The Future of the Contact Centre is Intelligent

She 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.

Customer service is evolving to keep up with rising expectations of the modern customer. Martechvibe shares the top learnings from a conversation with Weiss about the future of the contact centre;

Metrics are evolving to benchmark CX 

Customer service should serve the brand. Earlier, metrics included channel, time and first contact resolution. When organisations 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.

Keep agents empowered and engaged 

Customer service used to be 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) installed on the agents’ desktops. 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 the support that would make the employee more engaged. The end game is to create a culture of transparency and empowerment and drive engagement.

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

Inject intent and sentiment into self-service 

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 the 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 question’s context is missed. The kids are asking whether the dog should eat it and whether it will 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 centre 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 between consumers and organisations to create intent-based self-service. There are four to 5,000 ways to ask the same question based on our research. 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 behavioural 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 and 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 centres understand what works in particular situations and match agent traits to tackle certain types of calls.

Conversational AI needs human intelligence 

We live in a very different world today. Eighty-one per cent of all consumers prefer to communicate over digital channels and problem-solving 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 organisations and contact centres try to catch up. Organisations are moving in a more linear path, but the digital explosion on the consumer side has been exponential. Contact centres 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 of solving things on their own, they’re becoming more knowledgeable.

The organisation 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 unattended format, brands must be smarter than the consumer at every point.

If and when a customer reaches the contact centre, you need someone on the other side of the line who 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 be able 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 centre has turned into a knowledge centre.

From managing channels to managing experiences

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. A holistic view of the customer feeds this. Marketers need to understand who the customer is, their intent, and how to get them.