The Contact Center Is Now A Knowledge Center

The Contact Center Is Now A Knowledge Center

The future of the contact center is intelligent, not only in the way it deals with customers but also agents. Einat Weiss, Chief Marketing Officer, NICE Systems talks about creating a culture of transparency and engagement to find the right fit between agent and situation, feeding bots with contextual data to make conversations more meaningful and rising to meet customer expectations. There is a significant shift in how contact centers operate and measure results. “Instead of looking at specific interactions and the type of efficiency, we’re calculating the journey excellence score,” she says.

Excerpts from the interview; 

How do you visualise the future of contact centres, especially due to the rise of conversational AI?

We live in a very different world today. It is the preference of 81% of all consumers to communicate over digital channels, and problem-solve by themselves. This is a new standard. It’s a trend that started before the pandemic but was accelerated significantly in 2020.

We see a rush for digital on the consumer side while organizations and contact centers try to catch up. Organizations are moving in a more linear path but the digital explosion on the consumer side has been exponential. Contact centers need to understand how to move faster to accommodate these growing consumer needs. There is a mad rush to the cloud and to accelerate digital adoption. We see another interesting trend; since consumers have this expectation to solve things on their own, they’re becoming more knowledgeable.

The organization has to be smarter in every interaction. When consumers search for something, brands need to be accurate in injecting knowledge into those searches. In a self-service or any unattended format, brands have to be smarter than the consumer at every point.

If and when a customer reaches the contact center, you need to have someone on the other side of the line that knows that the consumer has tried everything they could do on their own to find a solution. 

Conversational AI is a big talking point in the community. But it is created when you know exactly what the consumer needs. You must know who they are and you must have the ability to approach them with a solution at the right time. A challenge here is to know when and how to inject human intervention. The contact center has turned into a knowledge center.

What are the challenges with self-service CX channels?

Self-service was always perceived as a kind of Q-and-A format. But there is a layer where brands also need to understand and react to consumer sentiment. The direction that conversational AI and self-service have to take – is the ability to understand intent and read through sentiment on digital channels.

Self-service runs the risk of being more annoying than IVR. The main reason for that is that these systems tend to be based on guesswork. For example, I have a dog. And whenever there are leftovers at home, my kids will ask Alexa, ‘can the dog eat tomatoes?’ Alexa’s answer is usually yes. Of course, the dog can eat tomatoes but the context of the question is missed. What the kids are asking is – should the dog eat it, and will it be harmful. Bots may guess correctly or may use the frequently used answer for a similar question. This doesn’t work most often. 

This is creating a lot of antagonism against self-service. When this happens consumers will either call the contact center or worse – defect to another brand. What we are trying to do is use our access to the large amount of real data that comes from interactions of consumers and organizations to create intent-based self-service. There are between four and 5,000 ways to ask the same question based on the research we did. We can connect the dots and take the different instances of that question, even if it’s not direct, and provide an accurate answer. This is what makes this bot smart, conversational, and helpful. 

We use AI models that are built to improve CSAT. The models can help agents in real-time, and are based on a behavioral analysis of the agent over time. We are trying to understand what helps build a rapport with the customer.

It tells us how fast agents should be speaking, how quickly they should get to the point. Agents need to know where the journey failed to meet the customer’s requirements so they can handle the situation accordingly.  

We also use AI to help with the agent onboarding process. It helps contact centers understand what works in particular situations, and match agent traits to tackle certain types of calls. 

How can brands better benchmark CX?

Customer service should serve the brand. Earlier, metrics included channel, time and first contact resolution. When organizations look at brand loyalty and customer lifetime value, these measurements are based on customer service. We are starting to see a shift. Instead of looking at specific interactions and the type of efficiency, we’re calculating the journey excellence score.

For customer service teams, it is about measuring what the customer had to go through to solve an issue, and how efficient the entire journey was. Here, it’s important to study customer sentiment. 

How can brands engage employees better, and what is the impact it can have on CX?

Customer service used to be a very over-the-shoulder or in-person management. Technology should be used as an enabler to create more employee empowerment and engagement in general, but specifically with today’s remote workforce.

We have an AI-based virtual assistant called NEVA (NICE Employee Virtual Attendant) that is installed on the desktops of the agents. It works to provide support and assistance that they would get from their management or their peers. Instead of an approach where managers look at a cold matrix that tracks the efficiency of their staff, we look at how we can provide support that would make the employee more engaged. The end game is to create a culture of transparency and empowerment and drive engagement. 

Using these analytics, we can suggest a personalised training plan to suit their strengths. The training is gamified so agents earn badges and learn through engaging activities. 

What is the biggest challenge brands face when delivering omni-channel service?

When you provide omnichannel service, it’s important to have the ability to understand who the consumer is. There is a shift from managing channels to managing experiences. This is fed by a holistic view of the customer. Marketers need to understand who the customer is, what is the intent and how to get them.