Last month, Unilever CMO Keith Weed addressed the IAB Annual leadership conference and urged brands to work with digital platforms to ensure messaging happens in a “brand-suitable” environment.
And considering 2017 was the first time that digital ad spending collectively surpassed TV, according to Recode, brands need innovative solutions to surface safe spaces to align their messaging in a changing digital video ecosystem.
Here’s just a quick summary of brand safety concerns from marketers in recent months:
November 2017 – A Teads survey of 104 CMOs and Senior Executives at US brands found that more than three quarters (78%) of marketing heads say they have become more concerned about brand safety in the last 12 months
December 2017 – A survey of 30 brand marketers by Digiday+ showed that brands place more responsibility on themselves than on agencies, vendors or publishers, when it comes to maintaining brand safety.
January 2018 – Logan Paul posts video the now-infamous “Suicide Forest” on YouTube, sparking outrage from pretty much everyone, consumers, viewers, advertisers, you name it.
February 2018 – Logan Paul is suspended and later removed from YouTube’s monetization program. YouTube issues new monetization rules and guidelines for channels who create content that would be deemed brand unsafe.
Despite these calls from the industry, YouTube’s top accounts still are littered with unsafe or unsavory content. Marketers are trading scale for safety, and it’s time to put technology to use to meet the needs to a changing video advertising ecosystem.
Thanks to innovations in computer vision and AI, detection of content and themes inside of videos possible to scale in a way never before.
Delmondo used Uru’s Brand Safety API to automatically look inside the video and audio of recent uploads on some of YouTube’s top influencer channels in a new in-depth report, which you can download here.
We analyzed the 25 most recent videos from the 17 top influencers on Youtube, using SocialBlade’s ranking of global YouTube channels by subscribers and disregarding those that belong to mainstream music artists.
Uru uses a combination of computer vision, natural language processing, and other artificial intelligence to generate its proprietary video brand safety scores.
Specifically, they use these tools to scour the video’s audio and visual data, looking for a long list of brand safety red flags. Each of these red flags is assigned a particular weight based on our in-depth field research with brands, learning the types of content they wish to avoid sponsoring and/or being associated with:
⁃ Unsafe objects and themes such as weapons, drugs, terrorism, and celebrity scandals;
⁃ Unsafe language such as profanities, misogyny, and hate speech;
⁃ Paid or sponsored content;
⁃ Negative sentiment;
⁃ Not safe for work material (nudity and extreme violence).
Every inputted video starts with a brand safety score of 1, meaning that it is totally safe video. But for each occurrence of a red flag that Uru finds, that video is penalized based on the weight assigned to that red flag. The lowest brand safety score possible is 0, meaning that it is a completely unsafe video.
Here are some of our findings
1. Surprisingly, despite 2017’s numerous brand safety scandals, YouTube’s most popular influencer channels still contain a lot of content that would be deemed brand-unsafe. Based on our analysis of the 25 most recently uploaded videos on each channel, content on these channels is still more likely to be brand-safe than brand-unsafe, but only by a small margin.
2. YouTubers use a lot of bad language. More specifically, YouTube’s most popular channels still contain unsafe language (such as profanities, misogyny, and hate speech), negative sentiment, and unsafe objects and themes (such as firearms and graphic violence).
Of the videos we examined,
– 67% contained unsafe language
– 61% contained negative sentiment
– 16% referenced or showed firearms
Even more, a surprising amount (higher than 15% on one popular channel) contained graphic violence (usually from gory video game footage).
3. Engagement thrives in brand-safe environments. The audience clearly cares about brand safety and it’s reflected clearly in the data. While you might think that divide speech, or unsafe themes might be more provocative in sparking conversation, it’s clear here that the YouTube audience doesn’t respond well to this and those creators ultimately received fewer views and engagement.
Of the videos we examined:
Videos from creators with Brand Safety Scores greater than .7 generated:
⁃ 38% higher average views per video
⁃ 73% higher average engagements per video
Videos from creators with Brand Safety Scores less than .7 generated:
⁃ 26% fewer average views per video
⁃ 51% fewer average engagements per video
You can see that creators who are known for more unsafe content or language in their videos tend to have overall less engagement than those creators who are known for more content thought of as “safe.”
4. Computer vision, machine learning and artificial intelligence working together with one another will be an important tool for scalable detection of brand safety in YouTube videos in near real-time in order to help advertisers monitor the influencers they sponsor and also help them decide which content on the platform is safe for them to target with marketing and ads.
Brand Safety is not just important for brand reputation and awareness, it’s crucial because all brands are becoming direct-to-consumer brands, using first-party data to fuel the feedback loops coming in that impact all facets of business.
Without trusted relationships with consumers, they won’t be as willing to part with that data. So both sellers and buyers need to work in concert to maintain this balance.