One-line takeaway: Moving beyond median profiles and designing for real behavioral clusters ensures products truly fit the needs, abilities, and motivations of the people using them.
In the late 1940s, the United States Air Force stumbled upon a serious problem as pilots struggled to control the new, faster jet-powered planes. At worst, these planes crashed 17 times in a single day.1 The culprit? The cockpits were designed to fit the physical dimensions of the average male pilot. When researchers measured over 4,000 pilots to update the cockpit design, not a single one fell within the average measurement range. A design meant to fit everyone ended up fitting no one.
The Air Force’s fix was to create an adjustable system, where each pilot could tailor cockpit components to their individual dimensions. Several decades later, UX designers are embracing a similar shift in philosophy. Just as no “average” cockpit fits every pilot perfectly, no digital product works the same for everyone. You wouldn’t design a shirt for the average body and expect it to fit everyone. Why, then, do we create digital products for the “average user?”
This is far from a revelation, as Don Norman, the Father of User Experience (UX), writes in The Design of Everyday Things that there is no such thing as the average person.2 And he couldn’t be more right. People have fundamentally different goals, preferences, abilities, and needs. Ironically, the concept of the typical user—which attempts to corral all these human differences into one neat little bubble—removes the human element from design. In an attempt to simplify complex human traits into something understandable, we fail to capture the true diversity of real, everyday users.
Instead of creating rigid products that fit the narrow ideal of the hypothetical middle, what if we started designing digital tools to fit the unique behavioral dimensions of individual users? This article explores how designing for real behavioral clusters can boost engagement, improve inclusive design, and reveal transformative solutions to address unmet user needs.
Why Demographics Miss the Mark
For years, designers have relied on demographic profiles to understand their users. After all, information about a user’s age, income, or occupation seems to tell a lot about their motivations and needs. The problem is that user personas, while valuable for empathizing with user subsets, often assume these static demographic profiles behave consistently.3 But these narrative characters are just that: works of fiction. The data tell a different story.
Even when brands design products for distinct user groups, surface-level demographics miss the mark, and users can feel it—85% of business leaders think they’re delivering personalization, but only 60% of consumers agree.4 These stats reveal a gap between how brands define personalization and how users experience it. A group of users might share the same zip code, income level, and age, but behavioral patterns often cut across these demographics. One user might research their options carefully, while another clicks impulsively. One needs help navigating complex interfaces, while another wants more control over their experience.
Demographic personas alone rarely capture these subtle differences, leaving critical user needs unmet. As much as we like to simplify and categorize, real human behavior just doesn’t fit neatly into nice little boxes.
Designing for Real People
Behavioral segmentation—grouping users by what they do rather than who they are—has become central to modern UX design.5 This perspective acknowledges that no two people think, click, or engage the same way. Some make decisions carefully, others act intuitively. Some focus on risk, others focus on reward.
Behavioral segmentation groups users based on observed behavioral patterns, which can reveal valuable information about individual motivations, abilities, and susceptibility to friction. It means designing for “habitual forgetters” or “motivated planners” rather than “young professionals” or “millennial parents.” This allows designers to create user journeys that align with how users typically interact with online tools and make decisions in digital spaces. The question now shifts from “who are these users?” to “what do these users do?”
By removing frictions, tapping into hidden motivations, and addressing unmet psychological needs, behavioral segmentation can unlock new, underserved markets. Whether it’s a project management tool or banking app, products designed for real behavioral clusters engage users who can’t find what they need from those designed for the elusive “average.”
When fintech firms began introducing mobile banking tools in Nigeria, potential users were hesitant to give them a try. On paper, the promise was clear: expand access to valuable financial services for a traditionally underserved market. But users struggled with the digital interfaces and found it hard to trust online tools, preferring instead to stick with familiar cash-based systems.6
To improve adoption rates and close market gaps, firms have started observing the behaviors and attitudes of potential users. By identifying user groups based on behavioral factors like savings habits, level of digital trust, or transaction patterns, banking companies can tailor digital experiences and financial options to individuals. Researchers are confident that these tailored services would deliver better inclusion than one-size-fits-all services.6 Moving forward, achieving true inclusivity in the underserved market will depend on customer-centered design and iterative research into each customer segment's needs. As the story so often goes, inclusivity is not about designing for demographics, but for behavior.
How Design Teams Are Unlocking Behavioral Clusters
Behavioral segmentation is about observing real people and looking for patterns in how they act. It’s like watching people on the beach and categorizing them based on what they’re doing rather than how they look. Some bake under the hot sun, some shelter under umbrellas—each wanting something different from their beach-going experience. Rather than predicting what people will do based on who they are, uncovering behavioral clusters means watching what people do and designing experiences that fit these real behaviors.
Some designers are also exploring the use of behavioral frameworks to reveal behavioral segments. For example, the Fogg Behavior Model (FBM) provides a lens to understand why people do the things they do. Frameworks like these act as maps, helping designers organize users into segments based on different levels of motivation and ability.8 Suppose you’ve launched a fitness app and want users to set daily activity goals. The target behavior—setting a goal using the app—is simple for some users, but complicated for others. Motivation also varies: some want to be active right away, while others feel overwhelmed about the path ahead. By using the FBM to sort these users into behavioral clusters, designers can tweak the app design and add small, tailored prompts to nudge users toward action.
Machine learning tools are making it even easier to create and cater to behavioral segments. Clustering algorithms, for example, uncover hidden patterns in user behavior, allowing designers to tailor interfaces, prompts, reminders, and workflows to the evolving habits of real users.9 This means giving more control to users who play around in their app settings, simplifying interfaces for those who struggle with complexity, or offering regular reminders to users who respond well to routine. Good design isn’t just about making tools efficient or engaging for everyone, but about designing products that fit how real people behave.
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Behavioral Segmentation the Ethical Way
Just as cockpits designed for the average body fit no pilot perfectly, designing digital tools for the “average person” risks excluding real users. The Air Force solved its ill-fitting cockpit problem by making the controls as customizable as possible, saving several lives in the process. Behavioral design asks us to do the same for digital products—a shift that can have similarly transformative outcomes. Focusing on distinct behaviors rather than demographic averages can help designers see people as individuals rather than fixed categories, ensuring tools are accessible and inclusive.
That said, behavioral segmentation isn’t ethically fool-proof. Clustering based on behavior can unintentionally reinforce stereotypes or make biased assumptions about people's behavior.10 As designers shift away from the outdated concept of the average user and explore behavioral segmentation, a new challenge is emerging: applying behavioral insights in a way that improves inclusivity and accessibility, rather than limiting them. How can we ensure behavioral segmentation injects empathy into the design process rather than treating people as predictable data points? This is a question that designers continue to grapple with, especially with the growing role of AI in predicting user behavior.
In the end, designing products for real humans while maintaining ethical standards is a bit of a balancing act. As we challenge assumptions about the average user, think of behavioral segmentation not just as a means to open new market opportunities, but as a tool for responsible, inclusive design, where user experiences are just as diverse as the users themselves.
