Content object signals: Aligning inventory transparency, data-driven buying, and yields
By Catherine Dale, VP of Magnite Streaming at Magnite
TV buyers are leaning into streaming advertising that combines the best of digital and linear including scalable, replicable and transparent ways of targeting audiences. Leveraging content object signals in the bid stream in a controlled and structured manner can facilitate efficient and effective programmatic CTV buying for buyers that can positively impact yield for media owners.
According to Magnite research, 81% of consumers in US and Canada are watching streaming TV with 50% of non-streamers likely to begin streaming within the next 6-12 months. As this growth continues, so too do buyers’ preferences in being able to target specific genres, shows, and episodes to advertise against for brand safety, relevancy and performance.
The metadata universally available on linear buying channels hasn’t always been available for programmatic CTV, forcing buyers and publishers to use manual workarounds to provide basic transparency into video content. In programmatic CTV, we have contextual data points referred to as content object signals, an openRTB protocol that allows media owners to define elements such as genre, category, and content rating. However it is only more recently that these signals have come into popularity in streaming as a way to give buyers more transparency to make more informed buying decisions and improve campaign performance, bringing the best of linear advertising to the programmatic CTV space.
Solving key challenges
Although content object signals are often shared post-campaign so buyers know where their ads ran, publishers have been apprehensive about sharing content object data for ad placement decisioning. Historically, there’s been a concern that doing so would lead to the cherry-picking of premium inventory leaving media owners with undervalued and overlooked inventory. However, in many cases it creates a more holistic view of inventory which we’ve seen translate into more buyer confidence and additional spend on publishers sharing data.
Making this data available and effective for audience targeting requires workflows to structure, standardize and connect the data between buyer and seller. As media owners increasingly leverage these signals in biddable environments, buyers will be willing to spend more – as they can do smarter, data-driven optimization – to find their audiences, which helps to drive publisher yields.
Content object signals can be a key contributor in the mission to resolve these challenges around targeting, measurement and brand safety, but there’s still work to be done. For instance, while OpenRTB provides a set of standard content parameters, a standardized taxonomy for metadata is the next step toward scaled buying efficiency across media owners.
Transparency as table stakes
Buyers want to know what inventory and audiences are available to them and what content they’re buying against to ensure brand safety and relevancy and to optimize campaigns. ‘Do not air’ lists, for instance, allow brands to avoid targeting specific content based on rating and other content parameters – such as mature/adult content – enabling optionality for buyers to buy at scale with confidence. As brands continue to explore CTV they want a more holistic view of their options in order to achieve the levels of trust and transparency seen in linear, that will allow them to increase and optimize their CTV budgets efficiently and effectively.
Content object signals: A win-win
Media owners are more able and willing to divulge data to meet buyer demands encouraged by the promise of a more holistic yield and a larger share of advertisers’ budgets. Enabling more accessible, scalable and effective content-targeted programmatic CTV will bring a host of benefits for buyers and media owners.
For media owners it can unlock new and incremental increases in demand as well as a willingness to pay a premium as buyers become more comfortable with programmatic CTV buying on the same level of transparency as linear. This can fuel media owners’ need for streamlined curation; packaging up contextually-fuelled inventory – into programmatic guaranteed deals for instance – to help media owners maintain more control of pricing. Using content metadata can also simplify media owners’ tech stack setups and ability to pass data dynamically without creating additional lines.
For buyers the greater targeting control and transparency can provide opportunities to scale campaigns across publishers while ensuring their ads align with brand safe and relevant content. Content object signals enable CTV buys to harness the strengths of both the linear and digital worlds – rich content information on one side and strong impression-level data on the other. In doing so, programmatic CTV can help campaigns that span linear TV and digital video become more cohesive across channels.
Furthermore, the insights that content object signals provide fuels optimization and smarter, working media spend, across new and existing inventory opportunities, aligning the brand message with increased ROI and greater engagement.
While content metadata has a large place in advancing industry transparency, Magnite acknowledges and supports other strategies through tech optionality, putting the power in the publishers’ hands to decide what data is shared. For instance, Magnite Data Lock offers publishers more control over their inventory with robust transparency settings to control the usage of user and content data on a deal-by-deal basis. By providing the tools and tech to connect the buy and sell side, Magnite is helping publishers maximize the revenue for their inventory while providing insights that brands need to feel more comfortable increasing their spend.