Dyson
From "It Sounds Cheap" to Engineering Specs
How I Turned Subjective Sound Perceptions into Objective Design Targets
The Challenge
While developing the Dyson Supersonic hair dryer, our analysis of thousands of competitor reviews showed that sound quality was make-or-break for users - it could either elevate the experience or create a major pain point. The problem was that traditional acoustic measurements told us nothing about how users actually perceived sound. We needed to bridge the gap between human emotional responses to sound and technical engineering specifications that could guide development of a premium product.
What I Did & The Outcome
I designed controlled experiments to map subjective sound perceptions to objective acoustic properties, using statistical analysis to identify which technical characteristics drove positive or negative user responses. This methodology guided engineering decisions throughout development and became the standard approach for acoustic target setting across all Dyson product categories. Essentially, solved the fundamental research challenge of turning "it sounds cheap" into measurable engineering targets that could be built to.
Approach
Designing Controlled Sound Experiments
I created controlled focus groups using eight carefully engineered sound samples that varied across key acoustic properties. This let me systematically explore the relationship between technical sound characteristics and user emotional responses rather than relying on guesswork.
Capturing Multi-Dimensional Data
I collected both qualitative and quantitative data streams
01
Natural language analysis
Documented how users actually described each sound
02
Emotional response mapping
Recorded reactions like "powerful," "pleasant," "calming"
03
Pattern identification
Analyzed frequency and sentiment of descriptions across user groups
Statistical Analysis for Insight Translation
I performed Principal Component Analysis to identify correlations between user perceptions and underlying sound properties, transforming subjective responses into quantifiable patterns that engineers could actually use.
Engineering Collaboration and Translation
I worked closely with acoustic engineers to map user perception patterns against technical metrics, creating a bridge between research insights and engineering specifications that teams could act on.
Impact & Results
Product and Organizational Impact
Pioneering specification guidelines
Linked emotional experience goals to measurable technical parameters
Methodology adoption
Became the standard guideline for acoustic target setting across all Dyson products
Research elevation
Demonstrated advanced research abilities combining qualitative insights with statistical categories