Content management continues to bring complexity in the enterprise, which can potentially threaten the necessary growth and increased functionality of the content management system. AI can help reduce that threat.
Many enterprise uses of AI are big, bold and obvious, but its advantages in content management are more modest, yet cumulatively effective. Here are five ways in which AI in content management can have a profound impact.
1. Automate content distribution
Businesses need to pay increasing attention to what content is most effective on what channels. The distinctions here are not trivial: Different customer demographics tend to congregate on particular social media channels -- thirtysomethings prefer Facebook, Gen Z leans toward Instagram and so on. Customizations in content make a difference within each population segment and sub-segment.
Detecting changes in the distribution of customers on various channels, as well as learning which messages are most effective on particular channels, is an ideal use of machine learning. In this case, AI in content management can easily extract and act on patterns, because there's plenty of data to work with.
2. Select webpage content with AI
A cornerstone of customer relationship management (CRM) is the landing page -- the destination for a link delivered via text or email to a potential customer. These pages are generally message-specific and tailored to advance the customer toward a sale, once the customer clicks the link.
However, CRM is moving toward personal customer journeys, so customer landing pages may need to be somewhat customized. Constructing different landing pages for different audience segments and sub-segments can be an approach to this problem, but it's labor-intensive and still not truly personalized.
AI can address this problem by delivering a personalized link to a potential customer via text or email. This link contains a customer ID, enabling the content management AI to dynamically gather personalized content for real-time construction of the landing page, based on CRM customer profile data.
3. Fine-tune ECM search
Some CMSes are gargantuan, packed with difficult-to-navigate oceans of content. However, the more varied the content, the more difficult it is to cope with the volume. Most platforms offer limited utility in coping with this conflict.
Machine learning can digest CMS searches and score their effectiveness, rewriting search terms to improve content targeting. This same arrangement can tag content more effectively, as well.
4. Conversational content systems
This is a huge facet of CMS that is only now beginning to rumble. It may be the biggest interface design problem in multichannel content systems, perhaps even requiring independent management. Conversational interfaces are different-in-kind from other interfaces; scripting options and more conditional metadata can often necessitate dedicated oversight to keep them operating as customers expect them to.
Conversation, of course, is the province of the chatbot -- increasingly used across many enterprise applications. Content in this context is not only what is delivered to the user -- it also encompasses the substance of the conversation between customer and chatbot.
In such a conversational application, the CMS must be aware of the bots interfacing with it, who those bots are servicing and how successful they are at various points in their service cycles. The satisfaction rating that customers give at the conclusion of an exchange determines the areas where bots need additional training.
5. AI-driven content relevance
Finally, there's the question of content relevance. A successful CMS query provides meaningful information. Content relevance is rapidly becoming more complex due to an increased percentage of visual content that is harder to score and more challenging access channels that include voice-based searches.
Tagging visual data in such a way that it is well-matched to text information is one strategy. This enables visual content search results to get a boost from easier-to-find-and-quantify text results that are highly relevant. Machine learning can match text and visual data by training on historical search data where the two were well-matched.
And here's an even more useful approach: When a data scientist performs a cluster analysis of historical searches, practical groupings of content will emerge, with user groups defined by their role, demographics or cross-sections of interests popping out in high relief; and clear hot of content that work for each group will become readily apparent. Data scientists can gather analytics to easily create custom tagging that matches users to favored content based on patterns in search. As with each of the approaches above, it's a matter of letting AI for content management figure out what to look for that isn't already part of the process.