Machine learning is a staple of 21st-century customer service. Powerful AI-powered machine learning that understands human language can be a considerable asset for self-service, delighting customers and agents. Managing these interactions is their primary objective. It can also be a great source for building a knowledge base that informs customers before they submit a support ticket.
Why Customers Prefer Knowledge Bases for Self-Service
Customers want to resolve issues quickly and conveniently. As a result, many choose self-service, including knowledge bases. To meet the needs and expectations of today’s customers, having a robust and easy-to-access knowledge base is critical.
Customers look to knowledge bases for several reasons:
- They don’t want to wait in queues to ask a question.
- They expect on-demand support, and it isn’t feasible for most companies to operate customer support centers 24/7.
- The knowledge base supports customers in the channel they most want to use.
Additionally, when you implement a knowledge base, its information can deflect tickets. Ticket deflection is a big deal for businesses. It describes the resolution of customer queries before the creation of a support ticket. When you can deflect more tickets and satisfy customer needs, there’s much less strain on your agents and shorter queues for those that need human intervention.
What to Consider When Building or Improving a Knowledge Base
If you don’t have a knowledge base or customers fail to find what they need in yours, you should develop a strategy to make it more useful. It’s an untapped resource that alleviates operational overload and provides a better experience.
Formatting and user experience design are critical when creating a knowledge base people actually use. There are multiple design considerations, including:
- Categorization of information by products or question types (i.e., orders, billing, payments, and so forth)
You may also include some advanced functionality, such as providing a guided tutorial that asks preliminary questions to get the customer to the correct section.
The articles and information in your knowledge base should follow your brand’s voice. That tone should also demonstrate the voice of your support team. Craft content in the same way that an agent would deliver it.
Other best practices include:
- Ensure that articles are clear and concise.
- Tier content for better readability.
- Make titles relevant to the topic.
- Manage content for online accessibility and equity.
What Role AI Plays in Knowledge Base Building
Once you have the format, functionality, and initial content for your knowledge base, you can leverage conversational AI-powered machine learning to continue to add to it. One of the most significant ways is through ticket tagging.
Ticket tagging plays a crucial role in how AI improves knowledge bases. AI tags tickets automatically by whatever parameters you set, such as product, location, or topics. Doing this provides a wealth of information to refine your knowledge base.
Here’s how this works in action. You could see the volume of tickets increase within a segment. When digging into this, you will likely find patterns. Having that information offers visibility around gaps in your content and provides ideas for what to add. Basically, if it’s a common question that shouldn’t require agent intervention, but you keep seeing tickets for it, that’s a signal that you need to include it in your knowledge base.
With a customer support AI platform, you’ll be able to leverage ticket data to build a knowledge base that offers more accurate responses. That can result in fewer tickets for agents.
How Agents Can Also Benefit from Knowledge Bases
If customers can’t resolve issues via self-service, the next stop is the agent queue. When using conversational AI and machine learning, agents can access knowledge bases relevant to the customer’s inquiry. It provides these automatically, so agents don’t have to waste time hunting for information.
Improve Your Knowledge Base and Much More with AI
Conversational AI is the future of customer support. It’s a valuable tool that enhances self-service. Because AI is part of the entire service ecosystem, it’s always working to help customers and agents.