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When Training Customer Service Agents, Ditch the Hierarchy

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training customer service agentsWe sat down with Nate Brown, 2017 ICMI and HDI Thought Leader, to talk about training customer service agents and optimizing management structure to improve chat team performance. As Director of Customer Experience at UL EHS, a company that provides employee health and safety software solutions, Nate manages a contact center that fields technical questions while striving to break down barriers to the knowledge base for agents and customers alike.

We discovered that Nate’s method of training customer service agents softens the traditional quality management - coach - agent hierarchy. In Nate’s model, quality management works with coaches to develop strategy, coaches are part of the customer support queue, and agents are self-appraising. By integrating each level of the contact center, Nate makes sure everyone has skin in the game.

RB.AI: What does your Quality Management structure look like, and why has this model lead to success?

NB: Our Quality Management is made up of three parts. As a customer contact center with an agent-centric focus, the first step to enforcing quality starts with the agent. The first step in training customer service agents is to make them self-appraising. They rate themselves by reviewing their own interactions on a regular basis. Additionally, they are involved in creating the simple quality form that we use. From there, customer service agents have one-on-one meetings with their coaches that are more casual, and one-on-one weekly meetings with their quality management supervisors, which are more formal. Our supervisors also take care of the duties that may be managed by a knowledge team or QA team in other organizations.

One defining feature of our strategy for training customer service agents is that the coach to agent relationship is collegial rather than hierarchical. It’s entirely peer-to-peer. We focus on people helping other people because it’s the right thing to do and feeds into the intrinsic motivation of the community we’ve created. 

"I’m always challenging the leadership team to think about how to improve the agent experience and customer experience simultaneously."

RB.AI: Your contact center handles technical customer inquiries about product use. How do you implement the knowledge base of the product to both the customer service agent and the consumer, and how has this implementation evolved?

NB: It’s crucial to eliminate barriers to knowledge for both your agents as well as your customers.  By focusing on accessibility and quality with our knowledge base, we find that we can facilitate better, faster resolutions both over the phone and within self-service channels.  The key is to guide the customer toward the best resolution path, which is done by putting the right information in front of them during their moment of need!

RB.AI: We think a lot about how to personalize customer service to make each customer service agent/customer interaction optimal. We’re seeing a huge trend toward personalization in the customer contact center space. Can you speak about personalization of customer experience and agent experience?

NB: Personalization is great when it can be executed in a sustainable fashion. But over-personalization can become a mess and lead to bad process, burning everyone out - all with limited ROI from a customer perspective. One example of over-personalization is when companies try to provide too many channels to serve customer preference and the quality of one or several of those channels suffers. It’s most important to choose the channels that work for you and provide a high-quality experience through those channels.

I’m a far bigger supporter of making effort reduction a priority. Saving time and energy for a customer is a universal win. One area where a personalized experience does make a lot of sense is actually with the customer support agent. Customizing coaching, training, and onboarding techniques using adult learning strategies based on how each employee learns is a wonderful technique.

"In all of our endeavors in the contact center, whether on the customer side or the agent side, we strive to eliminate barriers to access and to provide quality knowledge."

RB.AI: That makes sense. At RapportBoost.AI we figured out that live chat agent performance and behavior has a great effect on the outcome of a conversation. We offer these recommendations in an intuitive way to the agent, and in turn, the effort reduction for the customer is reduced. What do you view to be the most effective strategies for training customer service agents so they perform their best?

NB: We’ve found that it’s more effective to provide customer service agents with customer metrics rather than individual quality scores. So actually providing the agent with customer satisfaction metrics as a measure of the success of their performance. When agents are given individual quality scores, they tend to ‘gamify’ their performance. In other words, their performance is driven by wanting to achieve a number – or to beat the game – rather than a pride for their work, or putting forward genuine effort to provide quality customer service. When it comes to evaluating quality or performance, the focus is best placed on behaviors and customers rather than scores.

RB.AI: Can you talk about the relationship between your coaches and your customer service agents, and the most effective coaching strategies you’ve developed for adapting agent behavior?

NB: The coaches at our contact center have their skin in the game, and this gives them pride and motivation to succeed. Our coaches actively work with management to create the strategic direction and have a direct opportunity to implement this direction in their coaching sessions. For every two to three agents, there’s one coach, which is a pretty low ratio. Coaches are also in the queue but with reduced queue time. Another way we build a collaborative atmosphere among our coaches is to provide a pool of coaching resources – our coaching library – that they can access together to create strategies and solve problems.

training customer service agentsNate Brown is on a quest to improve the agent experience and the customer experience through creativity, knowledge, and hard work. In addition to being a Director of Customer Experience at UL, Nate is the VP of Communication for the HDI Music City Chapter and a well-known speaker in both the ICMI and HDI communities. You can read more on his site,

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Dani Apgar

About Dani Apgar

Danielle is a Co-Founder of RapportBoost.AI, and one of the world’s most successful enterprise artificial intelligence sales professionals. She led the sales, customer success, account management, and sales engineering teams for more than 3 years at Persado, a CNBC Disruptor Top 50 Artificial Intelligence company funded by Goldman Sachs and Bain Capital. She has personally closed six and seven-figure artificial intelligence/SaaS/ARR deals and has worked with companies like Microsoft, Travelocity, Caesars Entertainment, Pottery Barn, Expedia, and Zulily. Her clients had the highest rate of customer retention and satisfaction in the 200+ person company. Dani first worked with Tony in a Silicon Valley tech company in 1999. Dani was a founding Executive Director for Humans for AI, which aims to build the diverse workforce of the future leveraging the existing AI technologies. Dani volunteers and has traveled to Africa with Just Like My Child Foundation. Their mission is to empower vulnerable adolescent girls by enabling them to create healthy, self-sustaining families who prosper without further aid.

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