As customers continue to increase their use of digital channels and self-service tools for customer service, it’s only natural that the volume of calls hitting contact centers has been shrinking. Indeed, while volume in every digital channel continues to grow for customer service, contact centers have seen call volumes decrease by 12 percent, according to the 2016 Global Contact Centre Benchmarking Report conducted by Dimension Data. What this also means is that a growing percentage of calls that are reaching the contact center represent more complex queries that customers are seeking human assistance to resolve.
Fortunately, there are steps that contact center leaders can take to help agents address call complexity. One is by increasing the use of speech analytics tools to identify the reasons why customers are calling and whether certain types of calls tend to be clustered at particular times. Blended with the use of workforce management tools, analytics can help in scheduling agents with the right skills at the right times to handle certain types of customer queries.
Moreover, the use of speech analytics can help contact center supervisors to identify coaching opportunities with agents, including instances where agents are having trouble resolving a complex customer query.
Meanwhile, the use of unified communications tools such as WebRTC can enable agents to connect directly with other subject matter experts in the company in helping customers to resolve knotty issues that may fall outside their knowledge area.
Because agents are fielding more complicated calls that can touch on all aspects of the customer relationship, agents should also have simple desktop tools that make it easy for them to access all customer account information using a single screen. This is one of the reasons why cloud contact center platforms are becoming more popular as they can provide agents access to a full history of a customer’s omnichannel interactions along with entry to a customer’s transactional history.
The rise in call complexity is also forcing contact center leaders to re-examine the operational metrics they use to gauge efficiency and effectiveness. For instance, while some leaders may choose to continue using metrics such as average handle time to help determine the length of different types of calls, supervisors should place greater emphasis on metrics such as first contact resolution. When customers do reach out to an agent for support, they expect that agent to meet all of their needs and not require them to call back for additional help.
Over time, complex queries won’t always fall upon agents to address. As self-service tools become more intuitive and make greater use of artificial intelligence and machine learning capabilities, some customers – especially those who prefer to self-serve – will increase their use of sophisticated automated technologies.
