Andrea Matsuda at NICE explains how machine learning is optimising schedules and employee experience.
Providing a great customer experience is at the centre of every contact centre strategy, but what about providing that same great experience to one’s employees?
The “Great Resignation” has forced businesses to rethink how they recruit and retain valued crew members, and contact centres are no exception: More than 60% of contact centres surveyed in 2022 by NICE said they are actively trying to retain talent.
It’s no longer just about getting the most out of every agent. Consideration must also be given to making those agents’ lives easier―and their job satisfaction higher―without sacrificing service levels or profitability. Tactics formerly seen as perks—hybrid work schedules, for example—have now become table stakes.
Today’s employees demand more autonomy and control over their schedules, and contact centres are leaning on their workforce management (WFM) solutions to deliver on the new mandate.
Modern WFM solutions can help give agents the flexible schedules they desire without negatively impacting the customer experience.
Our WFM’s approach utilizes a closed-loop application that leverages the power of machine learning and artificial intelligence to predict staffing needs with a higher degree of accuracy than traditional WFM systems.
The solution starts with educated guesses then learns and fine-tunes information with each successive iteration.
Each user can set how many passes and how much time should be allowed for the system to run through the process—a capability that’s particularly important in contact centres that offer flexible scheduling, which typically requires more time and passes than in environments with fixed scheduling rules.
Meeting Service-Level Agreements
Machine learning can also help contact centres protect the customer experience. The WFM identifies when coverage is at risk of falling short of service-level agreements and adjust on the fly, modifying breaks, lunches, and even shift start times and length (provided they still comply with the work rules defined for each employee) in real time.
Once changes are made, the WFM performs another round of analysis to ensure that the changes have the desired effect. If new opportunities for improvement are discovered, additional adjustments are made, and the process continues.
Ensuring Scheduling Fairness
Keep in mind: Systems used to optimize scheduling must account for legality and fairness. If an agent doesn’t feel the process is fair―or does not understand it―engagement takes a hit, and retention is likely to increase as a result.
That’s why our WFM uses what’s called “fairness intelligence,” a model that employs machine learning to verify that all schedules fall in line with local labor laws, union requirements, and the rules the contact centre sets for its staffing models.
For example, some employees may volunteer to work certain days of the week, weekends, or holidays, while others want to be rotated through assignments on a consistent basis.
The WFM leverages machine learning to monitor the sequences of shifts to make sure less-desirable arrangements―such as back-to-back shifts―are either fairly distributed or avoided whenever possible.
Accommodating Remote and Hybrid Models
As more contact centres transition to work-from-home 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 must be optimized for face-to-face interactions, such as coaching sessions and team meetings. Our WFM enables contact centres to set policy rules that help ensure that agents and managers can maximize time when they’re in the office at the same time.
For any WFM solution to meet the contact centre’s needs in the era of the Great Resignation, it must be able to balance the demands of the business with employee demands for better work-life balance.This blog post has been re-published by kind permission of NICE – View the Original Article
For more information about NICE - visit the NICE Website
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