Stop Designing for the Average User
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.
References
- Rose, T. (2016, January 16). When U.S. air force discovered the flaw of averages. Toronto Star. https://www.thestar.com/news/insight/when-u-s-air-force-discovered-the-flaw-of-averages/article_e3231734-e5da-5bf5-9496-a34e52d60bd9.html
- Norman, D. A. (2013). The design of everyday things. MIT Press.
- Salminen, J., Wenyun Guan, K., Jung, S. G., & Jansen, B. (2022, April). Use cases for design personas: A systematic review and new frontiers. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1-21). https://doi.org/10.1145/3491102.3517589
- Segment. (2021). 2021 State of personalization report. https://segment.com/state-of-personalization-report-2021/
- Borg, K., Lindsay, J., & Curtis, J. (2021). Targeted change: Using behavioral segmentation to identify and understand plastic consumers and how they respond to media communications. Environmental Communication, 15(8), 1109–1126. https://doi.org/10.1080/17524032.2021.1956558
- Nkechika, C. G. (2022). Digital financial services and financial inclusion in Nigeria: Milestones and new directions. Central Bank of Nigeria Economic and Financial Review, 60(4), 151–170. https://dc.cbn.gov.ng/efr/vol60/iss4/12/
- Toxboe, A. (2023, April 6). Designing for change: Using the COM-B model to drive behavior change. Ui-patterns.com. https://ui-patterns.com/blog/designing-for-change-using-the-com-b-model-to-drive-behavior-change
- Fogg, B. J. (2009, April). A behavior model for persuasive design. In Proceedings of the 4th International Conference on Persuasive Technology (pp. 1-7). https://doi.org/10.1145/1541948.1541999
- SalesHub. (2025, September 12). AI market segmentation that actually drives sales (real data speaks). https://www.saleshub.ca/ai-market-segmentation-that-actually-drives-sales-real-data-speaks/
- United States Artificial Intelligence Institute (USAII®). (2024, November 15). Ethical considerations in AI-driven customer segmentation. https://www.usaii.org/ai-insights/ethical-considerations-in-ai-driven-customer-segmentation
About the Author
Kira Warje
Kira holds a degree in Psychology with an extended minor in Anthropology. Fascinated by all things human, she has written extensively on cognition and mental health, often leveraging insights about the human mind to craft actionable marketing content for brands. She loves talking about human quirks and motivations, driven by the belief that behavioural science can help us all lead healthier, happier, and more sustainable lives. Occasionally, Kira dabbles in web development and enjoys learning about the synergy between psychology and UX design.
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