Naming Conventions Aren’t Enough. It’s Time for Content IDs
Enterprise organizations are on the brink of an AI-fueled content overload. With the new capabilities of generative AI, brand and marketing teams can now create campaign assets in mere moments. The ability to instantly produce dozens of variations of messaging and visuals will improve personalization and localization — but will also lead to exponentially higher volumes of content that needs to be stored, managed, and accessed across teams.
For most organizations, that leads to a significant challenge around content data standards.
Today’s enterprise orgs maintain a veritable alphabet soup of content storage options — DAMs, CMSs, MAPs, cloud storage, project management tools, and more. Information about campaign performance is often splintered across disparate reporting dashboards and agency partners. This makes it difficult for team members to locate specific assets when needed, understand how content performs, or discover relevant assets that could be reused or repurposed for new campaigns.
Keeping content metadata consistent across these systems is already an enormous challenge. And all of this is about to get exponentially more complex with GenAI.
As content production scales, content metadata management will need to be automated as well — or enterprise orgs will be faced with an impossible maze of metadata.
Ironically, the complexity of adding AI-generated content to the mix threatens to become a major roadblock to marketing teams’ ability to leverage other AI toolsing across the organization. These technologies rely on real-time access to quality data to operate — and if content overload creates messy, inconsistent, siloed data, then marketing teams won’t be able to implement AI-powered solutions successfully.
In order to face this challenge, marketing teams need to let go of the old way of managing metadata — just using naming conventions — and embrace the future of automated, scalable, consistent content IDs.
Why solely using naming conventions is an old-school approach
Marketing teams have long relied on naming conventions to manage data. Under this approach, manually creating and curating names for campaigns and assets has been the default option for managing content metadata — along with an optimistic hope that no other individual or downstream system changes that data along the way.
Despite marketers’ growing dependence on data over the last decade, very few teams today are using standard IDs — creative, project, campaign, placement, or otherwise — and a dedicated system to manage data.
But as generative AI facilitates a much faster pace of creating assets, applying more standardization and automation needs to become a top priority. As campaigns include dozens (or hundreds) of variations for creative to improve personalization and optimize performance, marketing leaders can’t expect this volume of data to be created and managed in point solutions or Excel spreadsheets. There has to be an automated solution that will scale alongside the AI-powered content creation itself.
How AI enables a new paradigm: content IDs
While AI may be exacerbating the problem, it also provides a possible path forward. New technologies can use computer vision to create metadata for all campaigns and content — analyzing visual and linguistic content, creating unique IDs for each piece, and programmatically using that ID as the identifier to connect each asset to any eventual media campaigns they’re featured in.
Content IDs provide a consistent approach to metadata that can be applied across every layer of the tech stack, from DAMs to ad servers to the CMS. This unified approach to content IDs allows teams to centralize data management across every system and removes the problems that come from individual teams trying to manually follow naming conventions across different platforms.
By eliminating these manual processes, you eliminate the possibility for human error — and open up new possibilities for improved measurement and optimization.
The possibilities of content IDs
Brands that crack the code on centralizing data management see enviable outcomes like improving content ROI through strategic repurposing and optimizing creative performance across markets. For example, Bayer reported a 30% lift in CPMs when their team used highly segmented campaign data to inform creative ideation and targeting.
Content IDs also act as a key that unlocks the ultimate goal of most marketers — to not just use AI to create content, but to transform performance across their organization. That includes more accurate measurement, more robust reporting, advanced predictive analytics, and real-time, programmatic campaign optimization.
If your data management strategy relies on text fields that are manually entered and managed by humans, the inevitable result is something will break in the data. And without consistent, cohesive datasets, it will never be possible to enable AI across the entire marketing lifecycle.
For enterprise brands with goals of leveraging AI, a system of record is needed — not just for active campaigns, but across all historical data. This is the only way to enable AI to train and understand the context of the business so that it can develop intelligent optimization strategies and power new data products.
And in order for AI-powered digital transformation projects to be implemented successfully, AI needs up-to-date knowledge of change management within the org. As business objectives, products, taxonomies, and people inevitably change over time, AI needs to be capable of adapting in order to drive results.
The good news: AI is more than capable of enabling better data standards at scale. Learn more about the powerful capabilities of computer vision, data labeling, and data enrichment in our latest webinar, The Power of AI for Content Data Standards.