How Machine Learning Is Optimizing Employee Schedules and Experience
5 de January de 2023
Delivering a great customer experience (CX) is at the heart of any call center strategy, but what about delivering that same great employee experience (EX)?
The “Great Resignation”—a social phenomenon triggered by COVID-19 that marked a turning point in the way employees view work—has forced companies to rethink how they recruit and retain the most talented team members, and contact centers are no exception.
It’s no longer just about getting the most out of each agent. You also have to consider the possibility of making your life easier, and increasing your job satisfaction, without reducing service levels or profitability. Tactics that were once seen as benefits (hybrid work schedules, for example) have now become a reality.
Today’s employees are demanding more autonomy and control over their schedules, and contact centers are leaning on their Workforce Management (WFM) solutions to meet the new mandate.
What Role Do Machine Learning and WFM Play in Call Centers?
1) Optimization of Schedules
Modern WFM solutions can help give agents the flexible hours they want without negatively impacting the customer experience (CX). In this sense, Machine Learning (ML) and Artificial Intelligence (AI) help predict staffing needs with a higher degree of accuracy than traditional WFM systems.
2) Compliance with Service Level Agreements
Machine learning can also help contact centers protect CX by identifying when coverage is at risk of not meeting service level agreements and adjusting it on the fly, changing breaks, lunches and even start times and the duration of the shifts (as long as they continue to comply with the work rules defined for each employee) in real time.
Once the changes have been made, it is advisable to carry out an analysis to ensure that they have had the desired effect. If new improvement opportunities are discovered, further adjustments are made and the process continues.
3) Guarantee of Fairness in Scheduling
The systems used to optimize scheduling must take legality and equity into account. So if an agent feels the process isn’t fair, or doesn’t understand it, engagement suffers and talent retention is likely to suffer as a result.
Therefore, it is necessary to implement “equity intelligence”, a model that uses machine learning to verify that all schedules comply with labor laws, union requirements and the rules that the call center establishes for its staffing models.
For example, some employees may volunteer to work certain days, weekends, or holidays, while others want to rotate through assigned hours on a consistent basis. Machine learning monitors shift sequences and makes sure that less desirable schedules, such as back-to-back shifts, are distributed fairly or avoided whenever possible.
4) Adaptation of Remote and Hybrid Work Models
As more contact centers transition to remote work and hybrid staffing arrangements, WFM solutions must evolve with the times. If employees are only in the office a few days a week, for example, those days should be optimized for training sessions and team meetings. Good WFM software allows call centers to set policy rules that help ensure that agents and managers can maximize time when they meet in the office.
For any WFM solution to meet the needs of the contact center in the era of the Great Resignation, it must be able to balance the demands of the business with those of the employees for a better work-life balance. By using Machine Learning capabilities to enable fairness in scheduling, skills usage assessment, and efficiency, it enables call centers to reduce schedule decline by 8% and administrative hours by 9%, while increasing agent occupancy by 9%.