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Helping Leaders Helping Agents Helping Customers

Helping Leaders Helping Agents Helping Customers

/ People, Technology, Artificial Intelligence
Helping Leaders Helping Agents Helping Customers

How AI tools facilitate coaching and supervising.

There are a variety of different ways customers can contact businesses when they have a complaint or concern.

In a 2022 survey from Salesforce, 59% of customers said they prefer to contact customer service over the phone, up from 54% in 2021. Meanwhile, email, the most popular channel, dropped in its popularity to 57% from 65% over the same period.

And with 89% of respondents expecting to interact with a live agent immediately upon contacting a company, the stakes are high for contact center representatives to deliver quality, empathetic interactions to each customer.

Contact center supervisors can only do so much when it comes to coaching and supervising agents, no matter how well-intentioned they are. That’s why artificial intelligence (AI) tools are essential for these functions, as we will see. And as the technology advances, Generative AI like ChatGPT could potentially impact AI-powered coaching and training tools.

AI’s Ability to Listen on Every Call

Contact center team leaders can’t listen to each call that every agent takes; it would be impossible to do so. Especially as customer service teams become increasingly hybrid or remote.

AI tools allow leaders to keep a pulse on each agent and their interactions, no matter where they are located. They can know in real-time what is transpiring during each call with each agent.

Moreover, not all agents are the same. AI applications can produce personalized data-driven development plans for agents to help them learn and grow that supervisors can validate and use in their coaching. This then frees up these leaders to do more meaningful, quality work with their additional time.

Contact center supervisors can only do so much when it comes to coaching and supervising agents...

Customer experience (CX) has always been an important metric used in training, allowing contact center leaders to measure how each customer-agent interaction went.

In the past, companies relied on customers to complete post-call surveys in order to gauge the experiences and provide continued training resources to agents.

Now, though, the development of AI tools for contact centers has led to an easier and more accurate collection of this metric, resulting in better coaching as well. AI models measuring CX can provide a real-time, objective measure of customer perception for each interaction, which eliminates the selection bias found in traditional post-call surveys.

AI tools also provide leaders with insights into the employee experience: a metric that has grown in significance over the past few years especially as employee burnout and agent fatigue have increased.

There is an intrinsic link between employee experience and CX. When employees are engaged and have good experiences on the job, this directly and positively impacts the CX. Contact center leaders must strategically leverage human-aware AI to deliver a better employee experience to achieve the highest levels of customer loyalty and engagement.

Combining Emotion AI and Conversation AI

When contact center leaders combine different types of AI models, they can glean a variety of insights to help facilitate coaching and supervising. One powerful combination is utilizing Emotion AI and Conversation AI (not to be confused with Conversational AI chatbots).

Emotion AI analyzes voice signals, or emotion-related behaviors, that the AI model picks up while the customer and agent are speaking. Conversation AI analyzes the words and topics discussed between the customer and the agent. When used together these powerful AI models provide deeper context to what was said and how the messages were received.

How can contact centers combine Emotion AI and Conversation AI to assist in coaching? One important way involves using AI to deliver real-time cues, or nudges, to the agent.

If the Emotion AI is picking up on the customer feeling frustrated or upset, for example, the agent can receive an alert in real-time with suggestions on how best to respond or proceed with the conversation. These nudges can also alert the agent of a job well done, enabling the agent to identify good behaviors, and continue delivering positive experiences to the customers.

When AI models deliver real-time cues as the call is happening, it allows agents to be coached in the moment. This makes them more likely to be successful on every call as they continue to learn and grow in their career.

AI tools also provide leaders with insights into the employee experience...

Additionally, it gives the supervisors visibility into live conversations from their teams: even if they are working in various locations. Without AI tools like this, agents often don’t receive feedback from their supervisors until their weekly, monthly, or even quarterly progress report meetings.

The CX has always been top of mind for contact center teams. Recently, it has been a priority to place just as much importance on monitoring and measuring the employee experience.

As call volume and complexity increases, agents endure higher rates of fatigue and burnout. When supervisors utilize AI tools they can continuously track both the customer and employee experiences and identify opportunities to improve both.

How ChatGPT Can Play a Role

Generative AI, specifically ChatGPT, has been all the rage across many industries over the past few months as people discover its capabilities. Generative AI touts many strengths, but companies should also be aware of its weaknesses (see Figure 1). Contact center leaders must consider its impacts on their training tools, especially their AI-powered tools.

In the contact center space specifically, the capabilities of Generative AI as a natural language processor and interface to explore data are exciting.

These large language models (LLMs) can summarize and identify key insights from messy call transcripts, for example. What was once done manually or through intensive computer processing, can now be done more swiftly and flexibly using Generative AI. This saves teams time and resources, allowing agents to do the more meaningful work of connecting and interacting with customers.

Agents can receive coaching in real-time, thanks to AI’s ability to scan and decipher the emotional tones and content of the interactions...

Even as little as one year ago, call summarization was a difficult problem. Now, Generative AI makes it easy. This allows summarization to be harnessed ubiquitously for coaching, quality processes, call wrap-up, and broad-based managerial insights, among other applications.

Almost every tech industry vendor has a strategy, a feature set or a product in the Generative AI landscape today. Big names like Microsoft, Google, and Amazon have committed significant resources to Generative AI. It’s only a matter of time before contact centers utilize this technology widely.

Figure 1: Generative AI Deficiencies

Although there are many benefits to Generative AI, particularly for coaching and training like quickly gathering key insights and call summarization, it’s not without its deficiencies.

Most notably, Generative AI is statistical in nature, which means that it can be wrong and which in turn raises risks for regulated institutions in particular.

Today, Generative AI also omits emotion-related behaviors and other human data, which means that it does not take into account the full spectrum of what is communicated in human conversations.

The Evolving Possibilities

When used in the contact center, AI tools facilitate coaching and supervising by providing supervisors with the insight needed to stay abreast of the interactions and experiences of customers and agents alike. These tools have proven especially useful to remote and hybrid contact center workforces.

Agents can receive coaching in real-time, thanks to AI’s ability to scan and decipher the emotional tones and content of the interactions and deliver cues to help guide the agent through the calls. AI models also shed light on the CX, which is an important measurement in the customer service industry.

As AI advances in this space, the employee experience is another metric on contact center leaders’ radars as call complexity and agent burnout increase. It seems as though the possibilities are endless as further Generative AI solutions come into the fold; it is an exciting time to participate in the technology empowering contact center success.

Josh Feast

Josh Feast

Josh Feast is the CEO and Co-Founder of Cogito. He is a serial entrepreneur and thought leader with a passion for creating innovative technology that helps people live more productive lives. He holds an MBA from the MIT Sloan School of Management and a Bachelor of Technology from Massey University in New Zealand.

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