Automated workforce management: Changing the face of Call Center management with Automation & AI
When it comes to assimilating data and performing calculations, computers are smarter and faster than humans. AI automates once-prohibitively complex, time-consuming activities, providing solutions in minutes or seconds instead of hours or days.
What is the objective of automated workforce management?
Artificial intelligence and machine learning can remove complexity, time, and human error from forecasting and scheduling.
Both are data-heavy, variable-driven exercises, and few contact centers have the budget for dedicated staff with the expertise to manage forecasting and scheduling. So, the question is, why not leverage AI for creating agent schedules? It's complex; why not simplify it? A schedule created manually is inevitably suboptimal; why not optimize it?
Eleveo’s Automated Scheduling tool enables you to simulate and test schedules reflecting three of the most important factors influencing the end-product: forecasts, break optimization, and channel optimization. Additionally, you can specify times for training and meetings or let the scheduling engine determine the best times for these activities. Regardless, the ability to set your scheduling priorities will improve the agent experience while helping you balance that experience with other organizational objectives, such as SLAs.
With automated workforce management, you’ll be free to devote time previously spent creating schedules to value-add activities, like agent coaching and other quality improvements, all the while, enjoying the peace of mind of knowing you are extracting maximum value from your agent resources – with a single keystroke.
Automated workforce management and quality management
Automated workforce management and quality management are mainly about exercising control over thousands or perhaps tens of thousands of inbound or outgoing interactions - calls, emails, and chats.
It’s necessary for managers to review and analyze hundreds, if not thousands of interactions to ensure customers are having a good experience. It's equal parts science and art, correlating metrics (like Average Handling Time or CSAT) to call outcomes, then analyzing those results for root causes and determining next steps.
Automated speech analytics allows contact center managers to collect and analyze large volumes of calls and interactions. It gives contact centers a data-driven, systematic way of addressing problems quickly and effectively.
The tool can detect and analyze tone of voice, stress levels, keywords and phrases – and even periods of silence. An AI-powered real-time alerting tool, combined with desktop analytics, can track agent and customer voice and screen interactions as they happen, informing supervisors of any cases in need of their immediate attention, allowing them to jump on the call and solve the problem.
A typical scenario might be a case where Average Handling Time (AHT) is too long. The root cause could be that the agent lacks adequate product knowledge to solve the problem, and they spend too much time in the knowledge base. Or, perhaps, the agent doesn't have proper control over the call/caller, and the conversation goes off on a tangent. Another reason might be that the customer doesn't have all the necessary information at hand (e.g., the version number or serial number of a piece of equipment) and has to find it.
Bottom line, automated workforce management and quality management tools will point your agent training and scheduling efforts in the right direction.
It's essential to keep in mind that even as the fascination with AI and its benefits grows (automated workforce management is a prime example), unique human qualities, like empathy and flexibility, will never be obsolete. Where automation’s strengths end, human-to-human relations begin.
Factors that most impact your contact center's long-term success - agent satisfaction, productivity, effectiveness, and retention – hinge less on technology's efficiency than your people-management style. The best-managed contact centers will find the proper balance between AI’s analytical capacity and individual judgment.
We must attach as much value to old-school social and leadership dynamics as automation, artificial intelligence, and machine learning. If we don’t, we will be missing something fundamental.