A customer support agent in an office, helping a customer on the phone. - AI customer support agent concept
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Over the years, the role of the customer support agent has evolved from simply handling customer inquiries to building customer relationships and growing the business. According to the Salesforce State of Service Report, 71 percent of agents see their role as more strategic than two years ago. This means not only are agents spending more time solving complex issues, but they’re also expected to upsell, cross-sell, and provide voice of the customer input into product development.

The problem is, they’re being asked to take on these changing responsibilities using the same old processes and tools.

“As agents become more strategic, they have the potential to add more business value, especially in the area of customer experience,” said Alok Ramsisaria, CEO of Grazitti Interactive, SearchUnify's parent company based in Sunnyvale, CA. "Having successfully completed hundreds of service implementations for enterprises all over the world, I can confidently say that the key to empowering agents is to arm them with contextual knowledge about the problem they're solving. And do it in a way that's scalable and delivers consistent results. Unfortunately, support teams are being hampered by manual processes and technology silos that keep them from servicing customers in the most efficient, effective way.”

That’s why support organizations are turning to technologies like artificial intelligence (AI) to help agents work more efficiently, and spend more time helping customers. In fact, 76 percent of contact center leaders say they plan to invest in AI over the next two years, and 56 percent see opportunities for widespread adoption in their contact centers, according to Deloitte’s Global Contact Center Survey.

“Emerging capabilities such as AI, machine learning, and cloud shape new business models designed for efficiency, improved accessibility and deeper understanding of customers,” notes the survey. “These same technologies can enable a skilled workforce to deliver personalized, human experiences to customers.”

Support’s Biggest Challenges

Before we dive into some of the ways AI can help agents deliver better service, let’s take a look at what today’s support organizations are up against.

Research shows that frontline sales and service agents waste 516 billion hours per year trying to navigate software they find difficult to use. Many times, agents have to switch between multiple systems like product documentation platforms, bug tracking systems, intranets, websites and others just to find what they need. Even then, the information they’re looking for might not be available if the resolution hadn’t yet been logged into the system.

The other challenge support organizations face is a lack of analytics. Agents need access to data and insights related to customer behaviors, past purchases, and search history so they can suggest the most relevant responses and answer questions faster. Unfortunately, these insights aren’t readily available in many organizations.

When none of these elements are running smoothly, not only do your customers become frustrated, but so do your agents. If they end up leaving, you’ll foot the bill for recruiting and onboarding new employees, as well as the lost productivity associated with that training time. McKinsey puts these costs at between $10,000 and $20,000 per employee.

Here’s How AI Can Help

To help overcome the many challenges they face, organizations are already getting started with AI technologies like cognitive search, assisted knowledge creation and chatbots. Here’s an overview of each and how they can help you in your organization.

Cognitive Search

Cognitive search is the foundation for using AI to empower your support agents. Whether you have data stored in your customer relationship management system (CRM), knowledge base, support portal, intranet or website, cognitive search can bring it all together in a simple, seamless way.

Cognitive search uses the power of AI and machine learning (ML) to help your agents quickly find the most relevant information across all of your company’s data sources, saving valuable time, increasing employee engagement, and accelerating case resolution. In fact, cognitive search has been shown to:

  • Improve case deflection by 40%
  • Increase support team productivity by 25%
  • Reduce support costs by 25%
  • Increase customer and employee satisfaction by 20%

Assisted Knowledge Creation

Knowledge-centered support (KCS) is a strategy based on the concept that knowledge is an asset that needs to be continually improved. Of course, the solutions agents provide to customers are only as good as the knowledge base from which they come. So, if an agent searches for a solution and comes up empty-handed, those content gaps need to be filled in.

That’s why it’s important to understand how your content performs so your agents can create content for unresolved issues. However, when agents are busy trying to juggle cases, it’s hard to find the time to write new knowledge base articles. Here’s where AI comes in.

AI can help build new content as an agent resolves new issues. Machine learning algorithms can analyze similar cases to pre-populate some article fields and create a first draft for an agent to build on. The agent still controls the content of the article, which is automatically attached to the case when published.

Chatbots and Agent Helper

Chatbots help organizations by automatically answering lower-level support questions so agents can work on more complex support requests, resolve issues faster and increase customer satisfaction. They’re estimated to help businesses save $8 billion per year by 2022, reports Juniper Research. Not only are chatbots an effective self-service option for customers, but they can also help agents do their jobs better, especially when they’re powered by search.

According to Deloitte, 57 percent of contact centers are already piloting agent helper, which uses chatbots to help agents surface the most relevant responses faster. Here’s how a search-powered chatbot works: When an agent opens a case, an intelligent bot searches all customer interactions, as well as first responses to them. The bot then quickly shows them the most important information they need to resolve a case — top help articles used in resolving similar past cases, top cases related to the current one, top agents who resolved similar cases, and the list of help articles the user checked before logging the case.

“Customer support must embrace technologies like AI, ML and natural language processing that augment an agent's effectiveness,” said Vishal Sharma, CTO for SearchUnify. “Rather than thinking about AI as a replacement for agents, it should be looked at as a way to help them do their jobs better. For example, I think of cognitive search as a platform on top of which support applications can be built. It uses case information already stored in the CRM and customers' behavioral insights to suggest next best actions.”

The Future of Customer Support

Customer expectations continue to change, and support organizations must adapt accordingly. The way to do that, advises Technology Services Industry Association (TSIA) is to ensure that your support interactions are:

  • Persistent: Each support interaction should be handled as part of a larger conversation so customers feel like they’re getting a consistent experience no matter which channel they use.
  • Personalized: When customers use self-service for support, make sure they can immediately find content relevant only to the products they own.
  • Intelligent: Organizations should use AI to proactively serve customers, and make it easier for agents to predict and anticipate customer needs.

When agents have the tools to quickly find contextually relevant answers and recommendations, they can resolve support issues faster, increase customer satisfaction, be more engaged at work, and ultimately provide more value to your business. That’s the power of AI and cognitive search. It breaks down silos so your agents can deliver the experiences your customers expect — today, and well into the future.