Ghost in the Machine: How Taxonomic Metadata Allows for Scaling Ethnographic Insights into Search Algorithms

Abstract

This case study explores how we personalized search results by turning ethnographic insights into taxonomic metadata, which in turn allowed us to use quantitative methods to assess business impact. The first part of the case study focuses on the problem we were trying to solve—creating better search results for nurses—and using ethnographic interviews to understand how nurses approached looking for jobs. The second part of the study dives more deeply into how metadata works, and why it was the perfect partner for capturing our ethnographic findings and making them into a scalable and measurable part of the design process. The third part of the study details how we tested and scaled our designs in the live project, and why we believe others might benefit from using a similar approach. Keywords: mental models, taxonomy, business impact.