Brand Safety Scope, Metrics, and Measurement
Introduction
Brand Safety Metrics and Measurement sit on a hierarchy of probable quantification of consumer interactions and dynamics. The specific dynamics — How many? How many times? How long? In what sequence? At what cost? etc. — form our basis for understanding certain dimensions of value for the media and the efforts involved to make those interaction brand safe.
Brands safety metrics are largely focused in two areas.
Media Quality
Where brands try to estimate the value delivered in actual human eyeballs or the avoidance of fraud, invalid traffic, and (non-)viewability issues. Each of these areas can be quantified to some level of certainty through vendors and analysis to determine how many of the brand’s media dollars resulted in a verified human connection. Although these metrics are largely “sampled” and more easily benchmarked through a verification supplier (IAS, DV, Pixelate, etc.), they use technology to qualify and quantify exposure and manage down non-human event in campaigns.
Brand Safety and Suitability
This metric is more elusive and non-binary. Although the majority of the industry reports having a good understanding of brand safety, and what inventory should not be made available to advertisers, the notion of what is suitable for one advertiser versus another is a source of continuous debate along the industry’s 13 key brand safety categories. The industry has collaborated on these key categories and continues to focus on how we might more consistently be able to repeatedly and consistently define content as High, Medium, and Low Risk adjacency opportunities.
Management of your adjacencies in the media is seen as a critical way to manage your brand’s reputation or avoid risky opportunities that negatively impact consumer perception. Many studies over recent years suggest that roughly 70-80% of consumers are negatively impacted by seeing an ad next to inappropriate content.
Consumers have a perspective that all brands have a say in where their ads appear, therefore offensive adjacencies reflect harmfully on the brands. In a digitized media ecosystem where voluminous trading is happening in milliseconds, this is a true challenge. For this reason, several efforts are underway – most using the power of AI/LLM’s to capture more data, consistently analyze that data, and provide brands with the opportunity to respond in near real time to content alignment that might be questionable.
Foundational Definitions
General Invalid Traffic Traffic that comes from known, nonhuman sources on publicly available IP lists. It can be identified through routine means of filtration by verification tools. Some GIVT is legitimate, some GIVT is malicious.
Sophisticated Invalid Traffic Nonhuman traffic that is more difficult to detect than General Invalid Traffic (GIVT), and requires advanced analytics, multipoint corroboration/coordination, or significant human intervention to analyze and identify.
Viewability Viewability is an online advertising metric that measures the number of impressions viewed by real, human users. A served impression does not necessarily count as a viewed impression, if it is served outside a live window or is served to a bot.
Ad Adjacency Ad adjacency — or more specifically, an ad's placement, or misplacement, next to unsafe content — is advertising appearing next to or in front of content the marketer deems inappropriate for their brand. The same holds true for publishers where the adjacency of ads that are not brand safe on their pages can find their reputations damaged.