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3 Ways DAMs Use AI

By Frances Russell | June 18, 2020 | 0 Comments

You might think effective personalization depends on data, but the content demands can be even more challenging. As shown in the chart below, CMOs listed content management as the #2 area of operational investment in 2019, allocating only slightly more to marketing analytics. In the content operations technology category, Digital Asset Management (DAMs) are among the newest solutions to hit the market.

Click to read (Gartner for Marketing Leaders (GML) subscription required): Keep Your Marketing Relevant During Turbulent Times With Content Operations Technology

To differentiate themselves from other content operations technologies that marketers already have, many DAMs boast the ability to use AI by leveraging 3rd party partnerships with cloud AI service providers (e.g. Amazon Rekognition, Google Cloud Vision API and Clarifai). Although the AI solutions are not natively built into DAMs, DAM vendors train these AI solutions to support specific use cases through their product.

While those use cases vary widely, most DAMs don’t use AI to provide the personalized experiences that marketers are after. Instead, they drive greater internal efficiencies that support the distribution of content better than other content operations technologies. Here are 3 common ways that DAMs use AI: 

  1. Automating manual processes –  Automated tagging of assets is probably the most well-known, AI-enabled feature that DAMs offer. Assets can be automatically tagged not just by objects but also by colors, emotions and increasingly, facial recognition. Beyond simply tagging assets, some DAMs allow marketers to refine search results for images based on the confidence level associated with each tag. 
  2. Enabling content reuse – Some DAMs use AI to crop images based on the view that the customer has, enabling organizations to re-use images across browsers, platforms and devices. Other DAMs use AI to facilitate reuse for a wider variety of content asset types. For example, some DAMs use natural language processing to produce transcripts for videos. But beyond just searching the text, marketers can search videos for product placement or snippets where the brand is depicted, and can then cut and stitch the most relevant portions of videos. 
  3. Creating global accessibility – Many DAMs who serve large, global corporations host portals through which marketers, other internal departments, agencies and 3rd parties can access content. To help manage that network of end users, most provide tiered levels of access, customizable access rights, or link sharing. To increase accessibility, these DAMs not only use tags that are intuitively understood by multiple end users; they also use AI to translate tags into multiple languages. Marketers can then streamline the process of categorizing the same content multiple times, and in some cases, eliminate the need to do so entirely.

When evaluating DAM solutions, marketers should ask how well a DAM’s AI capabilities align with the organization’s original use case. Ask vendors to go beyond buzzwords like “AI-enabled” and high-level business outcomes like “content reuse.” By doing so, you can more easily discern where AI is a mostly flashy term from where AI is worth paying for.

For insight into how AI can be used to enhance personalized experiences with or without DAMs, see Five Ways to Use AI for Marketing Personalization Across Channels (subs. req).

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