Webmasters Gallery

The text-box-trim and text-box-edge properties in CSS enable developers to trim specifiable amounts of the whitespace that appear above the first formatted line of text and below the last formatted line of text in a text box, making the text box vertically larger than the content within.

This whitespace is called leading, and it appears above and below (so it’s two half-leadings, actually) all lines of text to make the text more readable. However, we only want it to appear in between lines of text, right? We don’t want it to appear along the over or under edges of our text boxes, because then it interferes with our margins, paddings, gaps, and other spacings.

As an example, if we implement a 50px margin but then the leading adds another 37px, we’d end up with a grand total of 87px of space. Then we’d need to adjust the margin to 13px in order to make the space 50px in practice.

As a design systems person, I try to maintain as much consistency as possible and use very little markup whenever possible, which enables me to use the adjacent-sibling combinator (+) to create blanket rules like this:

/* Whenever <element> is followed by <h1> */
<element> + h1 {
  margin-bottom: 13px; /* instead of margin-bottom: 50px; */
}

This approach is still a headache since you still have to do the math (albeit less of it). But with the text-box-trim and text-box-edge properties, 50px as defined by CSS will mean 50px visually:

Disclaimer: text-box-trim and text-box-edge are only accessible via a feature flag in Chrome 128+ and Safari 16.4+, as well as Safari Technology Preview without a feature flag. See Caniuse for the latest browser support.

Start with text-box-trim

text-box-trim is the CSS property that basically activates text box trimming. It doesn’t really have a use beyond that, but it does provide us with the option to trim from just the start, just the end, both the start and end, or none:

text-box-trim: trim-start;
text-box-trim: trim-end;
text-box-trim: trim-both;
text-box-trim: none;

Note: In older web browsers, you might need to use the older start/end/both values in place of the newer trim-start/trim-end/trim-both values, respectively. In even older web browsers, you might need to use top/bottom/both. There’s no reference for this, unfortunately, so you’ll just have to see what works.

Now, where do you want to trim from?

You’re probably wondering what I mean by that. Well, consider that a typographic letter has multiple peaks.

There’s the x-height, which marks the top of the letter “x” and other lowercase characters (not including ascenders or overshoots), the cap height, which marks the top of uppercase characters (again, not including ascenders or overshoots), and the alphabetic baseline, which marks the bottom of most letters (not including descenders or overshoots). Then of course there’s the ascender height and descender height too.

You can trim the whitespace between the x-height, cap height, or ascender height and the “over” edge of the text box (this is where overlines begin), and also the white space between the alphabetic baseline or descender height and the “under” edge (where underlines begin if text-underline-position is set to under).

Don’t trim anything

text-box-edge: leading means to include all of the leading; simply don’t trim anything. This has the same effect as text-box-trim: none or forgoing text-box-trim and text-box-edge entirely. You could also restrict under-edge trimming with text-box-trim: trim-start or over edge trimming with text-box-trim: trim-end. Yep, there are quite a few ways to not even do this thing at all!

Newer web browsers have deviated from the CSSWG specification working drafts by removing the leading value and replacing it with auto, despite the “Do not ship (yet)” warning (*shrug*).

Naturally, text-box-edge accepts two values (an instruction regarding the over edge, then an instruction regarding the under edge). However, auto must be used solo.

text-box-edge: auto; /* Works */
text-box-edge: ex auto; /* Doesn't work */
text-box-edge: auto alphabetic; /* Doesn't work */

I could explain all the scenarios in which auto would work, but none of them are useful. I think all we want from auto is to be able to set the over or under edge to auto and the other edge to something else, but this is the only thing that it doesn’t do. This is a problem, but we’ll dive into that shortly. 

Trim above the ascenders and/or below the descenders

The text value will trim above the ascenders if used as the first value and below the descenders if used as the second value and is also the default value if you fail to declare the second value. (I think you’d want it to be auto, but it won’t be.)

text-box-edge: ex text; /* Valid */
text-box-edge: ex; /* Computed as `text-box-edge: ex text;` */
text-box-edge: text alphabetic; /* Valid */
text-box-edge: text text; /* Valid */
text-box-edge: text; /* Computed as `text-box-edge: text text;` */

It’s worth noting that ascender and descender height metrics come from the fonts themselves (or not!), so text can be quite finicky. For example, with the Arial font, the ascender height includes diacritics and the descender height includes descenders, whereas with the Fraunces font, the descender height includes diacritics and I don’t know what the ascender height includes. For this reason, there’s talk about renaming text to from-font.

Trim above the cap height only

To trim above the cap height:

text-box-edge: cap; /* Computed as text-box-edge: cap text; */

Remember, undeclared values default to text, not auto (as demonstrated above). Therefore, to opt out of trimming the under edge, you’d need to use trim-start instead of trim-both:

text-box-trim: trim-start; /* Not text-box-trim: trim-both; */
text-box-edge: cap; /* Not computed as text-box-edge: cap text; */

Trim above the cap height and below the alphabetic baseline

To trim above the cap height and below the alphabetic baseline:

text-box-trim: trim-both;
text-box-edge: cap alphabetic;

By the way, the “Cap height to baseline” option of Figma’s “Vertical trim” setting does exactly this. However, its Dev Mode produces CSS code with outdated property names (leading-trim and text-edge) and outdated values (top and bottom).

Trim above the x-height only

To trim above the x-height only:

text-box-trim: trim-start;
text-box-edge: ex;

Trim above the x-height and below the alphabetic baseline

To trim above the x-height and below the alphabetic baseline:

text-box-trim: trim-both;
text-box-edge: ex alphabetic;

Trim below the alphabetic baseline only

To trim below the alphabetic baseline only, the following won’t work (things were going so well for a moment, weren’t they?):

text-box-trim: trim-end;
text-box-edge: alphabetic;

This is because the first value is always the mandatory over-edge value whereas the second value is an optional under-edge value. This means that alphabetic isn’t a valid over-edge value, even though the inclusion of trim-end suggests that we won’t be providing one. Complaints about verbosity aside, the correct syntax would have you declare any over-edge value even though you’d effectively cancel it out with trim-end:

text-box-trim: trim-end;
text-box-edge: [any over edge value] alphabetic;

What about ideographic glyphs?

It’s difficult to know how web browsers will trim ideographic glyphs until they do, but you can read all about it in the spec. In theory, you’d want to use the ideographic-ink value for trimming and the ideographic value for no trimming, both of which aren’t unsupported yet:

text-box-edge: ideographic; /* No trim */
text-box-edge: ideographic-ink; /* Trim */
text-box-edge: ideographic-ink ideographic; /* Top trim */
text-box-edge: ideographic ideographic-ink; /* Bottom trim */

text-box, the shorthand property

If you’re not keen on the verbosity of text box trimming, there’s a shorthand text-box property that makes it somewhat inconsequential. All the same rules apply.

/* Syntax */
text-box: [text-box-trim] [text-box-edge (over)] [text-box-edge (under)]?

/* Example */
text-box: trim-both cap alphabetic;

Final thoughts

At first glance, text-box-trim and text-box-edge might not seem all that interesting, but they do make spacing elements a heck of a lot simpler.

Is the current proposal the best way to handle text box trimming though? Personally, I don’t think so. I think text-box-trim-start and text-box-trim-end would make a lot more sense, with text-box-trim being used as the shorthand property and text-box-edge not being used at all, but I’d settle for some simplification and/or consistent practices. What do you think?

There are some other concerns too. For example, should there be an option to include underlines, overlines, hanging punctuation marks, or diacritics? I’m going to say yes, especially if you’re using text-underline-position: under or a particularly thick text-decoration-thickness, as they can make the spacing between elements appear smaller.

---

Two CSS Properties for Trimming Text Box Whitespace originally published on CSS-Tricks, which is part of the DigitalOcean family. You should get the newsletter.

I collect a bunch of links in a bookmarks folder. These are things I fully intend to read, and I do — eventually. It’s a good thing bookmarks are digital, otherwise, I’d need a bigger coffee table to separate them from the ever-growing pile of magazines.

The benefit of accumulating links is that the virtual pile starts revealing recurring themes. Two seemingly unrelated posts published a couple months apart may congeal and become more of a dialogue around a common topic.

I spent time pouring through a pile of links I’d accumulated over the past few weeks and noticed a couple of trending topics. No, that’s not me you’re smelling — there’s an aroma of nostalgia in the air., namely a newfound focus on learning web fundamentals and some love for manual deployments.

Web Developers, AI, and Development Fundamentals

Alvaro Montero:

Ultimately, it is not about AI replacing developers, but about developers adapting and evolving with the tools. The ability to learn, understand, and apply the fundamentals is essential because tools will only take you so far without the proper foundation.

ShopTalk 629: The Great Divide, Global Design + Web Components, and Job Titles

Chris and Dave sound off on The Great Divide in this episode and the rising value of shifting back towards fundamentals:

Dave: But I think what is maybe missing from that is there was a very big feeling of disenfranchisement from people who are good and awesome at CSS and JavaScript and HTML. But then were being… The market was shifting hard to these all-in JavaScript frameworks. And a lot of people were like, “I don’t… This is not what I signed up for.”

[…]

Dave: Yeah. I’m sure you can be like, “Eat shit. That’s how it is, kid.” But that’s also devaluing somebody’s skillset. And I think what the market is proving now is if you know JavaScript or know HTML, CSS, and regular JavaScript (non-framework JavaScript), you are once again more valuable because you understand how a line of CSS can replace 10,000 lines of JavaScript – or whatever it is.

Chris: Yeah. Maybe it’s coming back just a smidge–

Dave: A smidge.

Chris: –that kind of respecting the fundamental stuff because there’s been churn since then, since five years ago. Now it’s like these exclusively React developers we hired, how useful are they anymore? Were they a little too limited and fundamental people are knowing more? I don’t know. It’s hard to say that the job industry is back when it doesn’t quite feel that way to me.

Dave: Yeah, yeah. Yeah, who knows. I just think the value in knowing CSS and HTML, good HTML, are up more than they maybe were five years ago.

Just a Spec: HTML Finally Gets the Respect It Deserves

Jared and Ayush riffin’ on the first ever State of HTML survey, why we need it, and whether “State of…” surveys are representative of people who work with HTML.

[…] once you’ve learned about divs and H’s 1 through 6, what else is there to know? Quite a lot, as it turns out. Once again, we drafted Lea Verou to put her in-depth knowledge of the web platform to work and help us craft a survey that ended up reaching far beyond pure HTML to cover accessibility, web components, and much more.

[…]

You know, it’s perfectly fine to be an expert at HTML and CSS and know very little JavaScript. So, yeah, I think it’s important to note that as we talk about the survey, because the survey is a snapshot of just the people who know about the survey and answer the questions, right? It’s not necessarily representative of the broad swath of people around the world who have used HTML at all.

[…]

So yeah, a lot of interest in HTML. I’m talking about HTML. And yeah, in the conclusion, Lea Verou talks about we really do have this big need for more extensibility of HTML.

In a more recent episode:

I’m not surprised. I mean, when someone who’s only ever used React can see what HTML does, I think it’s usually a huge revelation to them.

[…]

It just blows their minds. And it’s kind of like you just don’t know what you’re missing out on up to a point. And there is a better world out there that a lot of folks just don’t know about.

[…]

I remember a while back seeing a post come through on social media somewhere, somebody’s saying, oh, I just tried working with HTML forms, just standard HTML forms the first time and getting it to submit stuff. And wait, it’s that easy?

Yeah, last year when I was mentoring a junior developer with the Railsworld conference website, she had come through Bootcamp and only ever done React, and I was showing her what a web component does, and she’s like, oh, man, this is so cool. Yeah, it’s the web platform.

Reckoning: Part 4 — The Way Out

Alex Russell in the last installment of an epic four-part series well worth your time to fully grasp the timeline, impact, and costs of modern JavsaScript frameworks to today’s development practices:

Never, ever hire for JavaScript framework skills. Instead, interview and hire only for fundamentals like web standards, accessibility, modern CSS, semantic HTML, and Web Components. This is doubly important if your system uses a framework.

Semi-Annual Reminder to Learn and Hire for Web Standards

Adrian Roselli:

This is a common cycle. Web developers tire of a particular technology — often considered the HTML killer when released — and come out of it calling for a focus on the native web platform. Then they decide to reinvent it yet again, but poorly.

There are many reasons companies won’t make deep HTML / CSS / ARIA / SVG knowledge core requirements. The simplest is the commoditization of the skills, partly because framework and library developers have looked down on the basics.

The anchor element

Heydon Pickering in a series dedicated to HTML elements, starting alphabetically with the good ol’ anchor :

Sometimes, the  is referred to as a hyperlink, or simply a link. But it is not one of these and people who say it is one are technically wrong (the worst kind of wrong).

[…]

Web developers and content editors, the world over, make the mistake of not making text that describes a link actually go inside that link. This is collosally unfortunate, given it’s the main thing to get right when writing hypertext.

AI Myth: It lets me write code I can’t on my own

Chris Ferndandi:

At the risk of being old and out-of-touch: if you don’t know how to write some code, you probably shouldn’t use code that Chat GPT et al write for you.

[…]

It’s not bulletproof, but StackOverflow provides opportunities to learn and understand the code in a way that AI-generated code does not.

What Skills Should You Focus on as Junior Web Developer in 2024?

Frontend Masters:

Let’s not be old-man-shakes-fist-at-kids.gif about this, but learning the fundamentals of tech is demonstrateably useful. It’s true in basketball, it’s true for the piano, and it’s true in making websites. If you’re aiming at a long career in websites, the fundamentals are what powers it.

[…]

The point of the fundamentals is how long-lasting and transferrable the knowledge is. It will serve you well no matter what other technologies a job might have you using, or when the abstractions over them change, as they are want to do.

As long as we’re talking about learning the fundamentals…

The Basics

Oh yeah, and of course there’s this little online course I released this summer for learning HTML and CSS fundamentals that I describe like this:

The Basics is more for your clients who do not know how to update the website they paid you to make. Or the friend who’s learning but still keeps bugging you with questions about the things they’re reading. Or your mom, who still has no idea what it is you do for a living. It’s for those whom the entry points are vanishing. It’s for those who could simply sign up for a Squarespace account but want to understand the code it spits out so they have more control to make a site that uniquely reflects them.

Not all this nostalgia is reserved only for HTML and CSS, but for deploying code, too. A few recent posts riff on what it might look like to ship code with “buildless” or near “buildless” workflows.

Raw-Dogging Websites

Brad Frost:

It is extraordinarily liberating. Yes, there are some ergonomic inefficiencies, but at the end of the day it comes out in the wash. You might have to copy-and-paste some HTML, but in my experience I’d spend that much time or more debugging a broken build or dependency hell.

Going Buildless

Max Böck in a follow-up to Brad:

So, can we all ditch our build tools soon?

Probably not. I’d say for production-grade development, we’re not quite there yet. Performance tradeoffs are a big part of it, but there are lots of other small problems that you’d likely run into pretty soon once you hit a certain level of complexity.

For smaller sites or side projects though, I can imagine going the buildless route – just to see how far I can take it.

Manual ’till it hurts

Jeremy Keith in a follow-up to Max:

If you’re thinking that your next project couldn’t possibly be made without a build step, let me tell you about a phrase I first heard in the indie web community: “Manual ‘till it hurts”. It’s basically a two-step process:

1. Start doing what you need to do by hand.
2. When that becomes unworkable, introduce some kind of automation.

It’s remarkable how often you never reach step two.

I’m not saying premature optimisation is the root of all evil. I’m just saying it’s premature.

---

That’s it for this pile of links and good gosh my laptop feels lighter for it. Have you read other recent posts that tread similar ground? Share ’em in the comments.

---

What’s Old is New originally published on CSS-Tricks, which is part of the DigitalOcean family. You should get the newsletter.

PSA: Today’s the day that Google’s performance tools officially stops supporting the First Input Delay (FID) metric that was replaced by Interaction to Next Paint (INP).

---

Quick Hit #18 originally published on CSS-Tricks, which is part of the DigitalOcean family. You should get the newsletter.

Jim hit a snag while working on a form. Placing labels next to inputs is trivial with flexbox, sure, but what happened in Jim’s case was a bit of dead-clicking between the labels and radio buttons.

The issue? Not the markup, that’s all semantic and cool. Turns out the gap he placed between the elements is non-interactive. Makes sense when you think about it, but frustrating nonetheless because it looks like a bug and feels like a bug even though there’s nothing wrong with the styles.

The solution’s easy enough: padding along the inside edge of the input extends its box dimensions, allowing the added space to remain interactive with visual spacing. Margin wouldn’t work since it’s akin to gap in that it pushes the element’s box instead of expanding it.

I’m linking up Jim’s article because it’s a perfect demonstration that CSS is capable of accomplishing the same thing in many ways. It’s easy to fall into the trap of “single-solution” thinking, but CSS doesn’t want anything to do with that. It’ll instead challenge you to adapt toward open-minded strategies, perhaps even defensive ones.

---

Sanding UI originally published on CSS-Tricks, which is part of the DigitalOcean family. You should get the newsletter.

Consider the early days of the internet, when websites like NBC News and Amazon cluttered their pages with flashing banners and labyrinthine menus. In the early 2000s, Steve Krug’s book Don’t Make Me Think arrived like a lighthouse in a storm, advocating for simplicity and user-centric design.

Today’s digital world is flooded with choices, information, and data, which is both exciting and overwhelming. Unlike Krug’s time, today, the problem isn’t interaction complexity but opacity. AI-powered solutions often lack transparency and explainability, raising concerns about user trust and accountability. The era of click-and-command is fading, giving way to a more seamless and intelligent relationship between humans and machines.

Expanding on Krug’s Call for Clarity: The Pillars of Anticipatory Design

Krug’s emphasis on clarity in design is more relevant than ever. In anticipatory design, clarity is not just about simplicity or ease of use — it’s about transparency and accountability. These two pillars are crucial but often missing as businesses navigate this new paradigm. Users today find themselves in a digital landscape that is not only confusing but increasingly intrusive. AI predicts their desires based on past behavior but rarely explains how these predictions are made, leading to growing mistrust.

Transparency is the foundation of clarity. It involves openly communicating how AI-driven decisions are made, what data is being collected, and how it is being used to anticipate needs. By demystifying these processes, designers can alleviate user concerns about privacy and control, thereby building trust.

Accountability complements transparency by ensuring that anticipatory systems are designed with ethical considerations in mind. This means creating mechanisms for users to understand, question, and override automated decisions if needed. When users feel that the system is accountable to them, their trust in the technology — and the brand — deepens.

What Makes a Service Anticipatory?

Image AI like a waiter at a restaurant. Without AI, they wait for you to interact with them and place your order. But with anticipatory design powered by AI and ML, the waiter can analyze your past orders (historical data) and current behavior (contextual data) — perhaps, by noticing you always start with a glass of sparkling water.

This proactive approach has evolved since the late 1990s, with early examples like Amazon’s recommendation engine and TiVo’s predictive recording. These pioneering services demonstrated the potential of predictive analytics and ML to create personalized, seamless user experiences.

Amazon’s Recommendation Engine (Late 1990s)

Amazon was a pioneer in using data to predict and suggest products to customers, setting the standard for personalized experiences in e-commerce.

TiVo (1999)

TiVo’s ability to learn users’ viewing habits and automatically record shows marked an early step toward predictive, personalized entertainment.

Netflix’s Recommendation System (2006)

Netflix began offering personalized movie recommendations based on user ratings and viewing history in 2006. It helped popularize the idea of anticipatory design in the digital entertainment space.

How Businesses Can Achieve Anticipatory Design

Designing for anticipation is designing for a future that is not here yet but has already started moving toward us.

Designing for anticipation involves more than reacting to current trends; it requires businesses to plan strategically for future user needs. Two critical concepts in this process are forecasting and backcasting.

• Forecasting analyzes past trends and data to predict future outcomes, helping businesses anticipate user needs.
• Backcasting starts with a desired future outcome and works backward to identify the steps needed to achieve that goal.

Think of it like planning a dream vacation. Forecasting would involve looking at your past trips to guess where you might go next. But backcasting lets you pick your ideal destination first, then plan the perfect itinerary to get you there.

Forecasting: A Core Concept for Future-Oriented Design

This method helps in planning and decision-making based on probable future scenarios. Consider Netflix, which uses forecasting to analyze viewers’ past viewing habits and predict what they might want to watch next. By leveraging data from millions of users, Netflix can anticipate individual preferences and serve personalized recommendations that keep users engaged and satisfied.

Backcasting: Planning From the Desired Future

Backcasting takes a different approach. Instead of using data to predict the future, it starts with defining a desired future outcome — a clear user intent. The process then works backward to identify the steps needed to achieve that goal. This goal-oriented approach crafts an experience that actively guides users toward their desired future state.

For instance, a financial planning app might start with a user’s long-term financial goal, such as saving for retirement, and then design an experience that guides the user through each step necessary to reach that goal, from budgeting tips to investment recommendations.

Integrating Forecasting and Backcasting In Anticipatory Design

The true power of anticipatory design emerges when businesses efficiently integrate both forecasting and backcasting into their design processes.

For example, Tesla’s approach to electric vehicles exemplifies this integration. By forecasting market trends and user preferences, Tesla can introduce features that appeal to users today. Simultaneously, by backcasting from a vision of a sustainable future, Tesla designs its vehicles and infrastructure to guide society toward a world where electric cars are the norm and carbon emissions are significantly reduced.

Over-Promising and Under-Delivering: The Pitfalls of Anticipatory Design

As businesses increasingly adopt anticipatory design, the integration of forecasting and backcasting becomes essential. Forecasting allows businesses to predict and respond to immediate user needs, while backcasting ensures these responses align with long-term goals. Despite its potential, anticipatory design often fails in execution, leaving few examples of success.

Over the past decade, I’ve observed and documented the rise and fall of several ambitious anticipatory design ventures. Among them, three — Digit, LifeBEAM Vi Sense Headphones, and Mint — highlight the challenges of this approach.

Digit: Struggling with Contextual Understanding

Digit aimed to simplify personal finance with algorithms that automatically saved money based on user spending. However, the service often missed the mark, lacking the contextual awareness necessary to accurately assess users’ real-time financial situations. This led to unexpected withdrawals, frustrating users, especially those living paycheck to paycheck. The result was a breakdown in trust, with the service feeling more intrusive than supportive.

LifeBEAM Vi Sense Headphones: Complexity and User Experience Challenges

LifeBEAM Vi Sense Headphones was marketed as an AI-driven fitness coach, promising personalized guidance during workouts. In practice, the AI struggled to deliver tailored coaching, offering generic and unresponsive advice. As a result, users found the experience difficult to navigate, ultimately limiting the product’s appeal and effectiveness. This disconnection between the promised personalized experience and the actual user experience left many disappointed.

Mint: Misalignment with User Goals

Mint aimed to empower users to manage their finances by providing automated budgeting tools and financial advice. While the service had the potential to anticipate user needs, users often found that the suggestions were not tailored to their unique financial situations, resulting in generic advice that did not align with their personal goals.

The lack of personalized, actionable steps led to a mismatch between user expectations and service delivery. This misalignment caused some users to disengage, feeling that Mint was not fully attuned to their unique financial journeys.

The Risks of Over-promising and Under-Delivering

The stories of Digit, LifeBEAM Vi Sense, and Mint underscore a common pitfall: over-promising and under-delivering. These services focused too much on predictive power and not enough on user experience. When anticipatory systems fail to consider individual nuances, they breed frustration rather than satisfaction, highlighting the importance of aligning design with human experience.

Digit’s approach to automated savings, for instance, became problematic when users found its decisions opaque and unpredictable. Similarly, LifeBEAM’s Vi Sense headphones struggled to meet diverse user needs, while Mint’s rigid tools failed to offer the personalized insights users expected. These examples illustrate the delicate balance anticipatory design must strike between proactive assistance and user control.

Failure to Evolve with User Needs

Many anticipatory services rely heavily on data-driven forecasting, but predictions can fall short without understanding the broader user context. Mint initially provided value with basic budgeting tools but failed to evolve with users’ growing needs for more sophisticated financial advice. Digit, too, struggled to adapt to different financial habits, leading to dissatisfaction and limited success.

Complexity and Usability Issues

Balancing the complexity of predictive systems with usability and transparency is a key challenge in anticipatory design.

When systems become overly complex, as seen with LifeBEAM Vi Sense headphones, users may find them difficult to navigate or control, compromising trust and engagement. Mint’s generic recommendations, born from a failure to align immediate user needs with long-term goals, further illustrate the risks of complexity without clarity.

Privacy and Trust Issues

Trust is critical in anticipatory design, particularly in services handling sensitive data like finance or health. Digit and Mint both encountered trust issues as users grew skeptical of how decisions were made and whether these services truly had their best interests in mind. Without clear communication and control, even the most sophisticated systems risk alienating users.

Inadequate Handling of Edge Cases and Unpredictable Scenarios

While forecasting and backcasting work well for common scenarios, they can struggle with edge cases or unpredictable user behaviors. If an anticipatory service can’t handle these effectively, it risks providing a poor user experience and, in the worst-case scenario, harming the user. Anticipatory systems must be prepared to handle edge cases and unpredictable scenarios.

LifeBEAM Vi Sense headphones struggled when users deviated from expected fitness routines, offering a one-size-fits-all experience that failed to adapt to individual needs. This highlights the importance of allowing users control, even when a system proactively assists them.

Designing for Anticipatory Experiences

Anticipatory design should empower users to achieve their goals, not just automate tasks.

We can follow a layered approach to plan a service that can evolve according to user actions and explicit ever-evolving intent.

But how do we design for intent without misaligning anticipation and user control or mismatching user expectations and service delivery?

At the core of this approach is intent — the primary purpose or goal that the design must achieve. Surrounding this are workflows, which represent the structured tasks to achieve the intent. Finally, algorithms analyze user data and optimize these workflows.

For instance, Thrive (see the image below), a digital wellness platform, aligns algorithms and workflows with the core intent of improving well-being. By anticipating user needs and offering personalized programs, Thrive helps users achieve sustained behavior change.

It perfectly exemplifies the three-layered concentric representation for achieving behavior change through anticipatory design:

1. Innermost layer: Intent

Improve overall well-being: Thrive’s core intent is to help users achieve a healthier and more fulfilling life. This encompasses aspects like managing stress, improving sleep quality, and boosting energy levels.

2. Middle layer: Workflows

Personalized programs and support: Thrive uses user data (sleep patterns, activity levels, mood) to create programs tailored to their specific needs and goals. These programs involve various workflows, such as:

• Guided meditations and breathing exercises to manage stress and anxiety.
• Personalized sleep routines aimed at improving sleep quality.
• Educational content and coaching tips to promote healthy habits and lifestyle changes.

3. Outermost layer: Algorithms

Data analysis and personalized recommendations: Thrive utilizes algorithms to analyze user data and generate actionable insights. These algorithms perform tasks like the following:

• Identify patterns in sleep, activity, and mood to understand user challenges.
• Predict user behavior to recommend interventions that address potential issues.
• Optimize program recommendations based on user progress and data analysis.

By aligning algorithms and workflows with the core intent of improving well-being, Thrive provides a personalized and proactive approach to behavior change. Here’s how it benefits users:

• Sustained behavior change: Personalized programs and ongoing support empower users to develop healthy habits for the long term.
• Data-driven insights: User data analysis helps users gain valuable insights into their well-being and identify areas for improvement.
• Proactive support: Anticipates potential issues and recommends interventions before problems arise.

The Future of Anticipatory Design: Combining Anticipation with Foresight

Anticipatory design is inherently future-oriented, making it both appealing and challenging. To succeed, businesses must combine anticipation — predicting future needs — with foresight, a systematic approach to analyzing and preparing for future changes.

Foresight involves considering alternative future scenarios and making informed decisions to navigate toward desired outcomes. For example, Digit and Mint struggled because they didn’t adequately handle edge cases or unpredictable scenarios, a failure in their foresight strategy (see an image below).

As mentioned, while forecasting and backcasting work well for common scenarios, they can struggle with edge cases or unpredictable user behaviors. Under anticipatory design, if we demote foresight for a second plan, the business will fail to account for and prepare for emerging trends and disruptive changes. Strategic foresight helps companies to prepare for the future and develop strategies to address possible challenges and opportunities.

The Foresight process generally involves interrelated activities, including data research, trend analysis, planning scenarios, and impact assessment. The ultimate goal is to gain a broader and deeper understanding of the future to make more informed and strategic decisions in the design process and foresee possible frictions and pitfalls in the user experience.

Actionable Insights for Designer

• Enhance contextual awareness
  Help data scientists or engineers to ensure that the anticipatory systems can understand and respond to the full context of user needs, not just historical data. Plan for pitfalls so you can design safety measures where the user can control the system.
• Maintain user control
  Provide users with options to customize or override automated decisions, ensuring they feel in control of their experiences.
• Align short-term predictions with long-term goals
  Use forecasting and backcasting to create a balanced approach that meets immediate needs while guiding users toward their long-term objectives.

Proposing an Anticipatory Design Framework

Predicting the future is no easy task. However, design can borrow foresight techniques to imagine, anticipate, and shape a future where technology seamlessly integrates with users evolving needs. To effectively implement anticipatory design, it’s essential to balance human control with AI automation. Here’s a 3-step approach to integrate future thinking into your workflow:

1. Anticipate Directions of Change
   Identify major trends shaping the future.
2. Imagine Alternative Scenarios
   Explore potential futures to guide impactful design decisions.
3. Shape Our Choices
   Leverage these scenarios to align design with user needs and long-term goals.

This proposed framework (see an image above) aims to integrate forecasting and backcasting while emphasizing user intent, transparency, and continuous improvement, ensuring that businesses create experiences that are both predictive and deeply aligned with user needs.

Step 1: Anticipate Directions of Change

Objective: Identify the major trends and forces shaping the future landscape.

Components:

1. Understand the User’s Intent

• User Research: Conduct in-depth user research through interviews, surveys, and observations to uncover user goals, motivations, pain points, and long-term aspirations or Jobs-to-be-Done (JTBD). This foundational step helps clearly define the user’s intent.
• Persona Development: Develop detailed user personas that represent the target audience, including their long-term goals and desired outcomes. Prioritize understanding how the service can adapt in real-time to changing user needs, offering recommendations, or taking actions aligned with the persona’s current context.

2. Forecasting: Predicting Near-Term User Needs

• Data Collection and Analysis: Collaborate closely with data scientists and data engineers to analyze historical data (past interactions), user behavior, and external factors. This collaboration ensures that predictive analytics enhance overall user experience, allowing designers to better understand the implications of data on user behaviors.
• Predictive Modeling: Implement continuous learning algorithms that refine predictions over time. Regularly assess how these models evolve, adapting to users’ changing needs and circumstances.
• Explore the Delphi Method: This is a structured communication technique that gathers expert opinions to reach a consensus on future developments. It’s particularly useful for exploring complex issues with uncertain outcomes. Use the Delphi Method to gather insights from industry experts, user researchers, and stakeholders about future user needs and the best strategies to meet those needs. The consensus achieved can help in clearly defining the long-term goals and desired outcomes.

Activities:

• Conduct interviews and workshops with experts using the Delphi Method to validate key trends.
• Analyze data and trends to forecast future directions.

Step 2: Imagine Alternative Scenarios

Objective: Explore a range of potential futures based on these changing directions.

Components:

1. Scenario Planning

• Scenario Development: It involves creating detailed, plausible future scenarios based on various external factors, such as technological advancements, social trends, and economic changes. Develop multiple future scenarios that represent different possible user contexts and their impact on their needs.
• Scenario Analysis: From these scenarios, you can outline the long-term goals that users might have in each scenario and design services that anticipate and address these needs. Assess how these scenarios impact user needs and experiences.

2. Backcasting: Designing from the Desired Future

• Define Desired Outcomes: Clearly outline the long-term goals or future states that users aim to achieve. Use backcasting to reduce cognitive load by designing a service that anticipates future needs, streamlining user interactions, and minimizing decision-making efforts.
  • Use Visioning Planning: This is a creative process that involves imagining the ideal future state you want to achieve. It helps in setting clear, long-term goals by focusing on the desired outcomes rather than current constraints. Facilitate workshops or brainstorming sessions with stakeholders to co-create a vision of the future. Define what success looks like from the user’s perspective and use this vision to guide the backcasting process.
• Identify Steps to Reach Goals: Reverse-engineer the user journey by starting from the desired future state and working backward. Identify the necessary steps and milestones and ensure these are communicated transparently to users, allowing them control over their experience.
• Create Roadmaps: Develop detailed roadmaps that outline the sequence of actions needed to transition from the current state to the desired future state. These roadmaps should anticipate obstacles, respect privacy, and avoid manipulative behaviors, empowering users rather than overwhelming them.

Activities:

• Develop and analyze alternative scenarios to explore various potential futures.
• Use backcasting to create actionable roadmaps from these scenarios, ensuring they align with long-term goals.

Step 3: Shape Our Choices

Objective: Leverage these scenarios to spark new ideas and guide impactful design decisions.

Components:

1. Integrate into the Human-Centered Design Process

• Iterative Design with Forecasting and Backcasting: Embed insights from forecasting and backcasting into every stage of the design process. Use these insights to inform user research, prototype development, and usability testing, ensuring that solutions address both predicted future needs and desired outcomes. Continuously refine designs based on user feedback.
• Agile Methodologies: Adopt agile development practices to remain flexible and responsive. Ensure that the service continuously learns from user interactions and feedback, refining its predictions and improving its ability to anticipate needs.

2. Implement and Monitor: Ensuring Ongoing Relevance

• User Feedback Loops: Establish continuous feedback mechanisms to refine predictive models and workflows. Use this feedback to adjust forecasts and backcasted plans as necessary, keeping the design aligned with evolving user expectations.
• Automation Tools: Collaborate with data scientists and engineers to deploy automation tools that execute workflows and monitor progress toward goals. These tools should adapt based on new data, evolving alongside user behavior and emerging trends.
• Performance Metrics: Define key performance indicators (KPIs) to measure the effectiveness, accuracy, and quality of the anticipatory experience. Regularly review these metrics to ensure that the system remains aligned with intended outcomes.
• Continuous Improvement: Maintain a cycle of continuous improvement where the system learns from each interaction, refining its predictions and recommendations over time to stay relevant and useful.
  • Use Trend Analysis: This involves identifying and analyzing patterns in data over time to predict future developments. This method helps you understand the direction in which user behaviors, technologies, and market conditions are heading. Use trend analysis to identify emerging trends that could influence user needs in the future. This will inform the desired outcomes by highlighting what users might require or expect from a service as these trends evolve.

Activities:

• Implement design solutions based on scenario insights and iterate based on user feedback.
• Regularly review and adjust designs using performance metrics and continuous improvement practices.

Conclusion: Navigating the Future of Anticipatory Design

Anticipatory design holds immense potential to revolutionize user experiences by predicting and fulfilling needs before they are even articulated. However, as seen in the examples discussed, the gap between expectation and execution can lead to user dissatisfaction and erode trust.

To navigate the future of anticipatory design successfully, businesses must prioritize transparency, accountability, and user empowerment. By enhancing contextual awareness, maintaining user control, and aligning short-term predictions with long-term goals, companies can create experiences that are not only innovative but also deeply resonant with their users’ needs.

Moreover, combining anticipation with foresight allows businesses to prepare for a range of future scenarios, ensuring that their designs remain relevant and effective even as circumstances change. The proposed 3-step framework — anticipating directions of change, imagining alternative scenarios, and shaping our choices — provides a practical roadmap for integrating these principles into the design process.

As we move forward, the challenge will be to balance the power of AI with the human need for clarity, control, and trust. By doing so, businesses can fulfill the promise of anticipatory design, creating products and services that are not only efficient and personalized but also ethical and user-centric.

In the end,

The success of anticipatory design will depend on its ability to enhance, rather than replace, the human experience.

It is a tool to empower users, not to dictate their choices. When done right, anticipatory design can lead to a future where technology seamlessly integrates with our lives, making everyday experiences simpler, more intuitive, and ultimately more satisfying.

Data has become the lifeblood of successful product development in today’s competitive market. Companies no longer rely solely on intuition or past experiences; instead, they harness the power of data to make informed decisions that drive innovation. 

But raw data alone is not enough. This is where data analytics consulting comes into play. It converts vast amounts of information into actionable insights that can make or break a product. 

This blog post will explain how partnering with data analytics experts can significantly enhance every stage of product development enhancement and ensure that your next big idea meets and exceeds market expectations.

An Overview of Data Analytics in Product Development

Data analytics in product development involves systematically analyzing various data points to gain insights that inform product creation, design, and improvement. It encompasses collecting, processing, and interpreting data to understand 

• User needs
• Market trends
• Potential areas for innovation

This approach ensures that decisions are grounded in factual evidence rather than assumptions, leading to more successful product outcomes. 

Key Metrics and Data Points

Have a look at the key metrics and data points involved in data analytics consulting for product improvement:

1. Customer Feedback: Gathering insights from user reviews, surveys, and social media interactions to understand preferences, pain points, and areas for improvement.
2. Market Trends: Analyzing industry trends, competitor products, and emerging technologies to identify opportunities and threats in the market.
3. Performance Metrics: Tracking key performance indicators (KPIs) such as product usage, customer retention, and sales data to evaluate how well a product meets its intended goals and where adjustments may be necessary.

Role of Data Analytics Consulting

Data analytics consulting provides expert guidance in interpreting complex data. It ensures that insights are actionable and aligned with business goals. 

Consultants bring specialized knowledge in data science and industry-specific trends, helping companies navigate the vast landscape of available data.

Their expertise allows businesses to focus on strategic decisions, reducing the time and effort needed to extract meaningful data insights for product strategy from raw data.

Key Benefits of Data Analytics Consulting in Product Development

Look at the advantages of integrating data analytics consulting into product development, ensuring a clear understanding of how these benefits contribute to overall success.

Informed Decision-Making

Businesses can make strategic decisions backed by concrete evidence by analyzing comprehensive data sets. This ensures that every step of product development is guided by insights that reflect actual customer behavior, market conditions, and emerging trends.

Data analytics services help identify potential risks and challenges early in the development process. This reduces uncertainty, allowing companies to launch products with greater confidence and a higher likelihood of success.

Enhancing Product Design and Features

Businesses can deeply understand what their customers want through data analytics in product design. By analyzing feedback, usage patterns, and market demands, companies can design products that resonate with their target audience.

Data-driven design allows for iterative improvements based on user interactions and feedback. This continuous refinement ensures that the product meets and exceeds user expectations, leading to a more satisfying and engaging experience.

Accelerating Time-to-Market

Data analytics identifies inefficiencies and bottlenecks in the development process, allowing teams to streamline workflows and eliminate delays. This optimization leads to faster product development cycles and quicker time-to-market.

By leveraging predictive analytics, businesses can forecast market demand and prioritize development efforts accordingly. This proactive approach helps align product launches with market needs, ensuring relevance and timeliness.

Cost Optimization

Data analytics enables precise resource allocation by identifying areas where investment will yield the highest returns. This ensures that resources are used efficiently, maximizing the impact of every dollar spent.

By focusing on data-driven strategies, companies can reduce the costs associated with unsuccessful product features or strategies that don’t align with market demand. This minimizes waste and improves overall profitability.

Competitive Advantage

Data analytics provides insights that allow businesses to position their products effectively in the market. Companies can differentiate their offerings by understanding competitors and market trends and capturing a larger market share.

Data-driven insights fuel innovation by revealing unmet needs and opportunities for differentiation. This leads to developing unique products that stand out in the market, giving companies a competitive edge.

Challenges in Implementing Data Analytics in Product Development

Have a look at the key challenges companies face when trying to implement data analytics in product development, providing a realistic view of the hurdles that need to be overcome.

Data Integration: One of the primary challenges is integrating data from various sources. Startup product development services often require data from multiple channels, including customer feedback, sales data, and market trends. 

Consolidating this information into a unified, actionable dataset can be complex, especially when dealing with disparate systems and formats.

Cost Considerations: Implementing data analytics has its costs. The investment required for data analytics consulting and the necessary tools and technologies can be significant. 

This can be a barrier for smaller companies or those with limited budgets, making it essential to carefully weigh the potential return on investment.

Talent Gap: A successful data analytics strategy requires skilled professionals who interpret complex data sets and translate them into meaningful insights. 

However, there is often a talent gap in the market, with a shortage of data scientists and analysts with the expertise needed to drive product development. This gap can hinder a company’s ability to leverage data analytics in its processes fully.

How to Choose the Right Data Analytics Consulting Partner?

Focusing on the following key areas can help you select a data analytics consulting partner who aligns with your needs, enhances your product development process, and drives meaningful results.

1. Experience and Expertise

You must seek the following expertise and skills when choosing a Data analytics consulting partner. 

Key Qualifications: Look for firms with extensive experience in data analytics, particularly in your industry. Expertise in relevant tools, technologies, and methodologies is crucial.

Industry Knowledge: Ensure the consulting partner deeply understands your specific market and product development needs. This knowledge helps in providing insights that are not only accurate but also actionable within your industry context.

Technical Skills: Evaluate the firm’s proficiency in advanced data analytics techniques, such as machine learning, predictive modeling, and data visualization. These skills are essential for deriving meaningful insights from complex data sets.

2. Customization and Flexibility

Look for the following factors when you choose a Data Analytics partner that offers flexibility and customization features. 

Tailored Solutions: Choose a partner that offers customized solutions rather than one-size-fits-all approaches. Your product development needs are unique, and a tailored approach ensures that the analytics strategies align perfectly with your specific goals and challenges.

Adaptability: It is crucial to adjust strategies and methods as your project evolves. A flexible consulting partner can accommodate changes in project scope, priorities, and emerging data needs, ensuring ongoing relevance and effectiveness.

3. Proven Track Record

Before choosing the right Data Analytics partner, you must look for the client testimonials and their past reviews.

Past Successes: Review the consulting firm’s portfolio of completed projects to assess their success in similar contexts. Look for case studies or examples of how their analytics solutions have positively impacted other companies.

Client Testimonials: Seek feedback from previous clients to gauge their satisfaction with the firm’s services. Positive testimonials and references can provide valuable insights into the firm’s reliability, communication, and effectiveness.

Future Trends in Data Analytics and Product Development

These trends are shaping the future of data analytics in product development, making processes more efficient, responsive, and aligned with evolving market needs.

AI and Machine Learning

• Automating Data Analysis: Artificial Intelligence (AI) and Machine Learning (ML) are increasingly used to automate complex data analysis tasks. These technologies can analyze large volumes of data more quickly and accurately than traditional methods, uncovering insights that might be missed otherwise.
• Enhancing Product Development: AI and ML algorithms can also be applied to the product development. For instance, they can help identify patterns in user behavior, optimize product features based on usage data, and even assist in designing new products by predicting customer preferences.

Predictive Analytics

• Anticipating Market Needs: Predictive analytics uses historical data and statistical algorithms to forecast future trends. This can be invaluable for product development, allowing companies to anticipate market demands and adjust their strategies accordingly.
• Informed Decision-Making: By leveraging predictive models, businesses can make more informed decisions about product features, launch timing, and market positioning, reducing the risk of product failures and enhancing overall success rates.

Real-Time Analytics

• Immediate Decision-Making: The ability to analyze real-time data transforms how decisions are made in product development. Real-time analytics allows teams to respond quickly to new information, user feedback, and market changes, ensuring product development stays aligned with current conditions.
• Dynamic Adjustments: This trend supports more agile development processes, enabling continuous improvements and adaptations based on live data. As a result, products can evolve rapidly to meet user needs and market demands more effectively.

Conclusion

In a world where consumer preferences and market conditions are constantly changing, the ability to harness data effectively is the difference between a product’s success and failure. 

Data analytics consulting services empower businesses to navigate this uncertainty confidently, turning raw data into strategic insights that fuel innovation and drive success. 

By integrating data-driven decisions into product development, companies can meet and exceed customer expectations, ensuring their offerings remain relevant and competitive in an ever-evolving marketplace.

Featured image by Lukas

The post How Data Analytics Consulting Enhances Product Development appeared first on noupe.

Checkbox and radio button hacks are the (in)famous trick for creating games using just CSS. But it turns out that other elements based on user input can be hacked and gamified. There are very cool examples of developers getting creative with CSS games based on the :hover pseudo-class, and even other games based on the :valid pseudo-class.

What I’ve found, though, is that the :target pseudo-class seems relatively unexplored territory in this area of CSS hacking. It’s an underrated powerful CSS feature when you think about it: :target allows us to style anything based on the selected jump link, so we have a primitive version of client-side routing built into the browser! Let’s go mad scientist with it and see where that takes us.

Unbeatable AI in CSS

Did I type those words together? Are we going to hack CSS so hard that we hit the singularity? Try to beat the stylesheet below at Tic Tac Toe and decide for yourself.

The stylesheet will sometimes allow the game to end in a draw, so you at least have a smidge of hope.

No need to worry! CSS hasn’t gone Skynet on us yet. Like any CSS hack, the rule of thumb to determine whether a game is possible to implement with CSS is the number of possible game states. I learned that when I was able to create a 4×4 Sudoku solver but found a 9×9 version pretty darn near impossible. That’s because CSS hacks come down to hiding and showing game states based on selectors that respond to user input.

Tic Tac Toe has 5,478 legal states reachable if X moves first and there’s a famous algorithm that can calculate the optimal move for any legal state. It stands to reason, then, that we can hack together the Tic Tac Toe game completely in CSS.

OK, but how?

In a way, we are not hacking CSS at all, but rather using CSS as the Lord Almighty intended: to hide, show, and animate stuff. The “intelligence” is how the HTML is generated. It’s like a “choose your own adventure” book of every possible state in the Tic Tac Toe multiverse with the empty squares linked to the optimal next move for the computer.

We generate this using a mutant version of the minimax algorithm implemented in Ruby. And did you know that since CodePen supports HAML (which supports Ruby blocks), we can use it secretly as a Ruby playground? Now you do.

Each state our HAML generates looks like this in HTML:

<div class="b" id="--OOX----">
  <svg class="o s">
    <circle></circle>
  </svg>

  <a class="s" href="#OXOOX----">
    <div></div>
  </a>

  <svg class="o s">
    <circle class="c"></circle>
  </svg>

  <svg class="o s">
    <circle class="c"></circle>
  </svg>

  <div class="x"></div>

  <a class="s" href="#O-OOXX---">
    <div></div>
  </a>

  <a class="s" href="#O-OOX-X--">
    <div></div>
  </a>

  <a class="s" href="#O-OOX--X-">
    <div></div>
  </a>

  <a class="s" href="#O-OOX---X">
    <div></div>
  </a>
</div>

With a sprinkling of surprisingly straightforward CSS, we will display only the currently selected game state using :target selectors. We’ll also add a .c class to historical computer moves — that way, we only trigger the handwriting animation for the computer’s latest move. This gives the illusion that we are only playing on a single gameboard when we are, in reality, jumping between different sections of the document.

/* Game's parent container */
.b, body:has(:target) #--------- {
  /* Game states */
  .s {
    display: none;
  }
}

/* Game pieces with :target, elements with href */
:target, #--------- {
  width: 300px;
  height: 300px; /
  left: calc(50vw - 150px);
  top: calc(50vh - 150px);
  background-image: url(/path/to/animated/grid.gif);
  background-repeat:  no-repeat;
  background-size: 100% auto;
  
  /* Display that game state and bring it to the forefront  */
  .s {
    z-index: 1;
    display: inline-block;
  }
  
  /* The player's move */
  .x {
    z-index: 1;
    display: inline-block;
    background-image: url("data:image/svg+xml [...]"); /** shortened for brevity **/ 
    height: 100px;
    width: 100px;
  }
  
  /* The browser's move */
  circle {
    animation-fill-mode: forwards;
    animation-name: draw;
    animation-duration: 1s;
    
    /* Only animate the browser's latest turn */
    &.c {
      animation-play-state: paused;
      animation-delay: -1s;
    }
  }
}

When a jump link is selected by clicking an empty square, the :target pseudo-class displays the updated game state(.s), styled so that the computer’s precalculated response makes an animated entrance (.c).

Note the special case when we start the game: We need to display the initial empty grid before the user selects any jump link. There is nothing to style with :target at the start, so we hide the initial state — with the:body:has(:target) #--------- selector — once a jump link is selected. Similarly, if you create your experiments using :target you’ll want to present an initial view before the user begins interacting with your page. 

Wrapping up

I won’t go into “why” we’d want to implement this in CSS instead of what might be an “easier” path with JavaScript. It’s simply fun and educational to push the boundaries of CSS. We could, for example, pull this off with the classic checkbox hack — someone did, in fact.

Is there anything interesting about using :target instead? I think so because:

• We can save games in CSS! Bookmark the URL and come back to it anytime in the state you left it.
• There’s a potential to use the browser’s Back and Forward buttons as game controls. It’s possible to undo a move by going Back in time or replay a move by navigating Forward. Imagine combining :target with the checkbox hack to create games with a time-travel mechanic in the tradition of Braid.
• Share your game states. There’s the potential of Wordle-like bragging rights. If you manage to pull off a win or a draw against the unbeatable CSS Tic Tac Toe algorithm, you could show your achievement off to the world by sharing the URL.
• It’s completely semantic HTML. The checkbox hack requires you to hide checkboxes or radio buttons, so it will always be a bit of a hack and painful horse-trading when it comes to accessibility. This approach arguably isn’t a hack since all we are doing is using jump links and divs and their styling. This may even make it — dare I say —“easier” to provide a more accessible experience. That’s not to say this is accessible right out of the box, though.

---

Time Travelling CSS With :target originally published on CSS-Tricks, which is part of the DigitalOcean family. You should get the newsletter.

Quite a fun article I worked on with Philip Braunen. Do you know that little bit of elasticity you get when scrolling beyond the viewport when browsing the web on a mobile device? iPhone calls it a “rubber-banding” effect. And you know it’s cool because Apple has previously fought to hold a copyright on it.

Anyway, Philip wrote into Smashing Magazine with a clever approach to mimic rubber-banding in CSS — not only for non-mobile UI but also applied to any sort of container you like.

But what about sticky headers and footers? If those have to be pinned to the container’s block edges, then how in heck do we include them in the rubber banding? Phillip’s trick is an extra div before the header, though we can get more concise markup using pseudos instead.

---

Sticky Headers And Full-Height Elements: A Tricky Combination originally published on CSS-Tricks, which is part of the DigitalOcean family. You should get the newsletter.

“Wrapping the around the is fine, and is sufficient for conformance on its own, however adding explicit association with for and id is still necessary in practice.” —James Edwards

---

Quick Hit #17 originally published on CSS-Tricks, which is part of the DigitalOcean family. You should get the newsletter.

Did you know that 55% of consumers are more likely to buy from a brand if they love its story? Or that businesses with a strong brand story have a 22% higher valuation than those without? These statistics highlight a powerful truth in the field services industry: storytelling matters.

Brand storytelling has emerged as a game-changer in an environment in which 89% of companies compete primarily based on customer experience, making brand storytelling increasingly important in business today. No longer simply fixing items or providing services; now it’s about building lasting connections and forging relationships that transcend simple transactions.

Let’s examine why brand storytelling in field services is vital and how it can transform your business.

Why Stories Matter in Field Services

Field services is all about solving customer needs and improving lives, so our priority should be doing an outstanding job and satisfying customers – but there’s another aspect which shouldn’t be neglected: telling your brand’s story.

Real-life Example

• HVAC business owner struggling to differentiate from competitors
• Started sharing his business’s journey
• Results:
  • Customers saw the business differently
  • More loyal customers
  • Increased word-of-mouth referrals

How to Tell a Good Brand Story

Key Elements

• How You Started: Share your beginning (e.g., starting in a garage, one van and a toolbox)
• What You Believe In: Highlight company values (e.g., eco-friendliness, community involvement)
• Success Stories: Share how you’ve helped customers
• Your Future Plans: Share your big dreams (e.g., new services, new technology)

How Stories Help Connect with Customers

Building Trust

• Importance of trust in field services
• Open and honest storytelling builds trust
• Trusted businesses often chosen over cheaper alternatives

Creating Emotional Connections

• Better memory retention of stories vs. service lists
• Emotional connections can turn one-time customers into repeat clients

Standing Out

• Differentiation in a crowded market
• Use your story in marketing to be memorable

What’s Coming Next in Brand Storytelling

Current trends show that 55% of people are more likely to buy from a brand if they love its story, and 44% will share the brand story with others. As we use more online marketing, businesses that tell good stories on social media and blogs will have an advantage. 

Another emerging trend is personalized storytelling, which means sharing stories that fit different groups of customers.

How to Start Telling Your Brand Story

Steps to Begin

1. Think About Your Journey: Reflect on big moments, problems faced, and wins
2. Get Your Team Involved: Include your team in storytelling
3. Share Your Story Everywhere: Maintain consistency across all platforms
4. Let Customers Join In: Encourage customers to share their experiences

Field Promax: Enhancing Your Brand Story Through Efficiency

Field Promax is a comprehensive field service management software that can significantly support and enhance your brand storytelling efforts.

Key Features

• Scheduling optimization
• Mobile app for technicians
• Automated billing and invoicing
• Real time tracking and reporting

Supporting Your Brand Story

1. Improved Customer Experience
   • Faster response times
   • More accurate appointment scheduling
   • Seamless communication between office and field
2. Professionalism and Reliability
   • Digital documentation and signatures
   • Prompt and accurate invoicing
   • Consistent service delivery
3. Data-Driven Storytelling
   • Use analytics to showcase your efficiency
   • Share improved metrics as part of your success story
4. Empowering Your Team
   • Technicians equipped with necessary information
   • Reduced administrative burden allows focus on customer interaction
5. Adapting to Modern Expectations
   • Demonstrates your commitment to using cutting-edge technology
   • Aligns with stories of continuous improvement and innovation

Simple Tips for Brand Storytelling in Field Service Businesses

1. Craft a Signature Service Style

• Develop a unique approach to service delivery
• Create catchy names for your service packages

2. Highlight Your Tools and Tech

• Showcase the specialized equipment you use
• Explain how your tools benefit customers

3. Feature Your Team’s Expertise

• Share employee certifications and training
• Create “meet the expert” profiles for your technicians

4. Document Your Problem-Solving Process

• Use before-and-after imagery of your work
• Create a step-by-step visual guide of your service process

5. Celebrate Local Connections

• Highlight partnerships with other local businesses
• Share stories of community involvement and impact

6. Develop a Mascot or Character

• Create a memorable mascot representing your services
• Use the character in marketing materials and social media

7. Showcase Your Environmental Initiatives

• Highlight any eco-friendly practices or products
• Share your company’s sustainability goals and progress

8. Create a “Day in the Life” Series

• Follow a technician through a typical workday
• Share behind-the-scenes glimpses of your operations

9. Establish a Unique Company Tradition

• Create an annual event or challenge related to your services
• Share stories and photos from these traditions

10. Develop a Customer Recognition Program

• Highlight “Customer of the Month” stories
• Share testimonials in creative, visual formats

Remember, your brand story is what makes you special. It’s not just about fixing things or providing services. It’s about connecting with people and showing them why your business is awesome. By incorporating tools like Field Promax into your operations, you’re not just working faster – you’re making more time to really talk to your customers and reinforce the promises your brand story makes.

Conclusion

In today’s competitive field services landscape, a compelling brand story can be the difference between being just another service provider and becoming a trusted partner in your customers’ lives. It helps build trust, create emotional connections, and set you apart from the competition. As you move forward, consider how you can weave your unique story into every aspect of your business, from your marketing materials to your daily interactions with customers. With a strong brand story and the right tools to support it, you’ll be well-positioned to create lasting relationships and drive your business to new heights of success.

The post Brand Storytelling in Field Services: Connecting with Customers Through Narrative appeared first on noupe.