In a world obsessed with averages, it’s easy to assume that building for the "average user" is safe, efficient, even strategic. After all, average is where the majority lives, right?
Wrong.
This isn’t just a design flaw—it’s a business risk. The real kicker? The idea of the “average user” was debunked over 70 years ago by, of all people, the U.S. Air Force. And the lessons they learned while designing fighter jet cockpits still apply—only now, we’re designing digital products, not planes.
Back in 1950, the U.S. Air Force was dealing with a deadly mystery: why were highly trained pilots making frequent errors in flight? The initial assumption was human error. But Lt. Gilbert S. Daniels, a physical anthropologist, had another theory.
He measured 4,063 pilots across 140 physical dimensions—height, chest circumference, limb length, etc.—to design a cockpit for the “average” pilot. What he discovered shattered decades of assumptions.
Out of thousands of pilots, not a single one matched the average across all dimensions. Even when narrowing it down to just three dimensions, fewer than 3.5% matched.
Designing for the average, it turned out, fit almost no one.
So what happened? Cockpits became adjustable. Aircraft became safer. And the Air Force learned that if you design for the individual, not the imaginary average, everyone benefits.
Today, we’re not designing cockpits—we’re designing checkout flows, onboarding experiences, healthcare portals, and AI interfaces. But the mistake is the same: designing for a mythical average user.
If you're in e-commerce, health tech, or building AI-driven platforms, here’s a hard truth: there’s no such thing as a typical user. You’ve got one customer on mobile in a rural town with spotty data and another on a high-end desktop in Singapore toggling between five tabs and dark mode.
Try building one experience that suits them both—and good luck hitting your retention metrics.
This is where automated user testing and personalization validation come in.
Automated user testing is the process of using intelligent tools (like CarbonCopies AI) to simulate how different users interact with your product. These simulations aren’t just bots clicking buttons—they’re AI-driven agents that behave like real people.
They catch bugs you didn’t know existed. They test long-tail user flows. They say, “Hey, this dropdown breaks if I switch languages mid-checkout” or “This chatbot loops forever when I say something weird.”
And they do it at scale—without a room full of QA engineers.
Let’s say you are investing in personalization (smart move). Great—you’ve got different content blocks for first-time vs. returning users, or dynamic pricing for enterprise customers vs. startups. But how do you make sure it works?
That’s what personalization validation is for. It’s the process of ensuring that each persona is getting the right experience, without errors or misfires.
This is not just about A/B testing. This is about validating that your tailored experiences actually fit the people they’re meant for.
CarbonCopies, for example, automatically runs these validations using AI personas that mirror real user behavior, catching issues before they hit your real customers.
Building “one version for everyone” is not just lazy—it’s unsustainable. Users expect personalized, frictionless experiences, and they’ll bounce fast if you don’t deliver.
When you design for every persona, you’re designing a product that:
Let’s be real: “accessible and adaptive” is no longer a nice-to-have—it’s a conversion booster.
Consider a fintech SaaS company using CarbonCopies AI for automated regression testing. They simulated three personas:
CarbonCopies simulated these workflows and uncovered:
Manual testing missed most of these. But automated user testing found them fast.
Unless you're running rigorous automated tests across all user types, you’re basically flying your product blind. You’re assuming your users are average, your journeys are smooth, and your edge cases are rare.
Spoiler: they’re not.
Your checkout flow might break for low-vision users. Your chatbot might loop for non-native speakers. Your “one-size-fits-most” UX might be quietly bleeding revenue.
And that’s the rub. When we design for the average, we alienate the many.
If fighter pilots couldn't fit into a cockpit designed for the average, why do we still expect digital users to thrive in one-size-fits-all experiences?
Embrace the variability. Validate every journey. Automate every edge case.
With automated user testing and personalization validation, you’re not just testing your product—you’re proving that it works for real people.
Not just the mythical average.