A quick exploration into the delicate balance between relying on instinctive understanding (intuition) and gathering deep understanding through analysis and observation (insight) in digital product design.
In our industry, the allure of the "design process" is undeniable. For every problem, question, quandary or goal, there seems to be a ready out-of-the-box framework, model or procedure. We gladly workshop any idea until we're absolutely sure it's perfect, until everyone put their two cents in. Guided by the elegance of statistics and metrics, we move forward towards final designs only when we’re certain of success.
Outside of big corporations and big budgets, among startups, grassroots initiatives, it doesn’t work that way. More often than not, there isn't time to go through a proper design process, data available is either scarce, old, unusable or just non-existent. Research is whatever we can do during our lunch break. Budgets don't include testing. And yet, we create.
How to walk that thin line between being a craftsman and an artist? When to dig for data and when to trust your gut? Learn from my mistakes.
Watch this WaysConf session, then continue with related talks or explore the current programme.
This talk challenges the false choice between design intuition and customer insight. Experience creates useful pattern recognition, but it also creates confidence in assumptions that may not fit the present context. Research is most valuable where consequences are uncertain, surprising, or costly, while established principles can support lower-risk decisions without ritual validation. Through product examples and a discussion of design trends, the talk argues for calibrated judgment: state the assumption, measure its impact, investigate contradictions, and learn from being wrong.
Experience becomes valuable when designers study failed decisions and update their mental models instead of merely accumulating years.
Watch from 2:30A branded method cannot replace thinking; teams should choose activities because they reduce a specific uncertainty.
Watch from 8:03Typography, perception, and common interaction principles do not need to be rediscovered for every ordinary product decision.
Watch from 13:47A strong order metric concealed widespread ride abandonment, and customer conversations revealed the missing explanation.
Watch from 19:29A small percentage change can represent major impact at scale, so high-consequence intuition deserves stronger validation.
Watch from 25:26These concise notes were edited from automatic captions and checked against the talk structure. They are not a verbatim transcript.
Senior judgment often feels immediate because many prior situations have been condensed into recognizable patterns. That does not make it magical or automatically correct. The useful part of experience is the record of mistakes, consequences, and corrections. Designers should be able to explain which pattern they recognize and what evidence would show that this context is different.
Watch from 2:30Teams can become attached to a named process and run its stages regardless of the question. Research and workshops then perform legitimacy rather than produce learning. Start by identifying what is unknown, how consequential the uncertainty is, and which evidence could change the decision. The lightest method that answers that question is enough.
Watch from 8:03Designers inherit substantial knowledge about reading, perception, feedback, and interaction. Repeating foundational research for every low-risk choice wastes time and can weaken professional accountability. Use established principles confidently where the context is ordinary and the decision reversible, while remaining alert to accessibility, culture, and product-specific constraints.
Watch from 13:47A successful order event did not guarantee a completed ride. A large share of customers initiated the service and then disappeared, exposing the limits of the funnel definition. Conversations with customers helped reveal what the event data could not explain. Quantitative evidence located the anomaly; qualitative inquiry made the behavior understandable.
Watch from 19:29The same uncertain decision carries different risk in a small experiment and a platform operating at enormous scale. A fraction of a percentage point can represent millions in revenue or widespread customer harm. Teams should therefore increase the strength of review, measurement, and experimentation as the cost and irreversibility of being wrong increase.
Watch from 25:26
