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I offer a single bit of advice to friends and family when they become new parents: When you start to think that you’ve got everything figured out, everything will change. Just as you start to get the hang of feedings, diapers, and regular naps, it’s time for solid food, potty training, and overnight sleeping. When you figure those out, it’s time for preschool and rare naps. The cycle goes on and on.

The same applies for those of us working in design and development these days. Having worked on the web for almost three decades at this point, I’ve seen the regular wax and wane of ideas, techniques, and technologies. Each time that we as developers and designers get into a regular rhythm, some new idea or technology comes along to shake things up and remake our world.

How we got here

I built my first website in the mid-’90s. Design and development on the web back then was a free-for-all, with few established norms. For any layout aside from a single column, we used table elements, often with empty cells containing a single pixel spacer GIF to add empty space. We styled text with numerous font tags, nesting the tags every time we wanted to vary the font style. And we had only three or four typefaces to choose from: Arial, Courier, or Times New Roman. When Verdana and Georgia came out in 1996, we rejoiced because our options had nearly doubled. The only safe colors to choose from were the 216 “web safe” colors known to work across platforms. The few interactive elements (like contact forms, guest books, and counters) were mostly powered by CGI scripts (predominantly written in Perl at the time). Achieving any kind of unique look involved a pile of hacks all the way down. Interaction was often limited to specific pages in a site.

The birth of web standards

At the turn of the century, a new cycle started. Crufty code littered with table layouts and font tags waned, and a push for web standards waxed. Newer technologies like CSS got more widespread adoption by browsers makers, developers, and designers. This shift toward standards didn’t happen accidentally or overnight. It took active engagement between the W3C and browser vendors and heavy evangelism from folks like the Web Standards Project to build standards. A List Apart and books like Designing with Web Standards by Jeffrey Zeldman played key roles in teaching developers and designers why standards are important, how to implement them, and how to sell them to their organizations. And approaches like progressive enhancement introduced the idea that content should be available for all browsers—with additional enhancements available for more advanced browsers. Meanwhile, sites like the CSS Zen Garden showcased just how powerful and versatile CSS can be when combined with a solid semantic HTML structure.

Server-side languages like PHP, Java, and .NET overtook Perl as the predominant back-end processors, and the cgi-bin was tossed in the trash bin. With these better server-side tools came the first era of web applications, starting with content-management systems (particularly in the blogging space with tools like Blogger, Grey Matter, Movable Type, and WordPress). In the mid-2000s, AJAX opened doors for asynchronous interaction between the front end and back end. Suddenly, pages could update their content without needing to reload. A crop of JavaScript frameworks like Prototype, YUI, and jQuery arose to help developers build more reliable client-side interaction across browsers that had wildly varying levels of standards support. Techniques like image replacement let crafty designers and developers display fonts of their choosing. And technologies like Flash made it possible to add animations, games, and even more interactivity.

These new technologies, standards, and techniques reinvigorated the industry in many ways. Web design flourished as designers and developers explored more diverse styles and layouts. But we still relied on tons of hacks. Early CSS was a huge improvement over table-based layouts when it came to basic layout and text styling, but its limitations at the time meant that designers and developers still relied heavily on images for complex shapes (such as rounded or angled corners) and tiled backgrounds for the appearance of full-length columns (among other hacks). Complicated layouts required all manner of nested floats or absolute positioning (or both). Flash and image replacement for custom fonts was a great start toward varying the typefaces from the big five, but both hacks introduced accessibility and performance problems. And JavaScript libraries made it easy for anyone to add a dash of interaction to pages, although at the cost of doubling or even quadrupling the download size of simple websites.

The web as software platform

The symbiosis between the front end and back end continued to improve, and that led to the current era of modern web applications. Between expanded server-side programming languages (which kept growing to include Ruby, Python, Go, and others) and newer front-end tools like React, Vue, and Angular, we could build fully capable software on the web. Alongside these tools came others, including collaborative version control, build automation, and shared package libraries. What was once primarily an environment for linked documents became a realm of infinite possibilities.

At the same time, mobile devices became more capable, and they gave us internet access in our pockets. Mobile apps and responsive design opened up opportunities for new interactions anywhere and any time.

This combination of capable mobile devices and powerful development tools contributed to the waxing of social media and other centralized tools for people to connect and consume. As it became easier and more common to connect with others directly on Twitter, Facebook, and even Slack, the desire for hosted personal sites waned. Social media offered connections on a global scale, with both the good and bad that that entails.

Want a much more extensive history of how we got here, with some other takes on ways that we can improve? Jeremy Keith wrote “Of Time and the Web.” Or check out the “Web Design History Timeline” at the Web Design Museum. Neal Agarwal also has a fun tour through “Internet Artifacts.”

Where we are now

In the last couple of years, it’s felt like we’ve begun to reach another major inflection point. As social-media platforms fracture and wane, there’s been a growing interest in owning our own content again. There are many different ways to make a website, from the tried-and-true classic of hosting plain HTML files to static site generators to content management systems of all flavors. The fracturing of social media also comes with a cost: we lose crucial infrastructure for discovery and connection. Webmentions, RSS, ActivityPub, and other tools of the IndieWeb can help with this, but they’re still relatively underimplemented and hard to use for the less nerdy. We can build amazing personal websites and add to them regularly, but without discovery and connection, it can sometimes feel like we may as well be shouting into the void.

Browser support for CSS, JavaScript, and other standards like web components has accelerated, especially through efforts like Interop. New technologies gain support across the board in a fraction of the time that they used to. I often learn about a new feature and check its browser support only to find that its coverage is already above 80 percent. Nowadays, the barrier to using newer techniques often isn’t browser support but simply the limits of how quickly designers and developers can learn what’s available and how to adopt it.

Today, with a few commands and a couple of lines of code, we can prototype almost any idea. All the tools that we now have available make it easier than ever to start something new. But the upfront cost that these frameworks may save in initial delivery eventually comes due as upgrading and maintaining them becomes a part of our technical debt.

If we rely on third-party frameworks, adopting new standards can sometimes take longer since we may have to wait for those frameworks to adopt those standards. These frameworks—which used to let us adopt new techniques sooner—have now become hindrances instead. These same frameworks often come with performance costs too, forcing users to wait for scripts to load before they can read or interact with pages. And when scripts fail (whether through poor code, network issues, or other environmental factors), there’s often no alternative, leaving users with blank or broken pages.

Where do we go from here?

Today’s hacks help to shape tomorrow’s standards. And there’s nothing inherently wrong with embracing hacks—for now—to move the present forward. Problems only arise when we’re unwilling to admit that they’re hacks or we hesitate to replace them. So what can we do to create the future we want for the web?

Build for the long haul. Optimize for performance, for accessibility, and for the user. Weigh the costs of those developer-friendly tools. They may make your job a little easier today, but how do they affect everything else? What’s the cost to users? To future developers? To standards adoption? Sometimes the convenience may be worth it. Sometimes it’s just a hack that you’ve grown accustomed to. And sometimes it’s holding you back from even better options.

Start from standards. Standards continue to evolve over time, but browsers have done a remarkably good job of continuing to support older standards. The same isn’t always true of third-party frameworks. Sites built with even the hackiest of HTML from the ’90s still work just fine today. The same can’t always be said of sites built with frameworks even after just a couple years.

Design with care. Whether your craft is code, pixels, or processes, consider the impacts of each decision. The convenience of many a modern tool comes at the cost of not always understanding the underlying decisions that have led to its design and not always considering the impact that those decisions can have. Rather than rushing headlong to “move fast and break things,” use the time saved by modern tools to consider more carefully and design with deliberation.

Always be learning. If you’re always learning, you’re also growing. Sometimes it may be hard to pinpoint what’s worth learning and what’s just today’s hack. You might end up focusing on something that won’t matter next year, even if you were to focus solely on learning standards. (Remember XHTML?) But constant learning opens up new connections in your brain, and the hacks that you learn one day may help to inform different experiments another day.

Play, experiment, and be weird! This web that we’ve built is the ultimate experiment. It’s the single largest human endeavor in history, and yet each of us can create our own pocket within it. Be courageous and try new things. Build a playground for ideas. Make goofy experiments in your own mad science lab. Start your own small business. There has never been a more empowering place to be creative, take risks, and explore what we’re capable of.

Share and amplify. As you experiment, play, and learn, share what’s worked for you. Write on your own website, post on whichever social media site you prefer, or shout it from a TikTok. Write something for A List Apart! But take the time to amplify others too: find new voices, learn from them, and share what they’ve taught you.

Go forth and make

As designers and developers for the web (and beyond), we’re responsible for building the future every day, whether that may take the shape of personal websites, social media tools used by billions, or anything in between. Let’s imbue our values into the things that we create, and let’s make the web a better place for everyone. Create that thing that only you are uniquely qualified to make. Then share it, make it better, make it again, or make something new. Learn. Make. Share. Grow. Rinse and repeat. Every time you think that you’ve mastered the web, everything will change.

Picture this. You’ve joined a squad at your company that’s designing new product features with an emphasis on automation or AI. Or your company has just implemented a personalization engine. Either way, you’re designing with data. Now what? When it comes to designing for personalization, there are many cautionary tales, no overnight successes, and few guides for the perplexed. 

Between the fantasy of getting it right and the fear of it going wrong—like when we encounter “persofails” in the vein of a company repeatedly imploring everyday consumers to buy additional toilet seats—the personalization gap is real. It’s an especially confounding place to be a digital professional without a map, a compass, or a plan.

For those of you venturing into personalization, there’s no Lonely Planet and few tour guides because effective personalization is so specific to each organization’s talent, technology, and market position. 

But you can ensure that your team has packed its bags sensibly.

There’s a DIY formula to increase your chances for success. At minimum, you’ll defuse your boss’s irrational exuberance. Before the party you’ll need to effectively prepare.

We call it prepersonalization.

Behind the music

Consider Spotify’s DJ feature, which debuted this past year.

We’re used to seeing the polished final result of a personalization feature. Before the year-end award, the making-of backstory, or the behind-the-scenes victory lap, a personalized feature had to be conceived, budgeted, and prioritized. Before any personalization feature goes live in your product or service, it lives amid a backlog of worthy ideas for expressing customer experiences more dynamically.

So how do you know where to place your personalization bets? How do you design consistent interactions that won’t trip up users or—worse—breed mistrust? We’ve found that for many budgeted programs to justify their ongoing investments, they first needed one or more workshops to convene key stakeholders and internal customers of the technology. Make yours count.

​From Big Tech to fledgling startups, we’ve seen the same evolution up close with our clients. In our experiences with working on small and large personalization efforts, a program’s ultimate track record—and its ability to weather tough questions, work steadily toward shared answers, and organize its design and technology efforts—turns on how effectively these prepersonalization activities play out.

Time and again, we’ve seen effective workshops separate future success stories from unsuccessful efforts, saving countless time, resources, and collective well-being in the process.

A personalization practice involves a multiyear effort of testing and feature development. It’s not a switch-flip moment in your tech stack. It’s best managed as a backlog that often evolves through three steps: 

1. customer experience optimization (CXO, also known as A/B testing or experimentation)
2. always-on automations (whether rules-based or machine-generated)
3. mature features or standalone product development (such as Spotify’s DJ experience)

This is why we created our progressive personalization framework and why we’re field-testing an accompanying deck of cards: we believe that there’s a base grammar, a set of “nouns and verbs” that your organization can use to design experiences that are customized, personalized, or automated. You won’t need these cards. But we strongly recommend that you create something similar, whether that might be digital or physical.

Set your kitchen timer

How long does it take to cook up a prepersonalization workshop? The surrounding assessment activities that we recommend including can (and often do) span weeks. For the core workshop, we recommend aiming for two to three days. Here’s a summary of our broader approach along with details on the essential first-day activities.

The full arc of the wider workshop is threefold:

1. Kickstart: This sets the terms of engagement as you focus on the opportunity as well as the readiness and drive of your team and your leadership. .
2. Plan your work: This is the heart of the card-based workshop activities where you specify a plan of attack and the scope of work.
3. Work your plan: This phase is all about creating a competitive environment for team participants to individually pitch their own pilots that each contain a proof-of-concept project, its business case, and its operating model.

Give yourself at least a day, split into two large time blocks, to power through a concentrated version of those first two phases.

Kickstart: Whet your appetite

We call the first lesson the “landscape of connected experience.” It explores the personalization possibilities in your organization. A connected experience, in our parlance, is any UX requiring the orchestration of multiple systems of record on the backend. This could be a content-management system combined with a marketing-automation platform. It could be a digital-asset manager combined with a customer-data platform.

Spark conversation by naming consumer examples and business-to-business examples of connected experience interactions that you admire, find familiar, or even dislike. This should cover a representative range of personalization patterns, including automated app-based interactions (such as onboarding sequences or wizards), notifications, and recommenders. We have a catalog of these in the cards. Here’s a list of 142 different interactions to jog your thinking.

This is all about setting the table. What are the possible paths for the practice in your organization? If you want a broader view, here’s a long-form primer and a strategic framework.

Assess each example that you discuss for its complexity and the level of effort that you estimate that it would take for your team to deliver that feature (or something similar). In our cards, we divide connected experiences into five levels: functions, features, experiences, complete products, and portfolios. Size your own build here. This will help to focus the conversation on the merits of ongoing investment as well as the gap between what you deliver today and what you want to deliver in the future.

Next, have your team plot each idea on the following 2×2 grid, which lays out the four enduring arguments for a personalized experience. This is critical because it emphasizes how personalization can not only help your external customers but also affect your own ways of working. It’s also a reminder (which is why we used the word argument earlier) of the broader effort beyond these tactical interventions.

Each team member should vote on where they see your product or service putting its emphasis. Naturally, you can’t prioritize all of them. The intention here is to flesh out how different departments may view their own upsides to the effort, which can vary from one to the next. Documenting your desired outcomes lets you know how the team internally aligns across representatives from different departments or functional areas.

The third and final kickstart activity is about naming your personalization gap. Is your customer journey well documented? Will data and privacy compliance be too big of a challenge? Do you have content metadata needs that you have to address? (We’re pretty sure that you do: it’s just a matter of recognizing the relative size of that need and its remedy.) In our cards, we’ve noted a number of program risks, including common team dispositions. Our Detractor card, for example, lists six stakeholder behaviors that hinder progress.

Effectively collaborating and managing expectations is critical to your success. Consider the potential barriers to your future progress. Press the participants to name specific steps to overcome or mitigate those barriers in your organization. As studies have shown, personalization efforts face many common barriers.

At this point, you’ve hopefully discussed sample interactions, emphasized a key area of benefit, and flagged key gaps? Good—you’re ready to continue.

Hit that test kitchen

Next, let’s look at what you’ll need to bring your personalization recipes to life. Personalization engines, which are robust software suites for automating and expressing dynamic content, can intimidate new customers. Their capabilities are sweeping and powerful, and they present broad options for how your organization can conduct its activities. This presents the question: Where do you begin when you’re configuring a connected experience?

What’s important here is to avoid treating the installed software like it were a dream kitchen from some fantasy remodeling project (as one of our client executives memorably put it). These software engines are more like test kitchens where your team can begin devising, tasting, and refining the snacks and meals that will become a part of your personalization program’s regularly evolving menu.

The ultimate menu of the prioritized backlog will come together over the course of the workshop. And creating “dishes” is the way that you’ll have individual team stakeholders construct personalized interactions that serve their needs or the needs of others.

The dishes will come from recipes, and those recipes have set ingredients.

Verify your ingredients

Like a good product manager, you’ll make sure—andyou’ll validate with the right stakeholders present—that you have all the ingredients on hand to cook up your desired interaction (or that you can work out what needs to be added to your pantry). These ingredients include the audience that you’re targeting, content and design elements, the context for the interaction, and your measure for how it’ll come together. 

This isn’t just about discovering requirements. Documenting your personalizations as a series of if-then statements lets the team: 

1. compare findings toward a unified approach for developing features, not unlike when artists paint with the same palette; 
2. specify a consistent set of interactions that users find uniform or familiar; 
3. and develop parity across performance measurements and key performance indicators too. 

This helps you streamline your designs and your technical efforts while you deliver a shared palette of core motifs of your personalized or automated experience.

Compose your recipe

What ingredients are important to you? Think of a who-what-when-why construct: 

• Who are your key audience segments or groups?
• What kind of content will you give them, in what design elements, and under what circumstances?
• And for which business and user benefits?

We first developed these cards and card categories five years ago. We regularly play-test their fit with conference audiences and clients. And we still encounter new possibilities. But they all follow an underlying who-what-when-why logic.

Here are three examples for a subscription-based reading app, which you can generally follow along with right to left in the cards in the accompanying photo below. 

1. Nurture personalization: When a guest or an unknown visitor interacts with  a product title, a banner or alert bar appears that makes it easier for them to encounter a related title they may want to read, saving them time.
2. Welcome automation: When there’s a newly registered user, an email is generated to call out the breadth of the content catalog and to make them a happier subscriber.
3. Winback automation: Before their subscription lapses or after a recent failed renewal, a user is sent an email that gives them a promotional offer to suggest that they reconsider renewing or to remind them to renew.

A useful preworkshop activity may be to think through a first draft of what these cards might be for your organization, although we’ve also found that this process sometimes flows best through cocreating the recipes themselves. Start with a set of blank cards, and begin labeling and grouping them through the design process, eventually distilling them to a refined subset of highly useful candidate cards.

You can think of the later stages of the workshop as moving from recipes toward a cookbook in focus—like a more nuanced customer-journey mapping. Individual “cooks” will pitch their recipes to the team, using a common jobs-to-be-done format so that measurability and results are baked in, and from there, the resulting collection will be prioritized for finished design and delivery to production.

Better kitchens require better architecture

Simplifying a customer experience is a complicated effort for those who are inside delivering it. Beware anyone who says otherwise. With that being said,  “Complicated problems can be hard to solve, but they are addressable with rules and recipes.”

When personalization becomes a laugh line, it’s because a team is overfitting: they aren’t designing with their best data. Like a sparse pantry, every organization has metadata debt to go along with its technical debt, and this creates a drag on personalization effectiveness. Your AI’s output quality, for example, is indeed limited by your IA. Spotify’s poster-child prowess today was unfathomable before they acquired a seemingly modest metadata startup that now powers its underlying information architecture.

You can definitely stand the heat…

Personalization technology opens a doorway into a confounding ocean of possible designs. Only a disciplined and highly collaborative approach will bring about the necessary focus and intention to succeed. So banish the dream kitchen. Instead, hit the test kitchen to save time, preserve job satisfaction and security, and safely dispense with the fanciful ideas that originate upstairs of the doers in your organization. There are meals to serve and mouths to feed.

This workshop framework gives you a fighting shot at lasting success as well as sound beginnings. Wiring up your information layer isn’t an overnight affair. But if you use the same cookbook and shared recipes, you’ll have solid footing for success. We designed these activities to make your organization’s needs concrete and clear, long before the hazards pile up.

While there are associated costs toward investing in this kind of technology and product design, your ability to size up and confront your unique situation and your digital capabilities is time well spent. Don’t squander it. The proof, as they say, is in the pudding.

Ever since I was a boy, I’ve been fascinated with movies. I loved the characters and the excitement—but most of all the stories. I wanted to be an actor. And I believed that I’d get to do the things that Indiana Jones did and go on exciting adventures. I even dreamed up ideas for movies that my friends and I could make and star in. But they never went any further. I did, however, end up working in user experience (UX). Now, I realize that there’s an element of theater to UX—I hadn’t really considered it before, but user research is storytelling. And to get the most out of user research, you need to tell a good story where you bring stakeholders—the product team and decision makers—along and get them interested in learning more.

Think of your favorite movie. More than likely it follows a three-act structure that’s commonly seen in storytelling: the setup, the conflict, and the resolution. The first act shows what exists today, and it helps you get to know the characters and the challenges and problems that they face. Act two introduces the conflict, where the action is. Here, problems grow or get worse. And the third and final act is the resolution. This is where the issues are resolved and the characters learn and change. I believe that this structure is also a great way to think about user research, and I think that it can be especially helpful in explaining user research to others.

Use storytelling as a structure to do research

It’s sad to say, but many have come to see research as being expendable. If budgets or timelines are tight, research tends to be one of the first things to go. Instead of investing in research, some product managers rely on designers or—worse—their own opinion to make the “right” choices for users based on their experience or accepted best practices. That may get teams some of the way, but that approach can so easily miss out on solving users’ real problems. To remain user-centered, this is something we should avoid. User research elevates design. It keeps it on track, pointing to problems and opportunities. Being aware of the issues with your product and reacting to them can help you stay ahead of your competitors.

In the three-act structure, each act corresponds to a part of the process, and each part is critical to telling the whole story. Let’s look at the different acts and how they align with user research.

Act one: setup

The setup is all about understanding the background, and that’s where foundational research comes in. Foundational research (also called generative, discovery, or initial research) helps you understand users and identify their problems. You’re learning about what exists today, the challenges users have, and how the challenges affect them—just like in the movies. To do foundational research, you can conduct contextual inquiries or diary studies (or both!), which can help you start to identify problems as well as opportunities. It doesn’t need to be a huge investment in time or money.

Erika Hall writes about minimum viable ethnography, which can be as simple as spending 15 minutes with a user and asking them one thing: “‘Walk me through your day yesterday.’ That’s it. Present that one request. Shut up and listen to them for 15 minutes. Do your damndest to keep yourself and your interests out of it. Bam, you’re doing ethnography.” According to Hall, “[This] will probably prove quite illuminating. In the highly unlikely case that you didn’t learn anything new or useful, carry on with enhanced confidence in your direction.”  

This makes total sense to me. And I love that this makes user research so accessible. You don’t need to prepare a lot of documentation; you can just recruit participants and do it! This can yield a wealth of information about your users, and it’ll help you better understand them and what’s going on in their lives. That’s really what act one is all about: understanding where users are coming from. 

Jared Spool talks about the importance of foundational research and how it should form the bulk of your research. If you can draw from any additional user data that you can get your hands on, such as surveys or analytics, that can supplement what you’ve heard in the foundational studies or even point to areas that need further investigation. Together, all this data paints a clearer picture of the state of things and all its shortcomings. And that’s the beginning of a compelling story. It’s the point in the plot where you realize that the main characters—or the users in this case—are facing challenges that they need to overcome. Like in the movies, this is where you start to build empathy for the characters and root for them to succeed. And hopefully stakeholders are now doing the same. Their sympathy may be with their business, which could be losing money because users can’t complete certain tasks. Or maybe they do empathize with users’ struggles. Either way, act one is your initial hook to get the stakeholders interested and invested.

Once stakeholders begin to understand the value of foundational research, that can open doors to more opportunities that involve users in the decision-making process. And that can guide product teams toward being more user-centered. This benefits everyone—users, the product, and stakeholders. It’s like winning an Oscar in movie terms—it often leads to your product being well received and successful. And this can be an incentive for stakeholders to repeat this process with other products. Storytelling is the key to this process, and knowing how to tell a good story is the only way to get stakeholders to really care about doing more research. 

This brings us to act two, where you iteratively evaluate a design or concept to see whether it addresses the issues.

Act two: conflict

Act two is all about digging deeper into the problems that you identified in act one. This usually involves directional research, such as usability tests, where you assess a potential solution (such as a design) to see whether it addresses the issues that you found. The issues could include unmet needs or problems with a flow or process that’s tripping users up. Like act two in a movie, more issues will crop up along the way. It’s here that you learn more about the characters as they grow and develop through this act. 

Usability tests should typically include around five participants according to Jakob Nielsen, who found that that number of users can usually identify most of the problems: “As you add more and more users, you learn less and less because you will keep seeing the same things again and again… After the fifth user, you are wasting your time by observing the same findings repeatedly but not learning much new.” 

There are parallels with storytelling here too; if you try to tell a story with too many characters, the plot may get lost. Having fewer participants means that each user’s struggles will be more memorable and easier to relay to other stakeholders when talking about the research. This can help convey the issues that need to be addressed while also highlighting the value of doing the research in the first place.

Researchers have run usability tests in person for decades, but you can also conduct usability tests remotely using tools like Microsoft Teams, Zoom, or other teleconferencing software. This approach has become increasingly popular since the beginning of the pandemic, and it works well. You can think of in-person usability tests like going to a play and remote sessions as more like watching a movie. There are advantages and disadvantages to each. In-person usability research is a much richer experience. Stakeholders can experience the sessions with other stakeholders. You also get real-time reactions—including surprise, agreement, disagreement, and discussions about what they’re seeing. Much like going to a play, where audiences get to take in the stage, the costumes, the lighting, and the actors’ interactions, in-person research lets you see users up close, including their body language, how they interact with the moderator, and how the scene is set up.

If in-person usability testing is like watching a play—staged and controlled—then conducting usability testing in the field is like immersive theater where any two sessions might be very different from one another. You can take usability testing into the field by creating a replica of the space where users interact with the product and then conduct your research there. Or you can go out to meet users at their location to do your research. With either option, you get to see how things work in context, things come up that wouldn’t have in a lab environment—and conversion can shift in entirely different directions. As researchers, you have less control over how these sessions go, but this can sometimes help you understand users even better. Meeting users where they are can provide clues to the external forces that could be affecting how they use your product. In-person usability tests provide another level of detail that’s often missing from remote usability tests. 

That’s not to say that the “movies”—remote sessions—aren’t a good option. Remote sessions can reach a wider audience. They allow a lot more stakeholders to be involved in the research and to see what’s going on. And they open the doors to a much wider geographical pool of users. But with any remote session there is the potential of time wasted if participants can’t log in or get their microphone working. 

The benefit of usability testing, whether remote or in person, is that you get to see real users interact with the designs in real time, and you can ask them questions to understand their thought processes and grasp of the solution. This can help you not only identify problems but also glean why they’re problems in the first place. Furthermore, you can test hypotheses and gauge whether your thinking is correct. By the end of the sessions, you’ll have a much clearer picture of how usable the designs are and whether they work for their intended purposes. Act two is the heart of the story—where the excitement is—but there can be surprises too. This is equally true of usability tests. Often, participants will say unexpected things, which change the way that you look at things—and these twists in the story can move things in new directions. 

Unfortunately, user research is sometimes seen as expendable. And too often usability testing is the only research process that some stakeholders think that they ever need. In fact, if the designs that you’re evaluating in the usability test aren’t grounded in a solid understanding of your users (foundational research), there’s not much to be gained by doing usability testing in the first place. That’s because you’re narrowing the focus of what you’re getting feedback on, without understanding the users’ needs. As a result, there’s no way of knowing whether the designs might solve a problem that users have. It’s only feedback on a particular design in the context of a usability test.  

On the other hand, if you only do foundational research, while you might have set out to solve the right problem, you won’t know whether the thing that you’re building will actually solve that. This illustrates the importance of doing both foundational and directional research. 

In act two, stakeholders will—hopefully—get to watch the story unfold in the user sessions, which creates the conflict and tension in the current design by surfacing their highs and lows. And in turn, this can help motivate stakeholders to address the issues that come up.

Act three: resolution

While the first two acts are about understanding the background and the tensions that can propel stakeholders into action, the third part is about resolving the problems from the first two acts. While it’s important to have an audience for the first two acts, it’s crucial that they stick around for the final act. That means the whole product team, including developers, UX practitioners, business analysts, delivery managers, product managers, and any other stakeholders that have a say in the next steps. It allows the whole team to hear users’ feedback together, ask questions, and discuss what’s possible within the project’s constraints. And it lets the UX research and design teams clarify, suggest alternatives, or give more context behind their decisions. So you can get everyone on the same page and get agreement on the way forward.

This act is mostly told in voiceover with some audience participation. The researcher is the narrator, who paints a picture of the issues and what the future of the product could look like given the things that the team has learned. They give the stakeholders their recommendations and their guidance on creating this vision.

Nancy Duarte in the Harvard Business Review offers an approach to structuring presentations that follow a persuasive story. “The most effective presenters use the same techniques as great storytellers: By reminding people of the status quo and then revealing the path to a better way, they set up a conflict that needs to be resolved,” writes Duarte. “That tension helps them persuade the audience to adopt a new mindset or behave differently.”

This type of structure aligns well with research results, and particularly results from usability tests. It provides evidence for “what is”—the problems that you’ve identified. And “what could be”—your recommendations on how to address them. And so on and so forth.

You can reinforce your recommendations with examples of things that competitors are doing that could address these issues or with examples where competitors are gaining an edge. Or they can be visual, like quick mockups of how a new design could look that solves a problem. These can help generate conversation and momentum. And this continues until the end of the session when you’ve wrapped everything up in the conclusion by summarizing the main issues and suggesting a way forward. This is the part where you reiterate the main themes or problems and what they mean for the product—the denouement of the story. This stage gives stakeholders the next steps and hopefully the momentum to take those steps!

While we are nearly at the end of this story, let’s reflect on the idea that user research is storytelling. All the elements of a good story are there in the three-act structure of user research: 

• Act one: You meet the protagonists (the users) and the antagonists (the problems affecting users). This is the beginning of the plot. In act one, researchers might use methods including contextual inquiry, ethnography, diary studies, surveys, and analytics. The output of these methods can include personas, empathy maps, user journeys, and analytics dashboards.

• Act two: Next, there’s character development. There’s conflict and tension as the protagonists encounter problems and challenges, which they must overcome. In act two, researchers might use methods including usability testing, competitive benchmarking, and heuristics evaluation. The output of these can include usability findings reports, UX strategy documents, usability guidelines, and best practices.

• Act three: The protagonists triumph and you see what a better future looks like. In act three, researchers may use methods including presentation decks, storytelling, and digital media. The output of these can be: presentation decks, video clips, audio clips, and pictures. 

The researcher has multiple roles: they’re the storyteller, the director, and the producer. The participants have a small role, but they are significant characters (in the research). And the stakeholders are the audience. But the most important thing is to get the story right and to use storytelling to tell users’ stories through research. By the end, the stakeholders should walk away with a purpose and an eagerness to resolve the product’s ills. 

So the next time that you’re planning research with clients or you’re speaking to stakeholders about research that you’ve done, think about how you can weave in some storytelling. Ultimately, user research is a win-win for everyone, and you just need to get stakeholders interested in how the story ends.

I’m not sure when I first heard this quote, but it’s something that has stayed with me over the years. How do you create services for situations you can’t imagine? Or design products that work on devices yet to be invented?

Flash, Photoshop, and responsive design

When I first started designing websites, my go-to software was Photoshop. I created a 960px canvas and set about creating a layout that I would later drop content in. The development phase was about attaining pixel-perfect accuracy using fixed widths, fixed heights, and absolute positioning.

Ethan Marcotte’s talk at An Event Apart and subsequent article “Responsive Web Design” in A List Apart in 2010 changed all this. I was sold on responsive design as soon as I heard about it, but I was also terrified. The pixel-perfect designs full of magic numbers that I had previously prided myself on producing were no longer good enough.

The fear wasn’t helped by my first experience with responsive design. My first project was to take an existing fixed-width website and make it responsive. What I learned the hard way was that you can’t just add responsiveness at the end of a project. To create fluid layouts, you need to plan throughout the design phase.

A new way to design

Designing responsive or fluid sites has always been about removing limitations, producing content that can be viewed on any device. It relies on the use of percentage-based layouts, which I initially achieved with native CSS and utility classes:

.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 blocks of code and move back to more semantic markup:

.logo {
  @include colSpan(6);
}

.search {
  @include colSpan(3);
}

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

Media queries

The second ingredient for responsive design is media queries. Without them, content would shrink to fit the available space regardless of whether that content remained readable (The exact opposite problem occurred with the introduction of a mobile-first approach).

Media queries 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.

Row markup was a staple of early responsive design, present in all the widely used frameworks 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 problem arose as I moved from a design agency building websites for small- to medium-sized businesses, to larger in-house teams where I worked across a suite of related sites. In those roles I started to work much more with reusable components. 

Our reliance on media queries resulted in components that were tied to common viewport sizes. If the goal of component libraries is reuse, then this is a real problem because you can only use these components if the devices you’re designing for correspond to the viewport sizes used in the pattern library—in the process not really hitting that “devices that don’t yet exist”  goal.

Then there’s the problem of space. Media queries 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.

fr units 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 queries 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 queries 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.

In the 1950s, many in the elite running community had begun to believe it wasn’t possible to run a mile in less than four minutes. Runners had been attempting it since the late 19th century and were beginning to draw the conclusion that the human body simply wasn’t built for the task. 

But on May 6, 1956, Roger Bannister took everyone by surprise. It was a cold, wet day in Oxford, England—conditions no one expected to lend themselves to record-setting—and yet Bannister did just that, running a mile in 3:59.4 and becoming the first person in the record books to run a mile in under four minutes. 

This shift in the benchmark had profound effects; the world now knew that the four-minute mile was possible. Bannister’s record lasted only forty-six days, when it was snatched away by Australian runner John Landy. Then a year later, three runners all beat the four-minute barrier together in the same race. Since then, over 1,400 runners have officially run a mile in under four minutes; the current record is 3:43.13, held by Moroccan athlete Hicham El Guerrouj.

We achieve far more when we believe that something is possible, and we will believe it’s possible only when we see someone else has already done it—and as with human running speed, so it is with what we believe are the hard limits for how a website needs to perform.

Establishing standards for a sustainable web

In most major industries, the key metrics of environmental performance are fairly well established, such as miles per gallon for cars or energy per square meter for homes. The tools and methods for calculating those metrics are standardized as well, which keeps everyone on the same page when doing environmental assessments. In the world of websites and apps, however, we aren’t held to any particular environmental standards, and only recently have gained the tools and methods we need to even make an environmental assessment.

The primary goal in sustainable web design is to reduce carbon emissions. However, it’s almost impossible to actually measure the amount of CO₂ produced by a web product. We can’t measure the fumes coming out of the exhaust pipes on our laptops. The emissions of our websites are far away, out of sight and out of mind, coming out of power stations burning coal and gas. We have no way to trace the electrons from a website or app back to the power station where the electricity is being generated and actually know the exact amount of greenhouse gas produced. So what do we do? 

If we can’t measure the actual carbon emissions, then we need to find what we can measure. The primary factors that could be used as indicators of carbon emissions are:

1. Data transfer 
2. Carbon intensity of electricity

Let’s take a look at how we can use these metrics to quantify the energy consumption, and in turn the carbon footprint, of the websites and web apps we create.

Data transfer

Most researchers use kilowatt-hours per gigabyte (kWh/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 provides a great reference point for energy consumption and carbon emissions. As a rule of thumb, the more data transferred, the more energy used in the data center, telecoms networks, and end user devices.

For web pages, data transfer for a single visit can be most easily estimated by measuring the page weight, meaning the transfer size of the page in kilobytes the first time someone visits the page. It’s fairly easy to measure using the developer tools in any modern web browser. Often your web hosting account will include statistics for the total data transfer of any web application (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. 

Reducing page weight requires a large scope. 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). Roughly half of this data transfer is image files, making images the single biggest source of carbon emissions on the average website. 

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

You might be familiar with the concept of performance budgeting as a way of focusing a project team on creating 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. Much like speed limits while driving, performance budgets are upper limits rather than vague suggestions, so the goal should always be to come in under budget.

Designing for fast performance does often lead to reduced data transfer and emissions, but it isn’t always the case. Web performance is often more about the subjective perception of load times than it is about the true efficiency of the underlying system, whereas page weight and transfer size are more objective measures and more reliable benchmarks for sustainable web design. 

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 benchmark page weight against competitors or the old version of the website we’re replacing. 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, then we could also start looking at the transfer size of our web pages for repeat visitors. 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 example, visitors who load the same page multiple times will likely have a high percentage of the files cached in their browser, meaning they don’t need to transfer all of 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. Measuring transfer size at this next level of detail can help us learn even more about how we can optimize efficiency for users who regularly visit our pages, and enable us to set page weight budgets for additional scenarios beyond the first visit.

Page weight budgets are easy to track throughout a design and development process. Although they don’t actually tell us carbon emission and energy consumption analytics directly, they give us a clear indication of efficiency relative 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, reduced data transfer translates to energy efficiency, a key factor to reducing carbon emissions of web products. 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. But as we’ll see next, since all web products demand some power, it’s important to consider the source of that electricity, too.

Carbon intensity of electricity

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. Carbon intensity is a term used to define the grams of CO₂ produced for every kilowatt-hour of electricity (gCO₂/kWh). This varies widely, with renewable energy sources and nuclear having an extremely low carbon intensity of less than 10 gCO₂/kWh (even when factoring in their construction); whereas fossil fuels have very high carbon intensity of approximately 200–400 gCO₂/kWh. 

Most electricity comes from national or state grids, where energy from a variety of different sources is mixed together with varying levels of carbon intensity. 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.

We don’t have control over the full energy supply of web services, but we do have some control over where we host our projects. 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 this user-contributed data, and a glance at their map shows how, for example, choosing a data center in France will have significantly lower carbon emissions than a data center in the Netherlands (Fig 2.3).

That said, we don’t want to locate our servers too far away from our users; it takes energy to transmit data through the telecom’s networks, and the further the data travels, the more energy is consumed. 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.

Using the distance itself as a benchmark, we can use website analytics to identify 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. 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 example, if a website is hosted in London but the primary user base is on the West Coast of the USA, then we could look up the distance from London to San Francisco, which is 5,300 miles. That’s a long way! We can see that hosting it somewhere in North America, ideally on the West Coast, would significantly reduce the distance and thus the energy used to transmit the data. In addition, locating our servers closer to our visitors helps reduce latency and delivers better user experience, so it’s a win-win.

Converting it back 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 does this by measuring the data transfer over the wire when loading a web page, calculating the amount of electricity associated, and then converting that into a figure for CO₂ (Fig 2.4). It also factors in whether or not the web hosting is powered by renewable energy.

If you want to take it to the next level and tailor the data more accurately to the unique aspects of your project, the Energy and Emissions Worksheet accompanying this book shows you how.

With the ability to calculate carbon emissions for our projects, we could actually take a page weight budget one step further and set 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. Translating that into carbon adds a layer of abstraction that isn’t as intuitive—but carbon budgets do focus our minds on the primary thing we’re trying to reduce, and support the core objective of sustainable web design: reducing carbon emissions.

Browser Energy

Data transfer 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. As front-end web technologies become more advanced, the computational load is increasingly moving from the data center to users’ devices, whether they be phones, tablets, laptops, desktops, or even smart TVs. Modern web browsers allow us to implement more complex styling and animation on the fly using CSS and JavaScript. Furthermore, JavaScript libraries such as Angular and React allow us to create applications where the “thinking” work is done partly or entirely 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 computation in the user’s web browser means more energy used by their devices. This has implications not just environmentally, but also for user experience and inclusivity. Applications that put a heavy processing load on the user’s device can inadvertently exclude users with older, slower devices and cause batteries on phones and laptops to drain faster. 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 isn’t just bad for the environment, but it puts a disproportionate financial burden on the poorest in society.

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. One tool we do currently have is the Energy Impact monitor inside the developer console of the Safari browser (Fig 2.5).

You know when you load a website and your computer’s cooling fans start spinning so frantically you think it might actually take off? That’s essentially what this tool is measuring. 

It shows us the percentage of CPU used and the duration of CPU usage when loading the web page, and uses these figures to generate an energy impact rating. 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.