Speech Analytics

Objectivity Contact center reliability in terms of delivering the value promised to customers depends largely on the performance of individual employees. Thus, in order to ensure that optimal effort is given on each and every call, management teams are required to implement contact center quality monitoring (QM). In most cases, this task is performed by a dedicated QM staff whose goal it is to ensure that required procedures are met while also conveying the messages and tones unique to each client. While QM focuses on assessing and improving an employee’s individual skills, its general purpose is two-fold: to meet the performance demands of contact center clients while also ensuring that operations remain compliant with industry standards. Manual Monitoring – An Inherently Challenging Process Typically, the QM process at most call centers involves an individual agent (be it a manager, supervisor or dedicated QM analyst) listening to individual calls and grading out an employee’s performance. After each assessment the results must then be shared through the appropriate channels, beginning with supervisors or shift managers and then with the individual employees themselves. These individual one-on-one sessions between employee and analyst or supervisor and analyst are essential in order to identify areas that need improvement. Finally, follow-up evaluations are required in order to ensure that education has been effective at helping employees improve the customer experience on his or her calls. While QM efforts are vital for improving contact center performance, the aforementioned process carries with it a number of inherent inefficiencies. These include: Resource management: Managing QM can force executive teams to walk a tight line between meeting the demands of client output and improving processes and performance. When developing a QM strategy, many often first look to supervisors and managers to perform these tasks. While their familiarity with the skills and weaknesses of their individual team members may offer valuable information to this process, adding QM to their already full schedules often requires asking them to sacrifice time and effort being dedicated to other areas. Oddly enough, the net impact of having supervisors perform QM is often a decrease in overall performance due to less attention being paid to other vital areas of operation. The next solution would then seem to be to create a dedicated QM staff whose workflow would be centered on call evaluations and employee training. This eliminates the need for management to be extensively involved in the QM process and creates a team of subject matter experts that employees may be able to rely upon as a resource. However, it is important to keep in mind that creating such a resource would likely require pulling employees that have demonstrated the highest levels of performance off of the phones in order to effectively perform this function. While the anticipated payoff is that they would then be able to convey their skills on to other employees, the question becomes how long the center would be able to wait for the rest of its staff to replace the newly named QM analysts’ levels of performance? This leads to the final concern that manual QM presents in regards to resource management: time. If a team of supervisors or QM analysts is expected to turn the trends discovered during call evaluations into actionable information that helps employees improve customer engagement, then they need the time to both listen to calls and provide education. While the expectation is already in place, that call monitoring will be a primary function of a QM team, the amount of time needed to follow up with employees and managers if often not accounted for. Once evaluations have been done, the QM team must then pull supervisors away from their regular tasks to share results, and then pull employees off the phone to provide education. For contact centers already straining to deliver optimal output levels, such allocations of time may prove to be too great of a cost. Limited sample sizes: Because the QM process can be so expensive and does require so much time, managers or analysts can only afford to perform their assessments using small sample sizes. In many cases, the actual rate of assessed calls may often represent less than one percent of a contact center’s total output. One has to wonder if such a limited volume is able to produce an accurate depiction of overall employee performance. Employees may argue that judging their capabilities on the phone off of a small random sample of their calls puts them at an inherent disadvantage. Depending upon their specific roles and responsibilities, they may feel more comfortable in certain areas than in others. Thus, while evaluating a call for a client or product with whom they are unfamiliar may serve to highlight their weaknesses, it fails to identify those areas which they may view as their strengths. Along with offering a potentially unbalanced assessment of an employee’s skills (or lack thereof), random call monitoring also may fail to show if employee education has truly taken hold. For example, if an employee were marked for a reassessment after having received training and education, reviewing a call from the week following said training may not show what he or she has learned and implemented than one from two-three weeks later. The trouble is, when pulling random call samples, QM analysts often have little control over the time frames from which their calls come from. One might argue that the solution to the problem of limited sample sizes is simply to increase his or her QM staff. However, wouldn’t such action simply serve as an example of throwing more resources at a process that’s already been proven to be somewhat ineffective? Objectivity: The final challenge that comes with manual QM monitoring is delivering objective results. When preparing the criteria that will be used to assess employee performance, the question has to be asked as to the basis used to develop it. Is it being prepared by upper-level executives based solely off of perceived client expectations, or former