Whether it’s rescheduling a flight or simply needing help to set up your Wi-Fi, we’ve all been that customer punching in the numbers to call a contact center. In this case, you’re fueled by a problem and, often, frustrated. To customers, the interaction is (hopefully) simple and straightforward. They call with an issue, an agent answers and solves the problem — and the resolution marks the end of the interaction.

This blog was written by Don Keane, Chief Marketing Officer at SuccessKPI, a Genesys AppFoundry Partner. 

Whether it’s rescheduling a flight or simply needing help to set up your Wi-Fi, we’ve all been that customer punching in the numbers to call a contact center. In this case, you’re fueled by a problem and, often, frustrated.

To customers, the interaction is (hopefully) simple and straightforward. They call with an issue, an agent answers and solves the problem — and the resolution marks the end of the interaction.

Within contact centers, we know there are a lot of behind-the-scenes technology, processes and people making it work. Agents, supervisors and evaluators are all critical to delivering that frictionless experience.

So, how can you ensure everything is running smoothly?

Let’s look at an interaction from the customer’s point of view to see how technology can empower better experiences.

Ring, ring! Someone’s calling — let’s get started.

The Agent: Navigating Customer Sentiment

Imagine you’re a customer who needs to reschedule a flight because your alarm didn’t wake you up in enough time to make it to the airport. Needless to say, tensions are high.

You call the airline’s customer service number, slightly panicked, and you’re connected to an agent. You explain your issue, and the agent works to find you a new ticket for another time. Straightforward. Easy. Done.

But to make the interaction feel this simple to the customer, the agent needs to do a lot all at once. This is where artificial intelligence (AI) can help.

Sentiment analysis is an AI and machine learning (ML) model trained to measure the emotion of interactions, such as if the interaction is positive, negative or neutral.

Sentiment analysis and analytics help businesses:

  • Get insight into the effectiveness of call center agents
  • Identify common problems and pain points in the delivery of customer support
  • Monitor opinions and attitudes about services, products and customer support
  • Give organizations a unified view of the full customer journey

In this case, the customer called the contact center in a frantic manner. The agent needed to not only find a solution but do so in a way that calmed the already frazzled customer.

Luckily, the agent knew how to do this because they were properly coached on these situations. And the customer left the interaction with a new plane ticket and a renewed loyalty to the airline.

The Evaluator: Coaching at Scale

The above interaction resulted in a happy, loyal customer. But what if it didn’t?

Let’s imagine you’re a customer in the same scenario, but the agent was unable to answer your questions and help you within a reasonable amount of time. You probably wouldn’t travel using this airline again, right?

Obviously, that’s why evaluators are such an essential part of a contact center. They’re the ones who listen to calls and determine if further training is needed.

But contact center growth has taken flight, and it’d be virtually impossible for evaluators to monitor every call made every day by every agent.

AI-powered quality management (QM) tools can help contact centers flag calls they need to evaluate. One way they do this is by detecting negative language within the calls. The interaction is then sent to a dashboard for the evaluator to listen and determine if further coaching is needed.

These AI-powered QM tools help evaluators become more efficient when coaching agents. The tools bring the interactions to the agents instead of having them search through thousands, if not millions, of customer calls.

The Supervisor: The Macro View

Step into the customer’s shoes one more time and imagine you’re wanting to book a flight. You want to make sure the airline you choose to travel with is reliable and has highly rated customer service in case anything goes wrong.

As a supervisor, it’s your job to ensure your contact center is running in a way that would make it highly rated. But with thousands, if not millions, of calls happening at any given time, that’s no small feat.

Traditional metrics such as Net Promoter Score and customer satisfaction are helpful to gauge the effectiveness of a contact center, but they’re no longer enough. AI-powered analysis and real-time insights help supervisors understand what’s happening within contact centers at any given time.

AI and ML engines need to analyze millions of interactions across all channels and package everything together in one secure platform. Then these tools surface critical trends and patterns. This is how supervisors can ensure their contact centers run smoothly.

Migrating to the Cloud with AI Technologies

Much like in airplanes, many businesses have reported a bit of turbulence when flying toward the cloud. In fact, 40% of businesses reported having limited success with their CCaaS migration efforts, according to a Forrester Consulting report on behalf of SuccessKPI.

But with an optimized experience — and with the right cloud toolsets — 79% of these businesses reported they believe they can reach successful outcomes with a cloud migration.

One such business was a healthcare company that wanted to migrate to the Genesys Cloud™ platform but wanted to do so without disrupting its customer experience.

With the right tools, including AI-powered insights, the company migrated to Genesys Cloud without disruption. It even began evaluating agent outlier performance by drilling down and viewing agent level statistics and call level details.

To learn more about integrating AI-powered tools from SuccessKPI into your contact center to improve your customer experience, visit SuccessKPI on the AppFoundry® Marketplace.