Consumers demand nothing less than the most personalised, timely, and efficient customer service. Unfortunately, traditional methods of contact centre staff capacity planning are not keeping up with the evolving needs of today’s post-pandemic marketplace. The inefficiency that results has a price—in the form of hidden costs due to attrition, absenteeism, and low agent engagement—as well as a negative impact on customer service quality. Organisations could reduce that inefficiency by adopting a scenario and risk-based approach to planning.

Powered by advanced technologies like artificial intelligence and automation, it’s now possible to adapt, learn, and respond in real-time to meet the new demands of customers. Coupled with risk-assessed capacity plans, which focus on customer demand as well as on the supply of workforce capacity, this innovative approach uses technology and data-driven insights to manage a wide range of customer service scenarios. As a result, organisations can deliver great service even under the most challenging conditions with more engaged and effective contact centre agents.

Overcoming customer service teams’ age-old hurdles

A significant shortcoming of traditional planning is its reliance on a limited range of purely quantitative metrics. Many organisations measure the quality of customer service delivery only by the number of calls agents handle or how quickly they answer calls. But this approach ignores crucial “human” metrics such as customer satisfaction and issue resolution rates.

Failure to track these important success indicators deprives contact centre teams of critical information that could help them make informed decisions about resource allocation, training, and coaching. Measuring a broader range of indicators can identify areas for improvement, enabling teams to adapt accordingly and ensure both operational efficiency and customer satisfaction.

Similarly, some organisations measure success solely by how quickly issues are resolved, leaving out the arguably more important quality of the resolution. This approach can inadvertently encourage agents to rush through calls to meet more superficial performance measures in place, leading to incomplete or ineffective solutions that result in repeat contact and disgruntled customers.

Furthermore, traditional planning approaches typically prioritise short-term, top-down financial targets, which can lead to poor decisions that compromise service quality and the customer experience. For example, reducing staffing levels without risk-based capacity planning to meet specific financial goals may provide short-term savings, but it can lead to longer wait times, reduced customer satisfaction, higher absences, increased attrition, and a detrimental impact on the organisation’s reputation.

Overcoming these challenges requires customer service teams to consider a broader range of performance and satisfaction indicators rather than focusing solely on quantitative measures. Embracing an innovative and visionary approach with a dynamic way of planning— that can adjust to the ever-changing needs of customers and the organisation—is critical.

Utilising advanced technologies for capacity planning success

Today, organisations have access to revolutionary technologies that can take their customer service delivery to new heights. With the help of chatbots, AI-powered virtual assistants, and advanced data analytics tools, organisations can provide more efficient and effective service by embedding these tools into the customer journey. By embracing these technologies, customer service teams can also elevate their planning and service delivery to transform the way they operate.

Predictive analytics, which involves using machine learning algorithms and data, enables contact centre teams to forecast demand, identify trends, and anticipate customer needs with greater accuracy. These capabilities can also be applied to staff capacity planning. By monitoring agent well-being and predicting stress and burn-out conditions, managers, and supervisors can intervene at appropriate points to help reduce the risk of absence and attrition.

By harnessing real-time information, predictive analytics empowers customer service teams to make informed decisions about resource allocation, staffing profiles, and optimal times to invest in agent training, along with targeted coaching and optimised service delivery. All these things can lead to more efficient operations, greater customer satisfaction, and vastly improved business outcomes.

Paired with leading workforce management software, which supports flexible scheduling and enhanced forecasting processes, the potential benefits are significant. Contact centre teams can optimise staffing, recover lost time, reduce costs, and enhance employee satisfaction through real-time intelligent automation.

This includes balancing cross-channel demand with multiskilled resources, utilising available time with automated training and coaching alerts, and offering flexible work time adjustments, all without manual intervention. Additionally, some workforce management software solutions either include or can easily integrate advanced analytics and reporting capabilities, allowing customer service teams to track and analyse key performance metrics and make data-driven decisions.

Assisted by cloud-based solutions, customer service teams can access the data and leverage the insights required to plan more effectively by quickly and easily adjusting capacity and resources to meet changing demand. With access to advanced artificial intelligence capabilities, self-service automation, and omnichannel service support, human agents can focus on more complex interactions. Organisations are then equipped to deliver more personalised and efficient customer experiences.

Embarking on an era of dynamic planning

Organisations face a major challenge when they rely on top-down financial targeting to set capacity planning targets. It can lead to inadequate plans which don’t factor in a range of operating scenarios, as it’s often based on incomplete data or historical trends that don’t use a risk-based methodology. This outdated approach can lead contact centre to underestimate or misjudge capacity requirements, which increases costs and creates unhappy customers.

In contrast, cutting-edge tools like Monte Carlo Simulation empower organisations to make more informed decisions. This innovative technique uses advanced statistical algorithms to simulate various outcomes for a given scenario, providing a detailed analysis of potential risks and rewards. In fact, independent communities like WFM Labs are championing this technology and approach to help contact centres end the cycle of missing service levels.

Contact centres that harness the power of Monte Carlo Simulation and embrace a people-first approach to dynamic planning will be better equipped to navigate the rapidly changing customer landscape. By considering uncertainty and variability, Monte Carlo anticipates various outcomes and incorporates the true costs and impacts of capacity planning, including customer demand and training and coaching time. In addition, it can assess the associated risks, enabling organisations to identify potential issues and develop comprehensive planning strategies that can be executed with real time optimised delivery.

Outdated planning approaches are no match for the evolving expectations of today’s customers. To stay ahead of the game, customer service teams need to embrace a dynamic planning strategy, infused with innovative technologies and cutting-edge methodologies like Monte Carlo Simulation and dynamic planning. This will enable them to be agile, adaptable and, most importantly, equipped to deliver a top-tier customer experience that will blow their competition out of the water.

About the Author

Paul Milloy, Business Consultant, Intradiem

Paul Milloy has almost 40 years of operational experience in customer facing activities & roles ranging from engineering, customer operations management to business change and improvement and latterly in resource planning.

He was Resource Planning Director at Centrica between 2016 and 2020 running an integrated planning function for British Gas Customer Operations, responsible for all aspects of end-to-end resource planning for all customer contact channels with over 20 million contacts per annum and an agent population of 8000.

In January 2021, Paul joined Intradiem as Chair of the Forefront Executive Council for UK&I, leading on thought leadership engagement and senior leader networking, and supporting on brand and solution awareness and business development.