In today’s intensely competitive markets, companies must strive to meet customer expectations during every interaction, and interactions occur through many channels. Our benchmark research into next-generation customer engagement finds that customers use up to 17 channels of engagement. Some channels involve assisted service from employees of the company, and some use self-service technologies such as interactive voice response (IVR), websites, mobile apps and social media, also known as digital service. Although the use of self-service is increasing, the research finds that organizations still expect volumes of assisted interactions to grow, albeit more slowly. The research also shows that the employees customers interact with may work in almost any line of business, including marketing, sales, the contact center, finance and human resources. These challenges require organizations to focus on people, processes, information and technology to optimize the performance of the workforce.
To meet customer expectations during assisted interactions, companies must have the right number of skilled employees available, including cases in which the customer begins in a digital channel but switches to assisted service, and handle a number of functions:
- Route interactions to the employee most likely to satisfy a particular customer and situation.
- Build work schedules that match agents’ numbers and skills to expected volumes and types of interactions. To do that requires systems that are responsive and flexible enough to handle unexpected events such as sudden surges of interactions or employee illness and absences.
- Assess the performance of agents and other employees handing interactions, and determine future training and coaching for individual needs.
- Motivate employees to improve their performance.
- Give employees access to all information and systems that can help resolve issues, as often as possible at the first attempt.
- Enable employees to find and collaborate with others who can help resolve issues without having to call the customer back.
- Assess the overall performance of interaction handling to ensure it remains within operational guidelines while meeting business objectives, and plan process improvements to optimize the customer journey and the agent experience.
To accomplish all these tasks, companies need to use a combination of systems, which collectively are known as workforce optimization. Our research into next-generation workforce optimization finds that the three types of systems most commonly in use are call recording (78%), quality management (70%) and workforce management (45%), although coaching (17%) and e-learning (15%) are the most likely to be deployed over the next two years. In each of these categories both conventional systems and newer, more capable ones are available.
Regarding the most popular kind we note that companies have been recording calls for many years but that few use them to full advantage. Typically, companies listen to a small percentage of calls and use this to manually complete agent performance scorecards. More innovative organizations use speech analytics systems that enable them to utilize all calls, automate much of the agent performance assessment process and most importantly link this information with customer feedback. Such companies record all types of interactions and use multiple forms of analytics systems to create an even broader picture of agent performance and customer experience.
Quality management systems are also mature but typically support a manual process of creating and filling out scorecards for different types of interactions. Here again more advanced systems use analytics to automate much of the process, including calculation of agent quality scores.
Workforce management systems typically use historical data about interaction patterns to produce work schedules that optimize available resources and meet operational targets. More advanced systems monitor employee performance against those schedules and include capabilities to optimize short-term agent utilization, for example by filling idle time with training and coaching.
Coaching and e-learning is a less mature category. Conventional products use the output from quality monitoring and analytics to identify coaching and training tasks, which can then be scheduled using workforce management. The most advanced systems can extract data from competed interactions to illustrate areas in need of improvement, or identify the performance of other employees who handle interactions using best practices.
As well as these advances in core workforce optimization applications, there are a number of potential game changers. The first is analytics. As I have already highlighted, analytics is increasingly important to workforce optimization. Participants in our next-generation customer engagement research said it will have the greatest impact of any application on customer and employee satisfaction. Advanced speech and text analytics, used in combination with structured data analytics, can produce a comprehensive view of interaction handling, employee performance and customer satisfaction. It can spot trends and issues, and predict likely outcomes of future interactions. This information can be used to forecast future resource requirements, suggest process changes, identify training and coaching needs, and automate calculation of metrics focused on both customers and employees, such as first-contact-resolution rates across channels, customer effort scores, customer lifetime value and agent performance. These metrics can become an integral part of gamification techniques that track and reward agents for performance in day-to-day operations and for taking part in training, coaching and game-playing sessions to help hone skills.
Technology integration is having an impact of workforce optimization. Nearly half (48%) of participants in our next-generation workforce optimization research said that it is important for these applications to be integrated. If they are, what have previously been stand-alone processes can flow across application boundaries: For example, customer feedback can link to quality monitoring, analytics can support information-driven processes in multiple applications, and e-learning sessions can be automatically inserted into agent schedules. Integration of applications also supports a closed-loop approach to workforce optimization that uses analytics to assess past performance, identify areas for improvement and monitor the impact of changes.
Other innovations also are having impacts. Many vendors now support access to their systems from mobile devices so that employees can work away from their desk – for instance, supervisors walking the contact center floor – or home. Cloud computing opens up the opportunity for small and midsize companies to access capabilities similar to those designed for large enterprises.
As I said, our research shows that assisted service has and will continue to play a key role in interacting with customers, and these people and processes must be managed. Increasing demands from customers, multiple channels of engagement, greater volumes of interactions and more complex interactions all increase the urgency of deploying the right number of skilled employees to deal with customers. I therefore recommend to organizations that rely on outdated systems, particularly spreadsheets, to manage those tasks evaluate how more advanced, analytics-driven systems can improve the performance of employees and consequently the customer experience.
Richard J. Snow
VP & Research Director, Customer