Editorial
The Gist
- AI transformation. AI in customer service is redefining how support agents, customers, and businesses interact, offering new tools for agents and a more personalized experience for customers.
- New metrics. Traditional KPIs like productivity level and customer satisfaction are evolving, as AI takes over routine tasks and agents focus on more complex issues.
- Profit mindset. AI is turning customer support from a cost center to a profit center by offering data-driven insights into customer behavior, thus enhancing business value.
The status quo customer service operation traditionally operates in a transactional, reactive environment. And generally, for good reason: Studies show the average support team closes out 578 tickets per day, 3,991 per week and 17,630 per month. The ticket volume in working hours comes out to a total of 6,594.
With such a high support volume, leaders primarily measure the success of their support agents by how quickly and how well they can close out a customer interaction on this ticket-by-ticket basis. AI in customer service is now driving a real transformation in how businesses support their customers, and many support agents will have concerns about the nature of their roles and how their success is measured in this new world.
The truth is that the evolving role of customer service in this new AI era is a net positive for support agents, customers and businesses alike.
Here’s how support leaders can prepare their teams for the AI service operation of the future.
1. Recognize, Understand and Mitigate Fears Around AI in Customer Service
The often elaborated AI vs. humans debate is causing uncertainty and discomfort across many industries. Support leaders should reassure agents that the best customer service is delivered via both AI and humans working together. It is key to get input from your team on how and where AI can be used alongside them to improve both their jobs and the customer experience — your support team knows your customers and the ins and outs of the role better than anyone else.
To start, there are three broad areas that I see AI in customer service will impact:
- Customer engagement: AI will power a conversational, personalized and contextual customer experience to resolve routine and straight-forward issues and ensure a seamless handover to support agents for more complex questions that require a human touch.
- Agent experience/productivity: AI will enable a suite of tools and applications to be available to support agents that will improve productivity and allow agents to focus on the most valuable work for customers. These tools will include case/conversation summaries, the ability to change tone and sentiment of responses, suggesting the most probable answers to the support agent, etc.
- Managing support operations: With AI capabilities, the task of managing support operations is improved. This includes being able to do case/conversation analysis at scale to pull trends that will inform product or service roadmaps and drive process improvements. It also includes being able to "infer" customer sentiment or satisfaction even when a customer has not provided feedback. It can also enable "quality assurance" at scale rather than the sampling of cases/conversations that are reviewed today.
Leaders need to explain how AI will change the way their teams operate for the better, instead of replacing support team roles. Ultimately, this will require a change in mindset around success benchmarks. This leads me to the second point of change.
Related Article: How to Leverage Your AI-Powered Customer Support Strategy
2. Rethink the Metrics You Use to Measure the Success of Your Support Agents
Two Holy Grail metrics have long been the primary indicator for support agent success: productivity level, or how quickly an agent can answer a customer query, and customer satisfaction, or how pleased a customer is with the caliber of support provided. Speed and quality have always been two pieces of the performance puzzle. With AI, KPIs for measuring agent performance and capacity planning need to evolve.
Rather than caring about two metrics alone, support leaders will need to use a bundle of metrics to measure an agent’s ability to answer the complex queries that will take a longer amount of time to solve. The metrics not only need to focus on how well the agent addressed the immediate customer issue, but also how proactively the agent engaged with the customer to anticipate and avoid the next likely issue. The metrics need to encourage support agents to be more proactive.
New metrics also need to take into account how well support agents are contributing to "expanding" the AI knowledge base. For example, is an agent providing feedback on how AI could have answered the question that they just handled?
Leaders need to reorient their teams around these new benchmarks and motivate support agents to achieve them through practices that may be different from what they’re used to. For example, leaders need to communicate that solving one extremely difficult customer query in a day will now bring greater reward than working to close out multiple simple tickets that could have been covered by AI.
We also need to reach the point where leaders allow teams a level of autonomy to do the right thing for the customer, without the metric impinging on their performance. In an AI-first world, where problems and questions coming through to humans are more complex and nuanced, it’s all about trusting your team to do the right thing for the customer.
Related Article: Generative AI Will Reduce 30% of Customer Service and Support Agents
3. Rally Your Team Around a 'Support as a Profit' vs. 'Support as Cost Center' Mindset
In the not-too-distant past, customer service was viewed as a cost center — a necessary, but non-profitable, part of business. Thanks to generative AI, gone are the days of tallying up costs and solely looking inward at operating overheads. To understand the true measure of support success, today’s modern support leaders will utilize AI to collect data about customers' purchasing decisions and preferences to assess the overall health of the business and strengthen customer loyalty, retention and advocacy.
At a time where consumers grow increasingly picky about where they spend their time, money and energy, the customer support teams that prioritize AI to collect customer information at an unprecedented rate and offer services according to the data collected will come out ahead. AI will enable leaders to focus more on the true bottom-line value that incredible customer service brings to a business. In doing so, support becomes a value adding and necessary part of business success.
Related Article: AI & ChatGPT: Do You Trust Your Data?
4. Encourage Teams to Strengthen the Skills They Need in an AI World
AI will cause an even greater shift from “reactive” to “proactive” customer service, whereby support agents need to provide answers to problems before customers know they have them. Leaders need to inspire support agents to think differently about the skills their role requires when AI becomes a bigger part of the support picture. For this shift to occur, leaders should help support agents prioritize their strengths in these three areas:
- Troubleshooting: Support agents excel at troubleshooting, but they will need this skill even more to be able to solve complex and outside-of-the-box problems for customers, with AI handling the most routine and simple customer queries. In an AI world, troubleshooting is not just about how to answer the question that the customer has asked, but also anticipating the next problem that the customer is likely to encounter and proactively advising the customer on how to avoid it. This is also about understanding how to ensure the same question does not get asked again, i.e., feeding the solution back into the AI engine so an AI bot could attempt to answer the question next time.
- Subject matter expertise: Rather than working as generalists, support agents will become experts in specific parts of their product or service offering. They will also need to step into the role of “knowledge managers” who take the time to tap into the tribal knowledge living within their heads and use it to fuel AI models with the kinds of responses they know customers want or prefer.
- Curiosity mindset: The thoughtful ability to think through, “Why did this customer have to come through to me in the first place? What was the issue? And how can I ensure that the issue doesn’t happen again?” is critical in the support agent role. Support agents need this level of curiosity to build and feed into the AI to ensure it can perform in alignment with the correct or best customer response.
Final Thoughts on AI in Customer Service
By focusing on these skills, support agents can step into the role of “conversation analysts,” making it their goal to ensure that every time they solve a problem, it’s the last time that particular issue occurs.
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Start with these four things and you will have your support team on the path to deliver more thoughtful, excellent customer service than ever before — even in the era if AI in customer service.
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