Traditionally, call centers monitor the calls of customer service employees using methods such as random call monitoring selection, consistent monitoring of a small sample size of employees with five being the optimum number, and a checklist that evaluates the efficiency of the call. This is all done with the goal of decreasing variations between calls, increasing customer quality experiences, and combining all of the data gathered in hopes of creating a standard call checklist that meets all of those requirements. However, it has been revealed that not only is this method of call monitoring and quality assurance outdated, it is also relatively ineffective when compared to the information and results that could be obtained through the usage of the increasingly popular Emerging Model.
The Emerging Model of call monitoring is one which is rooted in customer satisfaction, and as such, makes the caller a more integral part of the experience. While the Traditional Method calls for in house agents of the call center to directly monitor calls through random selection and sampling, the Emerging Model calls for monitoring as approved by callers.
Callers have the option to opt in or out of call monitoring, and when they choose to opt in, they are essential to improving customer relations because of their now heightened attention to details and ability to judge the quality of service. The customer service representative remains unaware of the callers choice to allow call monitoring, and will behave as they usually would.
The information from the calls are then sent to those in charge of call quality assurance, and the customer has the added option of being able to leave a formal review of the representative's service through either immediate prompting or later computer-system automated callback survey. This component, along with actual callers instead of in house agents posing as callers, makes the Emerging Model more effective for evaluating.
While in the Traditional Model the data tends not to vary too much because of ineffective sampling resulting in a bad curve, the Emerging Model allows for the detection of normal patterns as well as the extremes they wish to avoid. The data is more comprehensive as well as well-rounded because of the bigger sampling size. Though it takes effort to phase out an old system in order to get employees acquainted with a new system, when the benefits of the new system far supersede the benefits of the system currently in place, it is best that businesses make the choice that best helps shape and define their futures.