporting material utilize at the range that my client needed required the whole group to identify a new pattern.

Despite our best efforts, we remained influenced by what we were more common with: design methods. An holistic content strategy cannot rely on WYSIWYG design and layout tools, unlike web-focused willing strategies. Our tendency to approach the material model with our common design-system thinking frequently led us to veer away from one of the main purposes of a material model: delivering content to audiences on multiple marketing channels.

Two fundamental tenets are necessary for a successful content model

We needed to explain to our designers, developers, and stakeholders that we were doing something completely different from their previous web projects, where everyone assumed that content would fit into layouts as visual building blocks. The previous approach was not only more familiar but also more intuitive—at least at first—because it made the designs feel more tangible. The team was able to understand how a content model differs from the design systems we were familiar with by discovering two principles:

1. Instead of layout, content models must define semantics.
2. And content models should connect content that belongs together.

Semantic content models

A semantic content model uses type and attribute names that reflect the content’s intended purpose and not how it will be displayed. For example, in a nonsemantic model, teams might create types like teasers, media blocks, and cards. These types may simplify the presentation of content, but they do not aid in understanding the meaning of the content, which would have opened the door to the content presented in each marketing channel. In contrast, a semantic content model uses type names like “product,”” service,” and “testimonial” to allow for each delivery channel to interpret and use the content as it sees fit.

When you’re creating a semantic content model, a great place to start is to look over the types and properties defined by Schema. a community-driven resource for type definitions that are understandable on platforms like Google search.

A semantic content model has a number of advantages:

Even if your team doesn’t care about omnichannel content, a semantic content model decouples content from its presentation so that teams can evolve the website’s design without needing to refactor its content. In this way, content can withstand irrational website redesigns.
• A semantic content model also gives you an advantage in the market. By adding structured data based on Schema. A website can provide hints to Google to understand the content, display it in search snippets or knowledge panels, and use it to respond to user voice-interface queries. Potential visitors could access your content without ever walking into your website.
• Beyond those practical benefits, you’ll also need a semantic content model if you want to deliver omnichannel content. Delivery channels must be able to comprehend the same content in order to use it across multiple marketing channels. For instance, if your content model provided a list of questions and answers, it could be used as a voice interface or by a bot to answer frequently asked questions ( FAQ ) pages.

For example, using a semantic content model for articles, events, people, and locations lets A List Apart provide cleanly structured data for search engines so that users can read the content on the website, in Google knowledge panels, and even with hypothetical voice interfaces in the future.

connective content models

Instead of slicing up related content across disparate content components, I’ve come to the realization that the best models are those that are semantic and also connect related content components ( such as a FAQ item’s question and answer pair ). A good content model connects content that should remain together so that multiple delivery channels can use it without needing to first put those pieces back together.

Consider creating an essay or article. The unity of an article’s parts determines its meaning and usefulness. Would one of the headings or paragraphs be meaningful on their own without the context of the full article? Our well-known design-system thinking on our project frequently led us to want to develop content models that would divide content into distinct chunks to fit the web-centric layout. This had a similar effect to an article that had had its headline removed. Because we were slicing content into standalone pieces based on layout, content that belonged together became difficult to manage and nearly impossible for multiple delivery channels to understand.

Let’s take a look at how connecting related content works in a real-world setting to illustrate. The client’s design team created a challenging layout for a software product page that included numerous tabs and sections. Our instincts were to follow suit with the content model. Shouldn’t we make adding multiple tabs in the future as simple and flexible as possible?

We felt like we needed a “tab section” content type because our design-system instincts allowed for the addition of multiple tab sections to a page because they were so well-versed. Each tab section would display various types of content. The software’s overview or specifications might be available in one tab. A list of resources might be provided by another tab.

Our inclination to break down the content model into “tab section” pieces would have led to an unnecessarily complex model and a cumbersome editing experience, and it would have also created content that couldn’t have been understood by additional delivery channels. How would a different system have been able to determine which “tab section” referred to a product’s specifications or resource list, for instance? Would that system have had to have used tab sections and content blocks to calculate this? This would have prevented the tabs from ever being rearranged, and it would have required adding logic to each other delivery channel to interpret the layout of the design system. Furthermore, if the customer were to have no longer wanted to display this content in a tab layout, it would have been tedious to migrate to a new content model to reflect the new page redesign.

Our customer had a breakthrough when we realized that for each tab, their customer had a specific purpose in mind: it would reveal specific information like the software product’s overview, specifications, related resources, and pricing. Once implementation began, our inclination to focus on what’s visual and familiar had obscured the intent of the designs. It wasn’t long after a little digging that the idea of tabs wasn’t applicable to the content model. What was important was the meaning of the content they were planning to display in the tabs.

In fact, the customer could have decided to display this content in a different way—without tabs—somewhere else. Based on the meaningful attributes the customer had desired to display on the web, we created content types for the software product. There were rich attributes like screenshots, software requirements, and feature lists as well as obvious semantic attributes like name and description. The software’s product information stayed together because it wasn’t sliced across separate components like “tab sections” that were derived from the content’s presentation. Any delivery channel, including those that follow, could comprehend and display this content.

Conclusion

In this omnichannel marketing project, we discovered that the best way to keep our content model on track was to ensure that it was semantic ( with type and attribute names that reflected the meaning of the content ) and that it kept content together that belonged together ( instead of fragmenting it ). These two ideas made it easier for us to shape the content model based on the design. Remember: If you’re developing a content model to support an omnichannel content strategy, or even if you just want to make sure Google and other interfaces understand your content, remember:

• A design system isn’t a content model. You should maintain the semantic value and contextual structure of the content strategy throughout the entire implementation process because team members might be drawn to conflate them and force your content model to resemble your design system. Without the use of a magic decoder ring, every delivery channel can now consume the content.
• If your team is struggling to make this transition, you can still reap some of the benefits by using Schema. structured data from org–based on your website. The benefit of search engine optimization is a compelling reason on its own, even if additional delivery channels aren’t on the horizon in the near future.
• Additionally, remind the team that decoupling the content model from the design will let them update the designs more easily because they won’t be held back by the cost of content migrations. They will be prepared for the upcoming big thing, and they will be able to create new designs without compromising the compatibility between the content and the design.

You’ll help your team understand these principles by firmly defending them in their efforts to give content the attention it deserves as both your most valuable resource and your most effective way to engage with your audience.

Here’s the key. To find stakeholders to determine high-risk assumptions and buried complexity, you must first convince them to do so so that they become just as motivated as you are to receive user-response. Generally, you need to make them think it’s their plan.

By bringing the group up around two straightforward issues, I’ll show you how to collectively introduce alignment and cracks in the group’s shared understanding in this article.

1. What are the things?
2. What are the associations between those things?

A cross between camera design and analysis

These two issues correlate to the first two methods of the ORCA approach, which may be your new best friend when it comes to reducing speculation. Delay, what’s ORCA? Glad you asked.

ORCA stands for Things, Relationships, CTAs, and Values, and it outlines a process for creating good object-oriented user experience. My style philosophy is based on object-oriented UX. ORCA is an iterative strategy for synthesizing person study into an elegant fundamental foundation to help monitor and conversation design. My work as a UX designer has become more creative, successful, fun, proper, and important thanks to OOUX and ORCA.

The ORCA approach has four incremental shells and a staggering fifteen steps. In each round we get more precision on our System, Rupees, Computer, and As.

ORCA is often described as a “garbage in, garbage out” procedure. To ensure that the observable design produced in the last round truly tests properly, the procedure needs to be fed by good research. The beginning of the ORCA approach, however, serves another function: it helps you buy the need for research if you don’t have a ton of study.

In other words, the ORCA process serves as a gauntlet between research and design. You can gracefully ride a killer whale from research to design with good research. But without good research, the process effectively spits you back into research and with a cache of specific open questions.

Getting in the same curiosity-boat

What gets us into trouble is not what we don’t know. It’s what we know for sure that just ain’t so.

Mark Twain

The first two steps of the ORCA process—Object Discovery and Relationship Discovery—shine a spotlight on the dark, dusty corners of your team’s misalignments and any inherent complexity that’s been swept under the rug. It starts to reveal what this timeless comic so skillfully demonstrates:

This is one reason why so many UX designers are frustrated in their job and why many projects fail. Every decision-maker is confident in their own mental picture, which is another reason why we frequently can’t sell research.

Once we expose hidden fuzzy patches in each picture and the differences between them all, the case for user research makes itself.

However, how we go about doing this is crucial. However much we might want to, we can’t just tell everyone,” YOU ARE WRONG”! Instead, we need to facilitate and guide our team members to self-identify holes in their picture. When stakeholders accept responsibility for their beliefs and understanding gaps, BAM! Suddenly, UX research is not such a hard sell, and everyone is aboard the same curiosity-boat.

Let’s say your users are physicians. And you have no idea how doctors use the system you are tasked with redesigning.

You might try to sell research by honestly saying:” We need to understand doctors better! What are the issues they face? How do they use the current app”? Here’s the issue with that, though. Those questions are vague, and the answers to them don’t feel acutely actionable.

Instead, you want your stakeholders themselves to ask super-specific questions. This conversation is more appropriate for facilitating than engaging in. Let’s listen in:

” Wait a sec, how frequently do doctors share patients?” Does a patient in this system have primary and secondary doctors”?

” Can a patient even have more than one primary doctor”?

Is it a “primary doctor” or just a “primary caregiver” ?Can’t that position be considered a nurse practitioner?

” No, caregivers are something else… That’s the patient’s family contacts, right”?

” So are caregivers in this redesign’s scope”?

” Yeah, because if a caregiver is present at an appointment, the doctor needs to note that. Like, tag the caregiver on the note… Or on the appointment”?

We are currently traveling somewhere. Do you see how powerful it can be getting stakeholders to debate these questions themselves? Here, the diabolical goal is to diplomatically and diplomatically shake their confidence.

When these kinds of questions bubble up collaboratively and come directly from the mouths of your stakeholders and decision-makers, suddenly, designing screens without knowing the answers to these questions seems incredibly risky, even silly.

If we create software without understanding the real-world information environment of our users, we will likely create software that does not align to the real-world information environment of our users. And most likely as a result, this software product will become more confusing, complicated, and unintuitive.

The two questions

But how do we approach these kinds of contentious inquiries diplomatically, effectively, collaboratively, and reliably?

We can do this by starting with those two big questions that align to the first two steps of the ORCA process:

1. What are the things?
2. What are the associations between those things?

In practice, getting to these answers is easier said than done. I’m going to demonstrate how these two straightforward questions can serve as the Object Definition Workshop’s starting point. During this workshop, these” seed” questions will blossom into dozens of specific questions and shine a spotlight on the need for more user research.

Work in preparation: Noun for foraging

In the next section, I’ll show you how to run an Object Definition Workshop with your stakeholders ( and entire cross-functional team, hopefully ). But first, you need to do some prep work.

In essence, look for nouns that are specific to the subject matter or industry of your project and use at least a few sources. I call this noun foraging.

Just a few excellent noun foraging sources can be found here:

• the product’s marketing site
• the product’s competitors ‘ marketing sites ( competitive analysis, anyone? )
• the already-existing product ( check the labels )!
• user interview transcripts
• notes from interviews with stakeholders or vision documents from stakeholders

Put your detective hat on, my dear Watson. Get resourceful and leverage what you have. Use those if all you have are a marketing website, some screenshots of the current legacy system, and access to customer service chat logs.

As you peruse these sources, watch for the nouns that are used over and over again, and start listing them ( preferably on blue sticky notes if you’ll be creating an object map later! ).

You’ll want to focus on nouns that might represent objects in your system. If you are having trouble determining if a noun might be object-worthy, remember the acronym SIP and test for:

1. Structure
2. Instances
3. Purpose

Consider, for instance, a library app. Is “book” an object?

Can you think of a few attributes for this potential object? Title, author, publish date … Yep, it has structure. Check!

What are some instances of this conceivable “book” object? Can you name a few? Check out The Alchemist, Ready Player One, and Everybody Poops!

Purpose: why is this object important to the users and business? Well, “book” is what our library client is providing to people and books are why people come to the library … Check, check, check!

Focus on capturing the nouns that have SIP as you are noun foraging. Avoid capturing components like dropdowns, checkboxes, and calendar pickers—your UX system is not your design system! Components are just the packaging for objects—they are a means to an end. No one is using your dropdown to play in your digital space! They are coming for the VALUABLE THINGS and what they can do with them. We are attempting to identify those things or objects.

Let’s say we work for a startup disrupting the email experience. This is how I’d start my noun foraging.

I’d like to take a look at my own email client, which turns out to be Gmail. I’d then look at Outlook and the new HEY email. I would examine Hotmail, Yahoo, and even Basecamp and other’email replacers’. I’d read some articles, reviews, and forum threads where people are complaining about email. While doing all this, I would look for and write down the nouns.

( Before moving on, feel free to explore this hypothetical product’s potential and then scroll down to see how closely our lists correspond. Just don’t get lost in your own emails! Remain with me!

Drumroll, please…

Here are a few nouns I came up with during my noun foraging:

• email message
• thread
• contact
• client
• rule/automation
• email address that is not a contact?
• contact organizations
• attachment
• Google doc file / other integrated file
• newsletter? ( HEY views this in a different way )
• saved responses and templates

Scan your list of nouns and pick out words that you are completely clueless about. It might be a client or automation in our email example. Do as much homework as you can before your session with stakeholders: google what’s googleable. But other terms might be so specific to the product or domain that you need to have a conversation about them.

Aside: Here are some real nouns that I needed to clarify while working on my own projects in the past.

• Record Locator
• Home Incentive
• Augmented Line Item
• Curriculum-Based Measurement Probe

A list of nouns that represent potential objects and a short list of nouns that need to be further defined are really all you need to prepare for the workshop session.

Facilitate an Object Definition Workshop

Noun foraging can be used as a starting point for your workshop; it can be done in concert. If you have five people in the room, pick five sources, assign one to every person, and give everyone ten minutes to find the objects within their source. When the time’s up, come together and find the overlap. Here, affinity mapping is your friend!

If your team is short on time and might be reluctant to do this kind of grunt work ( which is usually the case ) do your own noun foraging beforehand, but be prepared to show your work. I enjoy showing screenshots of documents and screens that have all the highlighted nouns. Bring the artifacts of your process, and start the workshop with a five-minute overview of your noun foraging journey.

HOT TIP: before jumping into the workshop, frame the conversation as a requirements-gathering session to help you better understand the scope and details of the system. You don’t need to tell them you‘re looking for gaps in the team’s understanding so that you can demonstrate the need for more user research; that will be our little secret. Instead, go into the session optimistically, as if your knowledgeable stakeholders and PMs and biz folks already have all the answers.

Let the whack-a-mole question then start.

1. What is this thing?

Want to have genuine fun? At the beginning of your session, ask stakeholders to privately write definitions for the handful of obscure nouns you might be uncertain about. Then, have everyone present their cards at once, and see if there are any (you will ) definitions. This is gold for exposing misalignment and starting great conversations.

As your discussion unfolds, capture any agreed-upon definitions. And when uncertainty sets in, quietly ( but clearly ) begin an “open questions” parking lot. � �

Here’s a fantastic follow-up after definitions solidify:

2. Do our users know what these things are? What is the name of this object?

Stakeholder 1: They probably call email clients “apps”. I’m not certain, though.

Stakeholder 2: Automations are often called “workflows”, I think. Or, maybe users think workflows are something different.

Ask the group if they can agree to use only that term as they go along if a more user-friendly term comes up. This way, the team can better align to the users ‘ language and mindset.

Okay, let’s get to the next part.

If you have two or more objects that seem to overlap in purpose, ask one of these questions:

3. Are these the same thing? Or are these different? What distinguishes them from one another if they are different?

You: Is a saved response the same as a template?

Stakeholder 1: Yes! Absolutely.

Stakeholder 2: I don’t think so… A saved response is text with links and variables, but a template is more about the look and feel, like default fonts, colors, and placeholder images.

Continually expand your expanding glossary of terms. And continue to capture areas of uncertainty in your “open questions” parking lot.

If you successfully determine that two similar things are, in fact, different, here’s your next follow-up question:

4. What’s the relationship between these objects?

You: Do saved responses and templates have any connection to each other?

Stakeholder 3: Yeah, a template can be applied to a saved response.

You, always with the follow-ups: When is the template applied to a saved response? Does that occur when the user is creating the saved response? Or when they apply the saved response to an email? What is the process behind that?

Listen. Capture uncertainty. When the number of “open questions” reaches a critical mass, pause to begin asking questions of groups or individuals. Some questions might be for the dev team ( hopefully at least one developer is in the room with you ). Someone who couldn’t make it to the workshop might have a question. And many questions will need to be labeled “user”.

Do you see how we are building up to our UXR sales pitch?

5. Is this object in scope?

Your next query makes it easier for the team to concentrate on what your users are most interested in. You can simply ask,” Are saved responses in scope for our first release”?, but I’ve got a better, more devious strategy.

By now, you should have a list of clearly defined objects. Ask participants to order these items either in small breakout groups or separately from the most important. Then, like you did with the definitions, have everyone reveal their sort order at once. Unsurprisingly, it’s not unusual for the VP to place something like” saved responses” at the top of the list while everyone else places it at the bottom. Try not to look too smug as you inevitably expose more misalignment.

I did this for a startup a few years ago. The three groups ‘ wildly different sorting patterns were displayed on the whiteboard.

The CEO sat back, examined it, and said,” This is why we haven’t been able to move forward in two years.”

Admittedly, it’s tragic to hear that, but as a professional, it feels pretty awesome to be the one who facilitated a watershed realization.

Once you have a good idea of in-scope, clearly defined things, this is when you move on to doing more relationship mapping.

6. Create a visual representation of the objects ‘ relationships

We’ve already done this before figuring out what two things might be different, but this time, we’ll ask the team about every possible connection. For each object, ask how it relates to all the other objects. In what ways are the objects connected? Pull out your trusted boxes-and-arrows technique to visualize all the connections. Here, we are connecting our objects with verbs. I prefer to use simple statements like “has a” and “has many.”

This system modeling activity brings up all sorts of new questions:

• Can attachments be included in a saved response?
• Can a saved response use a template? Can the user override a template in an email that has been saved as a template?
• Do users want to see all the emails they sent that included a particular attachment? For example,” show me all the emails I sent with ProfessionalImage. attached .jpg I’ve changed my professional photo and I want to alert everyone to update it”.

Effective responses might come directly from the workshop participants. Great! Capture that new shared understanding. However, as uncertainty arises, keep adding new questions to your expanding parking lot.

Light the fuse

You’ve set up the explosives strategically along the floodgates. Now you simply have to light the fuse and BOOM. Watch the buy-in for user research flooooow.

Have the group reflect on the list of open questions before the workshop ends. Make plans for getting answers internally, then focus on the questions that need to be brought before users.

Your final step is now. Take those questions you’ve compiled for user research and discuss the level of risk associated with NOT answering them. Ask, “if we design without an answer to this question, if we make up our own answer and we are wrong, how bad might that turn out”?

With this approach, we are making our decision-makers fight for user research because they themselves define questions as high-risk. Sorry, not sorry.

Your moment of truth is right now. With everyone in the room, ask for a reasonable budget of time and money to conduct 6–8 user interviews focused specifically on these questions.

HOT TIP: if you are new to UX research, please note that you’ll likely need to rephrase the questions that came up during the workshop before you present them to users. Make sure your questions are non-repeated and don’t force the user to choose any default responses.

Final words: Hold the screen design!

Seriously, if you’re ever going to design screens again, make sure you first address these fundamental inquiries: what are the objects and how do they relate?

I promise you this: if you can secure a shared understanding between the business, design, and development teams before you start designing screens, you will have less heartache and save more time and money, and ( it almost feels like a bonus at this point! ) users will be more receptive to what you put out into the world.

Before you begin building screens, I sincerely hope this will free up your time and money to spend on user education and clarifying what you are designing. If you find success using noun foraging and the Object Definition Workshop, there’s more where that came from in the rest of the ORCA process, which will help prevent even more late-in-the-game scope tugs-of-war and strategy pivots.

Wish you the best of luck! Now go sell research!