Case Study Speaker Feature Matrix
Project Summary
Led product design for a feature that visualizes speaker sound profiles by combining AI-driven sentiment analysis and expert insights. Collaborated with data scientists and AI teams to translate qualitative customer reviews into a structured, easy-to-understand model resulting in increased conversions and speaker sales.
Key Highlights
- Applied AI to extract sentiment and attribute data from thousands of customer reviews
- Collaborated with audio experts to define and validate core sound characteristics
- Designed clear visual indicators (icons, graphs) to support quick product comparison
- Ran usability testing to refine the design and ensure intuitive user comprehension
- A/B tested the final design, resulting in a measurable lift in conversions
Process
We began by analyzing reviews, frequency response data, and product specs to uncover the attributes that truly shaped speaker performance. We partnered with in-house audio experts to validate our assumptions and prioritize what mattered most to customers. From there, I led the creation of early prototypes and conducted user testing to evaluate clarity, usefulness, and comprehension.
AI tools played a key role in surfacing sentiment trends and mapping subjective customer language into structured, comparable attributes. The result was a data-informed UX approach that balanced technical accuracy with simplicity and user trust.
Results
The final UI allowed customers to explore and compare speakers based on sound profiles using a combination of visual cues, simplified graphs, and review summaries. Users reported that the tool helped them narrow choices faster and feel more confident in their purchases. A/B tests on product pages showed a strong conversion lift and reinforced the value of combining customer voice, expert validation, and thoughtful design.