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:
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.
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?
Consider this example: For an evaluation of “Customer Service,” the evaluation criteria may be:
This criteria presents two problems: first, certain points seem to be contradictory to each other. How is an employee to engage a customer in conversation while still keeping a call solely focused on product and service aspects (information typically found only in call scripts)? Or how sincere does thanking the customer for his or her loyalty seem if his or her call was due to doubts and concerns about the brand’s products? And how exactly can one judge an employee’s abilities to resolve future concerns?
Second, expressions such as “proper tone”, “professionalism” and “sincere” are often open to interpretation. What a QM analyst or supervisor may view as cold and unemotional may be what an employee believes to be forthright and professional. When grading employee performance in these areas, how are such contradictions and ambiguities judged? Could a single employee action produce a high assessment score in a certain area along with a lower mark in another?
Finally, one has to consider the discretionary powers of the analyst or manager performing the assessment. What external factors may be influencing their evaluations? Empathy, emotion and even professional or personal relationships may come into play when judging the performance of another. An example may be to compare two employees, one who has a strong evaluation history while the other has struggled. In one case, the evaluator may judge the former more harshly based upon increased expectations, while the latter is scored more favorably simply based off of incremental improvements. Ultimately, do such assessments really paint a clear picture of actual performance?
The Solution is Speech Analytics
The solution to solving the problems inherent with manual QM is truly revolutionary, in that it involves completely altering the employee assessment and reporting source. Implementing speech analytics into QM eliminates all of the shortcomings of manual processes, adding value through both process improvements and resources saved. How so? Consider the obstacles to effective manual monitoring mentioned above, and how speech analytics helps to address them:
How effective has this proven to be compared to manual processes? Comparisons between contact centers implementing both methods have consistently shown the analytical systems to produce assessment results lower than those generated through traditional methods (in most cases, significantly). These studies serve to show just how impactful objectivity can be to the QM process by highlighting how, in many cases, manual evaluations could actually hinder performance rather than look for ways to improve it.
How is this possible, especially when comparison studies have shown evaluation scores to go down across the board when using an analytical evaluator? It’s because it serves to level the playing field between employees, producing tangible unbiased results that don’t penalize employees for being strong in certain areas and weak in others. QM analysts don’t have to worry about the perception that certain individuals are singled out for poor performance due to the fact that the system notifies employees confidentially. They are then allowed to work with groups of employees to help work towards shared goals.
Speech analytics truly represents a revolution in the field of quality monitoring for contact centers. It improves upon all areas of manual monitoring, particularly those that carry with them inherent deficiencies. Analytics evaluators review all calls against a well-defined set of employee skills, producing unbiased actionable information that management and QM teams can then use to identify opportunities where overall performance can be improved to produce higher customer satisfaction rates and significant as well as near-immediate revenue increases.