Quantitative ethnography is the niche subfield you’ve never heard of, but it’s one you’ve been increasingly pressured to practice in over the past couple of years. It’s the math that turns words into numbers underlying generative AI, and LLMs have been getting in between you and a radically new approach to working with verbatims, transcripts, and other texts.<\/p>\n
Business stakeholders are always pushing for greater efficiency, faster turnarounds. Qualitative researchers are always looking for more contact with users, and greater engagement with findings and reporting. Quantitative ethnography (and epistemic network analysis) offers a compromise: by trading structure and semantics for human sensemaking in the analysis part of research, perhaps both groups can get what they want.<\/p>\n
I’ve had the opportunity to conduct quantitative ethnographic analyses in enterprise studies involving dozens of products, and impacting hundreds of thousands of end-users. Stakeholders were willing to accept a different kind of analysis, and engage more deeply with the process, in exchange for quicker answers.<\/p>\n
In this talk, I’ll share how quantitative ethnography differs from qualitative ethnography, the tradeoffs you’ll have to make, and the kinds of results you can expect. This isn’t a tools talk, but you won’t need to do any math, either. I’ll close with a look into the near future, one where you can talk with as many users as will take your call with effectively zero additional analysis work; where you can have the analysis running live during your session, and have the user participate in the sensemaking process on-the-fly; and the dream of every product manager, one where stakeholders can have dashboards of evidence updated live as users talk.<\/p>\n","protected":false},"excerpt":{"rendered":"
Quantitative ethnography is the niche subfield you’ve never heard of, but it’s one you’ve been increasingly pressured to practice in over the past couple of years. It’s the math that turns words into numbers underlying generative AI, and LLMs have been getting in between you and a radically new approach to working with verbatims, transcripts, […]<\/p>\n","protected":false},"author":219819,"featured_media":3564,"template":"","session-theme":[34],"session-type":[25],"rm-permissions":[],"event-type":[],"class_list":["post-1139","sessions","type-sessions","status-publish","has-post-thumbnail","hentry","session-theme-advancing-research-tools-and-methods","session-type-conference-session"],"acf":[],"_links":{"self":[{"href":"https:\/\/rosenfeldmedia.com\/advancing-research\/wp-json\/wp\/v2\/sessions\/1139","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/rosenfeldmedia.com\/advancing-research\/wp-json\/wp\/v2\/sessions"}],"about":[{"href":"https:\/\/rosenfeldmedia.com\/advancing-research\/wp-json\/wp\/v2\/types\/sessions"}],"author":[{"embeddable":true,"href":"https:\/\/rosenfeldmedia.com\/advancing-research\/wp-json\/wp\/v2\/users\/219819"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/rosenfeldmedia.com\/advancing-research\/wp-json\/wp\/v2\/media\/3564"}],"wp:attachment":[{"href":"https:\/\/rosenfeldmedia.com\/advancing-research\/wp-json\/wp\/v2\/media?parent=1139"}],"wp:term":[{"taxonomy":"session-theme","embeddable":true,"href":"https:\/\/rosenfeldmedia.com\/advancing-research\/wp-json\/wp\/v2\/session-theme?post=1139"},{"taxonomy":"session-type","embeddable":true,"href":"https:\/\/rosenfeldmedia.com\/advancing-research\/wp-json\/wp\/v2\/session-type?post=1139"},{"taxonomy":"rm-permissions","embeddable":true,"href":"https:\/\/rosenfeldmedia.com\/advancing-research\/wp-json\/wp\/v2\/rm-permissions?post=1139"},{"taxonomy":"event-type","embeddable":true,"href":"https:\/\/rosenfeldmedia.com\/advancing-research\/wp-json\/wp\/v2\/event-type?post=1139"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}