Historically, data vendors, DMPs and analytics providers have used custom taxonomies to describe and segment their audiences, with little or no standardization across vendors. This results in sometimes drastically different descriptions even for similar data, where vendor A could describe a segment as “Auto-intenders” and vendor B describes a similarly segmented audience as “In-market for cars”. Comparability is also made more difficult due to the fact that in house taxonomies reflect a wide range of audience segmentation approaches, including demographic (age, gender, household income, etc), interest-based, purchase-intent based, psychographic, and many more.
With the introduction of IAB Tech Lab’s Audience Taxonomy, the industry now has a common nomenclature for audience segment names to improve the comparability of data across different providers. It is a key element in IAB Tech Lab’s Data Transparency Standard (datalabel.org), which facilitates consistent labeling of audience data by first-party and third-party sources. The Audience Taxonomy also provides a mechanism to make segmentation approaches much clearer (categorically) by introducing Tier 1 level labeling that designates whether the segment describes attribution that is demographic, interest-based, or purchase-intent based.