By Jessica Miles
A brand is a fundamental part of a business. It shapes identity, builds trust and recognition among consumers, and is the driving force behind customer loyalty. As consumers increasingly aim to choose brands that align with their personal values, it’s more imperative than ever for marketers to protect their brand image and reputation to ensure it reflects the personal values their consumers hold. Because of this growing discernment among consumers, it’s no surprise that brand safety and suitability are at the forefront of marketers’ minds.
Balancing risk mitigation while also scaling a brand that drives results is a significant challenge as marketers double down on brand safety and suitability to maximise the impact of their digital campaigns.
Here’s the good news for marketers who want to attain both these objectives — you do have the ability to both mitigate brand risk and drive return on ad spend (ROAS). By pairing brand safety and suitability parameters with contextual adjacencies that are appropriate and relevant to consumers, marketers can meet both needs simultaneously.
- Scaling with safety and suitability
Executing a successful brand safety and suitability strategy can require a different approach across digital channels and environments. In fact, brand risk is among the top challenges media experts identified across social media, CTV, and open web.
Percentage of media experts who agree that brand risk is a challenge for the channel/ environment:
Minimising risk is a top priority, and marketers are taking a more proactive approach to ensure brand safety and suitability of their ad placements across digital environments. In the early days of brand safety, digital marketers relied heavily on keyword blocking to protect their brands because it was the only option. Safety and suitability solutions have evolved greatly since then, and there are now more advanced solutions available to accurately protect your brand without sacrificing scale.
Keywords are too blunt to accurately block unsafe and unsuitable content for brands. Also, keyword blocking poses significant challenges that can prevent marketers from meeting key marketing objectives such as protecting the brand, improving ROAS, increasing efficiencies, and reaching diverse audiences.
To overcome these challenges, marketers need to adopt a contextual approach that analyses the entire page to understand if the content is safe and suitable for a brand on a holistic level. Marketers can look at context through a consumer lens as an effective suitability strategy that mitigates brand risk while also addressing the challenge of scale.
- Contextual (NLP and Sentiment)
Contextual strategies improve consumer perception and minimise brand risk while opening up opportunities to advertise on a broader set of content. Understanding how to account for context, especially from a consumer perspective, can ensure a holistic safety and suitability approach for marketers. Consumers build their perceptions about brands in conjunction with the content and environment where advertising appears, and prefer that ads appear near relevant and positive or neutral content.
With the use of semantic technology and natural language processing (NLP), Integral Ad Science (IAS) has built brand safety and suitability products that account for various contextual signals, processing data to provide human-like machine comprehension of content — including sentiment. Boasting a large number of off-the-shelf vertical and topical segments and brand-specific segments for content avoidance as well as for targeting across both direct and programmatic buys, IAS’s technology can access the true meaning of content by understanding the specific emotions intended by the creator or author of a page.
These data-backed approaches help ensure safe and suitable ad placements and arm marketers with actionable data to optimise and scale performance. With contextual targeting, marketers can unlock scale and efficiency while driving consumer favorability.
- Contextual advertising on social media platforms and user-generated content
In a rapidly evolving media landscape and booming content growth, consumers continue to spend more on social media platforms. In order to provide advertisers with more granular, comprehensive, and accurate data, it becomes imperative to expand contextual technology to cover content beyond text, including images, video, and audio.
Gone are the days of using metadata analysis since it only looks at textual information about the video (such as genres, rating, thematic elements etc.). Today, multimedia classification emerges as a revolutionary technology that promises to alleviate advertisers’ concerns. Multimedia classification technology refers to the use of artificial intelligence and machine learning algorithms to analyse and categorise multimedia content such as images, videos, and audio content. Multimedia classification granularly measures for brand safety and suitability and addresses concerns of running ads adjacent to unsuitable content. Particularly on social platforms, technology needs to keep up with the user-generated feed at scale. To put in simple terms, the difference between the two methodologies is like reading a rotten tomatoes review and actually seeing a movie.
Greater transparency empowers publishers to thoughtfully curate inventory packages and establish pricing strategies. Some of the key findings on multimedia technology from IAS’s recently published brand-safety white paper show it is:
- 10X more accurate than metadata for “Obscenity and Profanity” high risk content
- 8X more accurate than metadata for “Death Injury and Military Conflict” high risk content
- 7X more accurate than metadata for “Arms and Ammunition” high risk content
With more actionable data provided by advanced machine learning/AI technology, marketers can invest in relevant, suitable content to scale their brand with confidence.
The author is country manager, ANZ at Integral Ad Science