When we are faced with a large amount of text, most of us do the same thing instinctively. We try to get the big picture before understanding every detail. Imagine scrolling through hundreds of customer reviews for a product you are thinking of buying. You do not read every review carefully. You skim, you scan, and very quickly you get a sense that people are mostly complaining about battery life, praising the design, and arguing about the price. That rough summary forms almo

One of the most common instincts in research is to ask people exactly what they think. We ask users why they chose a specific product, what they liked about an interface, what confused them during a task, what mattered most to their workflow, and what ultimately influenced their final decision. This approach feels intuitive, respectful, and efficient; after all, who knows the user better than the user themselves? Yet, decades of psychological and behavioral research point to

Most product teams still assume that if a feature ships, users will naturally discover it. In practice that assumption fails all the time. New capabilities launch, engineering and design celebrate, dashboards show a small bump in traffic, and then adoption plateaus at a level that cannot possibly justify the investment. The real problem is usually not usability in the narrow sense. It is feature awareness: do people even notice that the thing exists, and do they understand wh

Interviews are like a gold mine, but only if you actually know how to extract the ore. Most teams stop at we talked to users and walk away with a handful of quotes and a gut feeling. Raw transcripts alone are not insight. They are messy, biased, emotionally charged human data that only become valuable through rigorous analysis. As a cognitive scientist and UX researcher who cares deeply about methodological quality, I see this mistake constantly. Great interview work is not d