Technologies such as artificial intelligence (AI), robotic process automation (RPA) and data analytics are redefining call center strategies and operations. But an effective approach requires more than a good toolbox. As the market matures, advanced call center functionality, smart tools and omnichannel platforms are increasingly becoming table stakes. In today’s environment, competitive advantage will go to those who can most effectively apply technology to improve business outcomes and enhance the customer experience.
Call center managers need to ensure that customers can easily and intuitively use the engagement channel they prefer.
Top-performing call centers leverage people, processes and technology to get the most from their technology investment. Specifically, they use smart leaders and smart tools to:
- Optimize the relationship between human and digital labor
- Develop more effective management and measurement models
- Continually refine how the business engages with customers
1 Optimize the Relationship Between Human and Digital Labor
The rapid adoption of intelligent automation into call center operations creates tremendous opportunities to create compelling customer experiences. Defining the optimal approach to deploying technology presents a challenge, however. As smart machines take on an increasing volume of the routine, repetitive and rules-based tasks of the contact center, agents have to assume new roles that fully leverage communication skills, decision-making and judgment. Put differently, you want to avoid sticking people with mindless tasks while leaving the robots in charge of handling sensitive customer issues.
It’s a formidable task, and one that extends far beyond technical expertise and deployment of smart tools. Complex and deeply entrenched call center processes—based largely on 20th Century technology—must often be redesigned from scratch. The goal here is to squeeze every ounce of functionality out of RPA and AI applications. This can fully exploit the ability of smart tools to execute a wide range of call center tasks faster, cheaper and more accurately than people can.
Once the dust settles from automating everything that is automatable, people—freed from the burden of spending big chunks of their day doing mindless work—have a lot more time on their hands. How can call center managers take advantage? One option is to simply reallocate workload and reduce headcount. However, while RPA can certainly support a do-more-with-less approach, experience has shown that bots typically drive reallocation of human resources rather than displacement.
Focusing just on efficiency, moreover, misses the larger opportunity, which lies in leveraging technology to optimize human-to-human interaction. This is where things get truly disruptive. Automating a significant portion of basic call center activity creates an opportunity to fundamentally redefine the role of human agents, as well significantly increase the value they deliver to a business. In a well-automated call center environment, an agent will spend the bulk of his or her day engaged with customers who have complex problems. Addressing these problems will require a deep level of business knowledge, as well as people skills such as communication and empathy. Each customer interaction, moreover, is likely to be a high-stakes proposition that creates a memorable and lasting impression—one way or the other. Consider the difference between, “I had this crazy problem and they were no help,” versus “I had this crazy problem and they were amazing.”
The increasingly critical and influential role of agents as brand representatives raises profound implications for talent, training, recruitment and compensation strategies. Put bluntly, in a traditional call center, agents typically have been among the least-skilled and lowest-compensated employees in an organization. Skills requirements were basic, and training was rudimentary. That picture is changing rapidly, and progressive call centers are using technology to create new, value-adding roles in the call center.
2 Management Models That Measure Better
The transformational impact of technology on the call center creates a need for new and better metrics. Productivity measures, for example, need to apply to both people and intelligent tools. Consider the agent productivity metric of average handle time (AHT). Traditionally, a low AHT was an indicator of agent diligence and efficiency in closing sales and resolving issues. In a highly automated environment, however, AHT can show that smart robots are underutilized. If routine tasks are automated, human agents should presumably spend more time on each interaction, since each interaction a human is handling should be complex and require time and special attention. As such, a low AHT suggests that automation opportunities are being left on the table.
Ongoing analysis of exception rates can help call centers continually extend the use of rule-based automation. By assessing the frequency of specific exceptions, call center managers can define thresholds of when exceptions should be automated. For example, if an exception occurs, say, once a month, it’s probably not economically viable to assign an RPA technician to configure a bot to handle that exception. Instead, it makes more sense for a human agent to address the issue. However, if the exception is occurring several times a week, it might make sense to automate that particular exception. By pushing the envelope, call centers can not only optimize the efficiency of their bots, but create additional bandwidth for human agents to apply their skill sets.
Prioritizing the outcome of the customer experience rather than internal operational efficiency is also critical. Take the traditional metric of call duration, for example—most customers would prefer a single three-minute call that resolves their issue, versus five calls of 45 seconds with five different agents.
As businesses increasingly focus on outcomes rather than internal metrics, relationships between customers and call center providers will continue to evolve from traditional butts-in-seats models to more partnership-based arrangements.
3 Ongoing Improvement of Customer Engagement
Call centers have focused on enabling omnichannel capabilities that offer multiple customer engagement options, including AI-enabled chat, IVR and mobile apps as well as traditional phone support. The basic—and formidable—challenge lies in optimizing each of these channels, in terms of delivering a quality customer experience as well as optimizing operational efficiency. While that’s a start, call center managers need to ensure that customers can easily and intuitively use the engagement channel they prefer. The fact is, some customers prefer old-fashioned phone service, where they can talk to a person, explain their situation and reach a resolution. Meanwhile, other customers want to do as much as possible from their mobile apps and bristle at the thought of having to waste time calling someone to solve a problem or place an order.
For the customer who prefers phone service, being instructed to “download the app” is annoying. The mobile phone user is equally annoyed if he or she needs to place a call. Even if both users ultimately have their need met, their experience will be tainted by annoyance at not having their preference accommodated. And while seemingly a minor point, even the slightest negative experience can be memorable and go a long way toward informing a customer’s perception of a brand. (“They messed up my order, and getting it fixed was kind of a pain,” versus, “They messed up my order, but getting it fixed was a breeze.”)
In other words, closing the ticket is no longer enough. To deliver a truly high-quality experience, the call center needs to close the ticket in the way the customer prefers. This requires technology platforms that can seamlessly integrate different channels. Intelligent tools that enhance data collection and analytics, meanwhile, can provide insight into individual customer preferences. AI-enabled sentiment analysis, for example, can gauge whether a customer on the phone is irritated or cheery. Granular analytics of the time a particular customer spends on various platforms can shed light on how easy or difficult it is to access a particular platform. While such initiatives involve parsing huge volumes of data for elusive cause/effect linkages, the payoff is deeper understanding of individual customer personas—which is increasingly becoming the gold standard of the industry.