Dynamic Brand Safety. Death of Static White-list?
09 Apr, 2020
It takes years and significant resources for a brand to build a reputation in the minds of consumers. Not just the product or service, while choosing a product, the consumers also connect with the brands values and principals. Even a single, less than desired association, can tarnish brand reputation and consumers’ trust for a long time. However, in this era of social media and user generated content, brand safety challenges are many and highly complex.
Video platforms continue to face flak for brand safety violations
March 2017 marked the first major brand safety catastrophe in the video advertising world. Guardian, the British daily newspaper blacklisted YouTube, when ads were found alongside hate speech and extremist content. Subsequently, household brands like Toyota, Proctor & Gamble, AT&T, Verizon pulled out millions of dollars’ worth of ad spend from YouTube.
For the first time, YouTube was dealing not only with reputation damage but also revenue damage. And it wouldn’t be the last time — YouTube was continued to be blamed for hurtful brand exposure, despite introducing corrective measures throughout 2017, 2018 and 2019. Video advertisers on Facebook and Twitter also continue to face similar brand safety issues.
What creates an unsafe video environment?
Millions of dollars’ worth of video ad spend primarily finds home on social video platforms like YouTube, Facebook, Twitter, Snapchat and more. Ads are placed against user generated content, where the objective is to leverage fast churning content that users relate with. However, with 500 hours of videos uploaded to YouTube alone each minute, it is a challenge to limit ad placement to content that can be deemed suitable.
What are you up against?
We analysed ~10 million videos across video sharing and hosting platforms using Mirrors, an in-video context detection platform that can identify faces, objects, actions, scenes and more within a streaming video. We found nearly 8–9% of all content as brand unsafe, featuring content categories including violence, smoking, adult, and extremist content.
This indicates that 1 in every 9 video ad placements on video sharing platforms can be across potentially harmful content.
The following graph shows the percentage split of content featuring top brand unsafe categories within the harmful content identified using Mirrors.
Why are brand safety measures not working?
Static Keyword based filters and white-listed channels are not cutting it
Advertisers, eager to mitigate brand safety risk, turn to keyword blacklists as the primary solution. Lists can range from 2,000 to 4000 unsafe keywords, used as a filtration mechanism to detect and block harmful video content. However, keyword blacklists cannot guarantee complete protection, and often fail to understand the complex undertones and various contexts a single word can be used for.
A video featuring smoking or violence might not be described so in its title, description or meta tags. There is no way for keyword-based solutions to identify these damaging contexts to filter out this video. Which can lead to household brands advertising across content which is highly unsuitable for their brand image.
And white-listed channels, another widely adopted brand safety measure, are more expensive and highly monitored. Featuring carefully curated content, these channels offer advertisers an experience that mimics regulated, brand-safe, broadcast network TV channels.
However, this limits the true essence of a social platform, and might not really be enabling the reach to the right audience. The cost of running ads using these channels is significantly higher, not producing a ROI commensurate with the investment.
Brand safety is not one size fits all
Each brand is different and must define its own guidelines for inappropriate and damaging context in accordance with its specific needs, values and brand image. And brand safety measures and tools should be able to provide required controls to amplify or lower restrictions to allow a highly customized approach.
What is unsafe content for one brand, can be the exact context that can help another brand reach their most relevant audience.
Blanket restrictions based on static keywords lists and white-listed channels lack the flexibility to offer custom controls, a key factor why brands are often forced to switch off controls in favor of ROI, even staking brand equity and reputation which takes years to build.
Can context relevance be the answer?
One of the key challenges with traditional brand safety tools is the inability to identify the right context. Emerging AI powered solutions are increasingly focusing on providing context relevance, and are fast becoming an answer to brand safety woes. AI enables processing of large volumes of data at speed, with better context, at higher scale and improved targeting efficiencies.
However, most of these solutions still depend on use of NLP and semantic analysis, not truly understanding the sub-text, nuanced contexts, and complex relationship words have in written or spoken language.
Artificial intelligence powered solutions such as computer vision are increasingly making it possible to detect in-video context with great accuracy, offering unparalleled insight for advertisers to place context-relevant video ads in a highly structured manner, and at the scale programmatic has traditionally offered.
What is computer vision?
Computer vision is an AI powered technology that imbibes the complexity of the human vision system, enabling computers to ‘see’ i.e. identify and process objects in visual content including images and streaming videos. Owing to AI advancements, computer vision has even surpassed humans in detecting certain objects.
Most computer vision algorithms can just detect objects, mostly within static images. But more advanced algorithms have been able to accurately identify people, facial expressions, activities, scenes, and even emotions, not only in static images but also in streaming video content.
Computer vision enabled in-video context detection offers a high degree of context relevance that surpasses limitations of traditional keyword and affinity-based targeting.
Computer vision’s AI powered in-video context detection can accurately block ad placements against unwanted, unsuitable, irrelevant and harmful content. These solutions can provide the highest accuracy, not letting damaging ad placements pass through by using frame by frame parsing of video content.
In-video context detection enables true brand suitability. Every brand is different and so are specific brand safety needs. Computer vision offers a tailored approach, offering absolute brand control: unlike keyword blacklists and white-listed channels, that most likely also block perfectly safe content.
Dynamic brand safety, with higher and more relevant reach
In-video context detection opens a whole new set of audience to improve reach, with unparalleled brand safety. The ad has a higher probability to match its environment in terms of context and messaging. It runs on the principal that users are engaging with their interests while consuming certain content, and engaging at the right moment can augment this experience and gain interest and trust.
With brand safety as a key consideration, an advertisement when served in a context that matches the content, is more likely to achieve increased clicks, views, and completed views.
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