Piecing Together Your Contact Center AI Strategy? Start Here.

Service teams that understand what drives customer demand will likely have the most success with contact center AI

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Piecing Together Your Contact Center AI Strategy Start Here. - CX Today News
Contact CentreInsights

Published: February 7, 2024

Charlie Mitchell

In the contact center space, one form of data rules them all: intent data.

Intent data tells a business: which problems do customers most often contact us about?

With this insight, contact centers can engage in root-cause analysis and work with other departments to fix the most pressing customer journey pain points.

In addition, leaders may highlight the almighty strain these issues put on the service team to inspire cross-departmental action.

When cross-departmental solutions aren’t possible, contact centers may begin to deploy virtual agents, live agent-assist tools, and other AI applications. 

Yet, the intent data and customer journey work must come first. If not, contact centers risk higher costs, lower efficiency, and – ultimately – frustrated agents.

→ Register for our upcoming webinar here: AI in Workforce Engagement Management: What You’re Missing, and What’s Coming (Wednesday, February 21)

Mining Intent Data: The Bad Old Days

Contact centers conventionally have two options when monitoring customer intent.

The first is to ask agents to enter a “disposition code” into the CRM after every contact, which tags each conversation with the contact reason.

Of course, this adds seconds to every interaction. Yet, most critically, the resulting data is often inadequate and sometimes – in operations that don’t factor disposition work into their quality assurance (QA) programs – dreadful.

Why? Because agents typically rush through their post-contact processing. As such, they default to their preferred code or the one at the top of the list.

Also, agents are sometimes unsure of which code applies to the contact, perhaps due to improper coaching or having to sift through a long list of codes.

Recognizing these issues, some businesses have turned to the second option: implementing a conversational analytics platform to automate the disposition process.

Yet, they’re often big, expensive AI systems that are too capable of performing only one task.

Generative AI to the Rescue

Thankfully, there is a new sheriff in the AI town. Its name? Generative AI (GenAI).

GenAI has evolved mining intent data from a specialism into something most CCaaS vendors can easily accomplish. As such, contact centers don’t need to bolt on expensive specialist systems.

Thanks to AI unlocking this increased accessibility of intent data, contact centers may more effectively monitor their demand drivers and isolate their top 10-20.

With this insight, service leaders can begin to root out their most pressing customer journey pain points and plan to overcome them.

As noted previously, that may require process fixes from other departments and perhaps even changes to company policy.

Nonetheless, all this leads to a more efficient contact center, which the service team may now layer with more AI to magnify those efficiencies and drive a greater return on investment (ROI).

The Stage Is Set: Virtual Agents Will Flourish

Unfortunately, cross-functional collaboration isn’t enough to overcome all customer journey pain points. That’s where virtual agents enter the fray.

A virtual agent can relieve more of the workload and gobble up the remaining customer intents that follow a transactional resolution path.

Conventionally, these virtual agents have taken lots of time and resources to build.

Yet, when the contact center knows the intents it wants to automate upfront, much of the early work falls by the wayside.

Moreover, the latest virtual agent platforms perform much of the design work too – again, thanks to recent GenAI developments.

As a result, businesses may now simply explain in natural language the task a bot should perform, the information it should gather, and the APIs it should send data to.

From there, the conversational AI platform can auto-generate a virtual agent flow, which IT may test, train, and optimize before embedding the bot into its operations.

Zoom Virtual Agent is one such platform that enables this, and the enterprise communications pioneer has had great success in implementing it themselves.

“It’s saving us 13 million dollars every month because of the sheer number of requests we get,” said Ben Neo, Head of Contact Center and CX Sales EMEA at Zoom, in conversation with CX Today.

“Also, when an escalation does happen, it’s supporting our live agents, giving them the context they need to quickly finish servicing our customers.”

That said, contact centers must remember that upfront intent work remains a critical part of building virtual agents, and – as GenAI hype continues – they mustn’t excitedly rush bots out the door. 

→ Don’t forget to register for our 21st February webinar: AI in Workforce Engagement Management: What You’re Missing, and What’s Coming

The Contact Center AI Journey Continues with Zoom

For the remaining intents that are too complex for a virtual agent to automate, live agents must lift the load.

Thankfully, AI can help here, too. For example, consider leveraging a Workforce Engagement Management (WEM) tool that automates quality scoring.

With this, contact centers can analyze the optimal route for agents to solve these queries. Then, with a broader WEM suite, coach these in.

Additional AI tools may also assist agents in following these routes. How? By proactively serving them with relevant information, automating desktop tasks, and auto-generating performance feedback.  

Zoom enables contact centers to achieve all of this and more. To dive deeper into its ever-expanding CCaaS platform, visit: explore.zoom.us/en/products/contactcenter/.

Artificial IntelligenceCCaaSGenerative AIVirtual AgentWorkforce Management

Brands mentioned in this article.

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