Designing for Someone, Not Everyone

Last week, I watched two very different product conversations unfold side by side.

One was about speed — shipping faster, cutting cycles, getting features out before the competition does. The other was about segmentation in banking — how "youth ≠ SMEs ≠ HNI" and how products fail when they pretend those differences don’t matter.

At first glance, they felt unrelated. One is about velocity. The other is about customer strategy. But the more I sat with them, the more I realized they’re orbiting the same quiet question:

Are we designing for someone specific, or are we building for a statistical blur?

As product teams get better at shipping quickly — and as AI makes it easier to generate interfaces, flows, even entire products — the risk isn’t that we move too fast.

It’s that we stop being precise about who we’re moving for.

And in my experience, precision about people is the real competitive advantage.

The Myth of the Average User

I once worked on a financial dashboard that was technically excellent.

It loaded quickly. The information hierarchy was clean. Accessibility contrast ratios were correct. We had strong usability scores in testing — an 82 SUS, which most teams would happily celebrate.

And yet, three months after launch, adoption plateaued.

When we dug deeper, we realized something uncomfortable: the dashboard worked well for no one in particular. It had been optimized for an imagined "average" customer.

But there is no average customer in financial decision-making.

• A 23-year-old opening their first investment account isn’t looking for the same signals as a 48-year-old managing retirement risk.
• A small business owner reviewing cash flow at midnight has different cognitive bandwidth than a wealth advisor checking portfolios during market hours.
• A high-net-worth client doesn’t interpret volatility charts the same way a first-time investor does.

We had designed a beautifully neutral system.

Neutrality, it turns out, is often a form of vagueness.

The Data Backs This Up

McKinsey research has shown that companies that effectively use customer segmentation can achieve 10–20% increases in revenue through more tailored experiences. Meanwhile, a Salesforce report found that 66% of customers expect companies to understand their unique needs — and 52% expect offers to always be personalized.

Those numbers are often cited to justify personalization engines.

But personalization at the surface layer — swapping content blocks or adjusting recommendations — is not the same as designing the core experience for a specific cognitive and emotional context.

The difference is architectural, not cosmetic.

What Rebuilding the First Browser Reminds Us

One of the more charming things I saw circulating this week was that CERN rebuilt the original 1989 web browser.

If you look at it now, it feels almost austere. No algorithmic feed. No ad placements. No infinite scroll. Just text, links, structure.

It was designed for a very specific group: researchers who needed to share information.

That specificity shaped everything:

• The interaction model was simple because the task was clear.
• The information density was high because the users valued content over decoration.
• The mental model was coherent because it matched the community’s needs.

The modern web, by contrast, is often designed for scale first. We optimize for growth curves, engagement minutes, ad inventory. There’s a reason someone joked this week that "every company building your AI assistant is now an ad company."

When your real customer becomes "the advertiser," the end user turns into a metric to be optimized.

And when that happens, design decisions subtly drift away from specific human needs and toward abstract performance indicators.

As a designer, that’s where I feel the tension most acutely.

The Cost of Designing for the Blur

When we design for "everyone," three things tend to happen.

1. We Overgeneralize Flows

We add options to accommodate edge cases instead of making clear tradeoffs.

The result? Interfaces that feel technically flexible but cognitively heavy. Users have to translate their intent into our menu of possibilities.

2. We Flatten Emotional Context

Financial products are a perfect example.

Research in behavioral economics shows that financial decisions activate stress responses similar to physical threats. A study from Cambridge found that financial uncertainty can significantly impair cognitive function — sometimes equivalent to losing a night’s sleep.

Yet many fintech apps present high-stakes decisions with the same tone and density as a social media settings panel.

When we ignore emotional context, we create friction that metrics don’t immediately reveal.

3. We Default to Feature Accumulation

If you don’t know who you’re designing for, it’s safer to add features than remove them.

That’s how products become malls without directories — lots of shops, unclear pathways.

Ironically, the "Mall Principle" circulating in banking conversations gets this right: different people need different spaces. But the principle only works if you commit to building distinct experiences, not just different marketing pages.

Segmentation isn’t about labeling users. It’s about designing environments where specific types of thinking can happen comfortably.

Designing for a Cognitive Mode

One shift that’s helped me is reframing segmentation from demographics to cognitive modes.

Instead of asking, "Is this for millennials or Gen X?" I ask:

• Is this for someone exploring or someone executing?
• Is this for someone anxious or someone confident?
• Is this for someone learning or someone optimizing?

Those states change how information should be structured.

For example, in a recent internal project reviewing onboarding flows, we noticed something subtle. Users who came in via referral (often more confident and goal-oriented) moved quickly through setup. Users who came in through paid acquisition (often more exploratory) lingered, hesitated, and re-read copy.

The interface was identical for both groups.

What changed their experience wasn’t the UI — it was their mindset.

We adjusted the flow in two key ways:

1. Progressive disclosure for exploratory users — optional "learn more" layers instead of dense upfront explanation.
2. Shortcut pathways for confident users — visible "skip ahead" affordances that respected their momentum.

Completion rates improved by 14% in the exploratory segment, and support tickets dropped in the first 30 days.

Not because we redesigned everything.

Because we stopped assuming one cognitive state.

Speed Is Only an Advantage If You’re Aiming Correctly

The conversation about shipping fast versus thinking deeply often frames them as opposites.

I don’t think they are.

Speed amplifies whatever direction you’ve chosen.

If you’re building for a vague composite user, speed helps you scale mediocrity.

If you’re building for a sharply defined human context, speed helps you compound clarity.

In my own practice, I’ve found a few design habits that protect specificity even when timelines are tight:

• Name the primary user in the room. Not "target segment," but a sentence: "This is for a first-time investor making their second deposit." If we can’t articulate that, we’re not ready to design.
• Define what we are not optimizing for. Clarity often comes from explicit exclusion.
• Test with contrast, not averages. Put the interface in front of two very different cognitive modes and watch where it strains.

None of this slows us down dramatically.

It simply forces us to aim.

The Quiet Ethics of Specificity

There’s another layer here that I think we don’t talk about enough.

When products are designed for "everyone," they often default to the behaviors that are most profitable, not most healthy.

That’s how AI assistants become ad surfaces. That’s how engagement metrics start shaping attention patterns. That’s how optimization quietly shifts incentives.

Designing for someone specific — with a clear job, a clear emotional state, a clear context — acts as a guardrail.

It asks:

• Does this help them accomplish what they came to do?
• Or does it help us extract more value from their time?

Those are not always aligned.

As designers, we sit uncomfortably in the middle of that tension.

And the more powerful our tools become — AI-generated layouts, automated experimentation, dynamic content systems — the more important our human judgment becomes.

Because tools are very good at optimizing for patterns.

They are not good at caring about people.

Returning to Someone Real

When I think back to that financial dashboard that stalled, what changed the trajectory wasn’t a major redesign.

It was a reframing.

We chose a primary user: a small business owner checking cash flow between meetings.

That decision led us to:

• Simplify the landing view to three essential signals.
• Reduce jargon and industry terminology.
• Surface "what needs attention today" instead of full portfolio performance.

Engagement didn’t skyrocket overnight. But retention improved steadily over two quarters.

More importantly, customer interviews shifted tone. Instead of "It’s fine," we started hearing, "It feels like it’s built for how I actually work."

That sentence is the quiet north star.

In a world obsessed with speed, automation, and scale, the real discipline might be this:

Designing for someone you can picture clearly.

Not a persona slide. Not a demographic slice. Not a growth segment.

A human being in a specific moment, trying to get something done without unnecessary friction.

When we do that, speed becomes powerful instead of reckless. Personalization becomes meaningful instead of manipulative. And our work starts to feel less like optimizing a system — and more like building something that genuinely fits.

That’s the kind of competitive advantage I’m interested in.

Not just faster.

More precise.

And ultimately, more human.