An effective Workforce Management organization ensures a contact center has the right number of agents, with the right skills, in place to deliver the desired member experience in the most efficient manner. Forecasting call volume and determining staffing needs are becoming more complicated due to the multiple channels that are now available for customer service. At the same time, the advanced data analytics that are now available to forecasters allow them to better predict call drivers, the events that trigger calls. As a result, the following trends in Workforce Management (WFM) have emerged:
Using data analytics for multiple channel demand forecasting and more robust driver analysis
An increase in data and analytics has resulted in improved demand forecasting and more robust driver analyses. Multiple channel usage is increasing for many contact issues since most people try self-service options before calling. Integrating analytics concerning the use of self-service channels into WFM modeling improves forecasting accuracy and highlights channel optimization opportunities that positively impact contact center utilization. Companies know online and self-service channels are preferred by customers and are investing in these channels. This typically leads to a decrease in transactional phone volume. Since easier transactions are being completed via self-service, forecasters must plan for fewer, but more complex calls that often come with higher Average Handle Times.
Consumer preference for self-service (online/live chat/chatbot) complicates staffing
The growth of online options and self-service has made staffing more complex. Live chat and chatbots equipped with robust search engines affect call volume and must be part of the staffing equation. An estimated 60%-70% of customers try to get their questions answered online first. They turn to live chat or chatbots next and only call as a last resort. Different channels require different staffs and skillsets. Those who handle phone calls well, don’t always know how to handle chat and are not always accurate typists. Those who are skilled at crafting concise messaging via email or chat may lack phone skills. Forecasters must understand who is using each channel and for what, what makes customers switch channels, and how multiple channel usage impacts total customer contacts. Companies must recognize that customers view their problem resolution experiences across all the channels they use to resolve their issues. Since most transactions now happen on channels other than phone, when people do call in, it’s often about a more complex issue that they can’t resolve online. Tougher questions require more skilled agents that are better able to handle complex issues, frustrated customers, and improved reference materials and tools.
Optimizing the use of in-house and out-source teams (including offshore)
Many companies have out-sourced (and possibly offshore) teams which act like extensions of their in-house contact centers but offer more flexibility. They can move resources across programs, hire faster and are great for handling anticipated call volume peaks. Typically, calls are assigned to each team based on volume, expertise, or availability. For example, the easier calls may be handled by out-source teams while complex calls are answered in-house, by highly trained agents. Predictive intent is a tool that uses caller data to anticipate the purpose of each call and route calls appropriately. Companies that leverage data analytics to gain greater insight into call types are more able to optimize their workforces by routing calls to the most cost-effective resources.
Utilizing Workforce Management data and metrics to improve First Call Resolution
First Call Resolution (FCR), the number of contacts necessary to get an issue resolved, is a key metric that Workforce Management teams manage inside contact centers. If a caller calls 2-3 times in a week, the first agent didn’t resolve the issue and may require additional coaching, training, or tools. It’s important to measure the effectiveness of the out-sourced and in-house teams because if First Call Resolution rates are lower for out-sourced teams than in-house teams, some calls are being paid for twice. This issue is masked in cost per call metrics, so understanding all of the important operational KPIs is important to drive the desired customer experience and cost model. If the Advanced Data Analytics and Workforce Management teams’ data indicates that a particular call type is being transferred back more frequently, they may decide to keep those calls in-house.
Leveraging Work-from-Home as a strategy for contact center optimization
Many organizations leverage work-from-home agents. This allows companies to save on real estate costs and attract and retain higher skilled talent. It increases efficiencies by offering alternatives for managers to address the needed flexibility and coverage for split shifts, shorter shifts, and oddly timed shifts. Real-time management is essential for managing a work-from-home staff because the command center, not the supervisors, must watch the queues to ensure high levels of accessibility and load balance volumes to achieve consistent and acceptable service levels. Effective WFM modeling leverages data analytics regarding the productivity, availability, and overall performance of work-from-home agents. High-level assumptions are not sufficient when work-from-home agents become an increasing portion of the workforce.
The improvements in demand forecasting and scheduling accuracy that Workforce Management and data analytics provide ensure contact center resources are utilized more efficiently and cost-effectively. To learn more about trends in Workforce Management and how your company can benefit from them, contact us or read about it here.