- AI advantage. Leveraging AI in customer experience significantly boosts digital engagement strategies.
- Metadata matters. Metadata’s role in creating more personalized digital experiences can’t be overlooked.
- Balance required. While rich metadata is crucial, an overload can cause diminishing returns and increase maintenance challenges.
Artificial intelligence (AI) has created a new realm of opportunity for brands and marketing technologists. With this comes the need for an updated roadmap outlining the avenues, driving lanes and turning points to navigate the new terrain — all of which should lead to the destination of improved digital customer experiences. Organizations are still designing the blueprints for how they want to use AI in customer experience.
As they go through that process, they should not overlook metadata. It contains key information that, when combined with customer data, can be used to power AI to help create digital experiences. Also, AI can be used to generate metadata about content. Having accurate and consistent metadata about content allows it to be used to create more personalized digital experiences.
As marketers consider their digital experience strategies, and how to make the best use of their available content, maximizing use of metadata should be a cornerstone of the strategy. Incorporating AI into their use of metadata presents an opportunity to change the way they manage and use content and digital assets shaping the customer experience.
Related Article: AI Customer Experience Ushers in a New Era of Engagement
Metadata: The Digital Asset Control Center
As its name suggests, metadata is “data about data.” For marketing content, metadata may include attributes of a document, image, video or other asset. These might be language, location, license, keywords or more. Metadata about products could describe things such as product name, product line, color, size, or materials.
The aggregate of metadata about a piece of content includes information and details collected from all relevant sources across an organization. Once this information is brought together, it can be used to organize digital assets and to make it available as part of digital experience platforms.
Metadata can be categorized into three classes based on its capabilities and the various roles it can play:
- Structural metadata provides information on the way assets are organized, structured, and associated with one another to paint a picture of how content pieces together, such as pages, product numbers and chapters.
- Administrative metadata assists in the management of assets — such as the date of creation and format of an asset.
- Descriptive metadata delineates a resource to make it discoverable, with examples being authors, the subject and descriptions.
Marketing teams should determine which metadata they will collect by considering what matters to their organization and aligning to business needs. Take one of my favorite brands – New Balance. The New Balance DAM has an asset metadata field for “product display name” to help ensure consistency and speed to market when syndicating the appropriate product shots and display name across various marketing channels. This metadata also helps users find and reuse assets based on how the product is marketed.
It is important to differentiate between having enough metadata versus having too much. While accurate and expansive metadata is essential for personalized experiences, too much of it can lead to an overload, creating diminishing returns and maintenance difficulties. Balance is key.
Connecting the Dots Between Metadata and AI in Customer Experience
The way AI in customer experience can be used to improve digital experiences comes down to the power of metadata.
AI relies on an asset’s metadata to further enrich the asset, making it more discoverable and useful, and to provide a means to automate digital asset management (DAM) workflows. For example, trained AI models can be leveraged for adding descriptive metadata for relevant assets, which may then be applied to different content experiences to help drive more personalized journeys.
While every product, service, and experience has its own metadata set, new AI tools can help take it a step further by enabling marketers to gain additional insights and more efficiently allowing them to understand how visitors are searching, interacting with, and finding their products. With this, marketers can home in on creating metadata that will better inform their engagement with customers.
AI’s ability to garner and produce more accurate data, and metadata’s ability to classify and organize information on digital assets, together create a powerful combination for helping marketers improve digital experiences at scale.
Related Article: Need to Get Scrappy? Use Your DAM Metadata
How to Leverage Metadata and AI in Customer Experience
One of the most promising things about integrating AI into digital experience creation is the potential to automate repetitive tasks. Whether it’s the automatic tagging of images with alt text or predictive organization of content assets, AI promises significant enhancements in efficiency by creating valuable metadata and freeing marketers from this otherwise tedious job.
Enabling AI-assisted collaboration across teams can also help manage content and asset complexity by streamlining digital operations, as well as reducing the time and resources required for effective digital experience management. The addition of intelligent search capabilities has also been a game-changer, and metadata is at the heart of this.
While everything has its own metadata set, AI in customer experience can help take it a step further by supplementing metadata strategies and enabling companies to gain additional insights, as well as to achieve a wider knowledge base of information useful in crafting more personalized experiences. In doing so, marketers and creatives can improve digital operations, thereby creating a more productive customer journey. This in turn can enhance customer engagement and contribute to more positive customer outcomes.
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