4 future-forward ways energy companies improve agent performance with AI

Keeping the lights on may be a figure of speech for a lot of us. But for energy companies worldwide, powering the homes of millions of people is a top priority.

For energy and utility contact centers, agent performance is directly tied to customer trust, especially when customers are calling about outages, billing disputes, or service during extreme weather. AI coaching for call center agents gives contact center leaders a way to review far more conversations than a manual QA process ever could, surfacing specific, actionable coaching from every interaction rather than a small monthly sample. Instead of vague scorecards, agents get feedback tied to what actually happened on the call: a missed disclosure, a rushed close, a tone shift when a customer got frustrated. For CIOs and contact center leaders, that means coaching that scales with call volume instead of falling further behind it. The four approaches below show how energy companies are putting this to work today.

According to a recent PwC report, 41% of power and utilities companies shared that their top COVID-2019 concerns were ensuring workforce productivity and mitigating a decrease in consumer confidence as the pandemic drags on. 

Take into account recent power outages all over the U.S., and it’s clear that doubling down on both the infrastructure and the quality of customer service to combat these challenges is a must-have.

Here are four strategies to try.

1. Implementing a culture of consistent customer service standards for every call

Modern energy and utilities companies prioritize building a culture of consistency across every interaction. With the remote landscape and agents working remote, enforcing this culture has become even more critical as the pandemic widens the coaching productivity gap.

The Observe.AI platform helps energy and utilities companies keep CX standards high with Contact Center AI, enabling quality teams to analyze every customer interaction to ensure that agents are delivering service that meets quality standards. 

One feature within the Observe.AI platform called Moments, or AI-powered interaction monitoring, allows utilities companies to track points of interest on calls and chat conversations that offer signals on energy specialists’ performance.

To ensure consistent CX standards, QA teams bucket Moments into three categories:

• Customer satisfaction: Are there any supervisor escalations that are common with a particular agent? Is the busy signal present on too many calls? These indicators can help identify where customer satisfaction gaps lie.
• Process adherence: Are agents prescribing to call opening scripts? Mandatory disclosures are important to maintaining consistency.
• Compliance: Is there a communication gap between the agent and the customer? Is customer information being verified?

With these monitoring measures in place, quality teams are able to better understand where agents are excelling, and where there is training needed to address areas in most need of improvement.

2. Help improve team success with a 360º view of performance.

Identifying the root cause behind results on contact center performance metrics such as average handle time (AHT), First Call Resolution (FCR), and dead air is a tall task with manual quality assurance tools and processes. As one quality supervisor put it,

“You can’t assume things are going great when you have no system in place to listen to calls at scale.”

Energy & utility QA teams can get this high-level view of how agents are performing with the Observe.AI Leaderboard, which provides both a zoomed in and zoomed out view of each agent’s performance. QA teams can then identify agents with high AHT, for example, and guide them to think of different ways to address customer queries to help drive AHT down. 

An example, one energy company directs callers to an online calculator to reduce emissions, empowering customers to drive down their own consumption while also improving customer service performance metrics.

3. Focus on up-skilling high-performing agents

Contact Center AI plays a role in eliminating some of the more tedious aspects of maintaining customer service quality such as listening to calls, collecting feedback, and distributing it. This creates an opportunity for high performing agents to be upskilled from engaging in manual tasks into specialist roles.

For instance, top performers at a top energy company have been trained to become energy specialists that manage an extended helpline. An energy specialist plays an important role in managing repeat callers by running efficiency assessments, installing energy-efficient equipment, and offering a deeper level of expertise to improve service delivery for customers with high energy usage.

Another specialized role is the Q&A specialist, who collates top questions identified on the Observe.AI platform, and creates defined scripts and answers to help agents address top call drivers faster and more easily.

All in all, these specialized roles are essential in helping to keep average handle time low, first call resolution high, and overall efficiency in top shape.

5. Scale coaching with AI agents that review every interaction

Manual QA sampling covers a small fraction of calls and chats, which means most agents only hear about performance issues weeks after they happen, if at all. AI agents for contact centers change that by automatically reviewing 100% of interactions and flagging the moments that matter most for coaching, from compliance misses to signs of customer frustration. This gives energy and utility companies a consistent, scalable way to deliver AI coaching for call center agents, so every agent gets timely, specific feedback rather than just the top performers or the ones a supervisor happened to spot-check. As call volume grows during storms, outages, or billing cycles, this kind of automated coaching keeps pace without adding headcount to the QA team.

4. Teach agents to practice empathetic and compassionate service

As the frontline brand representatives, agent soft skills are critical. Teaching them the essential skills needed to interact with hundreds of people day in and day out effectively fosters success, especially for utilities & energy companies that often handle delicate conversations with customers who face financial difficulties.

One way Observe.AI is helping agents improve their ability to engage in active listening is through sentiment analysis, which sheds light on the tonality of a speaker towards a product, person, topic, or event. 

Sentiment analysis allows businesses to analyze customer and agent interactions on a deeper level by automatically detecting positive, negative, or neutral interactions on calls. Agents can then gain a better understanding of their tone of voice on calls with what we call sentiment insights.

To beef up its soft skills coaching efforts and sentiment analytics, one energy company also implemented Sharepoint services to give agents access to feedback more quickly. This was especially helpful when their teams moved remote, came back to the office, and went remote again weeks later. 

Looking ahead, energy & utilities companies leveraging Contact Center AI will be able to more actively define their agent coaching programs, particularly with the help of new functionalities within the Observe.AI platform.

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About the Author

Sharath Keshavnarayana is the Co-founder and CRO at Observe.AI, and has over a decade of experience in the customer care space. Connect with Sharath on LinkedIn.