With an ever-growing diversity of digital channels, it should be easier than ever for your customers to interact with you. But as you try to support more digital touchpoints, is it affecting the quality of the experience you’re delivering?

Customer self-service, for example, is often a great way to support customers—but only if they can reliably locate the information they need in realtime. This is what makes it such an attractive use case for the principles of knowledge management and, in particular, for the latest developments in Semantic AI, working together with structured content. Through this approach, not only will customers be able to find what they need, but you can also support smart virtual assistant applications, and transform the ability of your customer support agents to serve customers more efficiently with the right information.

So what should you have in place to transform customer support in this way?

Rich Taxonomies and Metadata for Findability

It starts with the foundation of good knowledge management: a metadata frame- work and associated taxonomy. Without this, customers or support agents seeking information will experience the problems you’d expect, including:

• Failure to locate the relevant content through navigation
• Lack of context in voluminous search results
• A fragmented experience looking for con- tent in different silos
• Return of duplicated content—or potentially similar but different versions of content—creating confusion

When developing a customer support taxonomy, you start with a base taxonomy, but critically you want to go on to enrich it with ontology development: Your base taxonomy for content relating to an offering, for example, might include classifications such as country, audience, language, content type and subject, type of offering and its operating system.

But it’s through ontology development (or taxonomy enrichment) that you add the context that feeds Semantic AI applications, enabling the right content to be reliably found, and making human con- versations “understandable” to machines. This will include capturing synonyms for concepts, relating concepts to one another, and further describing concepts and their attributes.

Best Practices for Taxonomy Management

It’s the taxonomy management system - using both your base taxonomy and ontology development - that allows information flow between legacy content management systems, next-generation component content management systems, digital asset management systems, business intelligence systems, and any other system feeding into your customer support use cases.

These use cases could include enterprise search, customer and agent knowledge portals, and virtual assistants.

Ideally you want a taxonomy management tool that does all of the following:

• Stores data as RDF-compliant triples in line with the World Wide Web Consortium (W3C) standard
• Allows for specific data management rules and user permissions for each concept domain
• Supports knowledge graphs to represent the relationships within and between your concept domains
• Allows concept domains to be extended over time for new support scenarios – such as integrating partner vocabularies within your support portal, or including social media and its taxonomy - or to expand knowledge management to company domains outside of customer support, such as finance or marketing

Intelligent Content: Using a CCMS

A component content management system (CCMS) differs from an ECM system or a CMS by structuring content into small modules, often called “topics,” for consumption across different use cases. Authoring, managing and delivering content from a CCMS allows for much more agile management and reuse of content, which is why we say that when you combine this approach with metadata, taxonomy and ontology, you create ‘intelligent content.’

With intelligent content you can dynamically deliver accurate, relevant, and specific content to portals, mobile devices, tools, and systems across your organization to help customer support agents, service technicians and channel partners solve customer problems efficiently. By doing so you can transform the information-finding experience in at least four ways:

• Website navigation and curated content by topic
• External SEO through sitemap control for topic landing pages
• Internal search—natural language search and browsing by topic
• Content delivery—the right topic-based content in the right place at the right time

For customer support, in particular, applications such as self-service conversational UIs or voice assistants come to life and are considerably more helpful when they are based on Semantic AI and are fed with quality intelligent content. These applications can use your content structures to find and connect relevant and related content across silos and deliver a smoother, more reliable support experience.

Before and After Scenario: CCMS With Semantic AI

Before: A field agent has encountered an error message. To identify the repair, they must first look up the error message in a PDF document that contains 300+ error messages, then refer to a separate document containing all the repair procedures to find the correct one. Referencing two different documents is time-consuming and subject to error, especially with the agent using a mobile device.

After: If, instead, we use CCMS with Semantic AI, the agent will simply access the error message on any device along with a link to the repair procedure - a fundamentally transformed experience. Multiply this kind of transformation across your customer support functions, and you can improve:

• Diagnosis before dispatch of field technicians to troubleshoot problems
• Chatbot success
• Translation costs for support documents
• Automation for timely content delivery
• Content accessibility across devices
• First-time resolution rates

Enterprises need intelligent customer support solutions to be competitive. These include applications with built-in Semantic AI that use intelligent content - well-structured and classified; maintained and delivered through a CCMS - to help customer support agents and customers find what they need to make smart decisions.

Arpita Maity is Senior Product Marketing Manager for Tridion Intelligent Content Platform, RWS.