Check out this funny caricature about quality management processes. Sound familiar?
Obviously, quality assurance is not a laughing matter. Organizations invest great resource in implementing quality assurance programs that focus and motivate employees to improve their interactions with customers. But many tend to focus on individual agent performance, while neglecting the bigger picture: overall business-level performance.
To move beyond the agent level and optimize quality at the business level, organizations should combine quality management (QM) processes with a set of analytics capabilities.
1. Focus on what matters: QM and Business Goals Alignment
Contact centers are under constant pressure to improve the customer experience, increase sales, and manage costs. These business goals require agents that are committed to quality interactions with customers. But there are cases in which it is almost impossible to directly link agents’ actions to specific business metrics or KPIs. For example, measuring agents’ ability to answer specific questions does not necessarily assess whether a customer was satisfied with the service received.
Analytics can be used to predefine criteria and out-of-the-box KPIs, allowing companies to monitor performance based on metrics that reflect business goals. When KPIs are not met, company decision makers are automatically notified of the most problematic interactions, which they can review and evaluate. By immediately drilling down to these interactions, companies can gain a better understanding of the root causes of larger business issues.
2. Catch those one-off calls: Automated Selection
In a standard quality management process, customer interactions are sampled randomly, so we may miss key interactions (the best and the worst) that could provide the most insight. In addition, because of resource constraints and sample size, random sampling is unlikely to be statistically representative. To resolve this issue, many contact centers want to increase their sample size. However, this increased volume presents significant resource constraints.
What can be done to overcome this? Analytics tools use automated selection to easily identify interactions that contain specific keywords, phrases, and concepts or relate to specific skills. For example, companies can choose to evaluate all interactions that involve an up-sell opportunity, a new marketing offering, or even include “irate” words. This can help decision makers identify coaching opportunities or establish best practices based on successful interactions.
3. One size doesn’t fit all: Targeted Coaching
Another task that is probably the most important in the QM process is coaching agents. Now that problem areas have been identified and can be adequately monitored, the next step is to provide appropriate coaching to enhance agents’ behaviors and raise their levels of engagement. Interestingly, Gallup’s 2013 State of the American Workplace report showed that higher levels of employee engagement correlate with better customer outcomes like improved satisfaction scores and loyalty.
Coaching has often been done through generic sessions that provide all agents the same training on the same topics at the same time (usually pre-defined). This “blanket” method is archaic, inefficient, and often results in wasted time and resources. However, by identifying and understanding the knowledge gaps that affect the business KPIs and by pinpointing agents in need of specific training, organizations can provide near real-time targeted coaching and monitor for continuous improvement.
By focusing on quality rather than quantity, companies can stop “wasting” resources on QM processes and significantly decrease the time it takes to get from insights to action.