Etsy · Thematic Analysis · Marketplace
A thematic analysis engagement for the Etsy Seller App — coding feedback across multiple data sources to surface the pain points that mattered, and structuring the results so the listening could continue long after the study ended.
Etsy's sellers are prolific with feedback — reviews, support signals, in-app comments — but volume isn't the same as insight. Feedback about the Seller App arrived continuously across multiple data sources, and the patterns that should drive design priorities were buried inside it. Etsy needed both an immediate answer (what's hurting sellers right now?) and a lasting one (how do we keep hearing them without repeating this project every year?).
We conducted qualitative thematic coding across more than 1,000 pieces of seller feedback drawn from multiple data sources, building a taxonomy of pain points and opportunities across seller experience touchpoints. To ground the themes in observed behavior, we supplemented the coding with discovery sessions and usability evaluations conducted via UserTesting.
The highest-value finding wasn't any single pain point — it was that seller feedback clustered reliably into themes that could be operationalized. Once the taxonomy existed, new feedback could be classified against it, which meant the analysis didn't have to end when the engagement did. Structure is what turns feedback from anecdote into evidence.
Findings informed Etsy's Seller App design improvements, and the thematic structure became the foundation for a data model supporting ongoing, AI-driven analysis of customer feedback — moving Etsy from periodic listening projects toward continuous, LLM-powered feedback monitoring.
A thousand pieces of feedback is noise until someone builds the structure that lets a team hear it.