The Power of Six Sigma in Optimizing Call Center Performance: An Experiment

A deep understanding of Six Sigma methodology can revolutionize the way businesses operate and improve their performance. Case in point, a call center’s function, which often faces challenges in agent performance, customer satisfaction, and operational efficiency. To shed light on this, let’s examine an experiment conducted on a call center, using Six Sigma tools to optimize the performance of its operations.

An Overview of the Experiment

The experiment involved testing various hypotheses around key performance metrics, such as contact resolution rates, agent satisfaction, calls per half hour, and Net Promoter Score (NPS). The goal was to understand the impact of different variables and conditions on these critical performance parameters.

The following presentation provides a visual representation and step-by-step explanation of the experiment conducted on the call center. It breaks down the hypotheses, methodology, findings, and implications of the experiment, offering a deeper understanding of how Six Sigma was leveraged to optimize the call center’s performance.

Key Insights from the Experiment

No Change in Contact Resolution Rates

Contrary to the initial hypothesis that there would be a difference in contact resolution rates before and after control groups, statistical evidence suggested that the contact resolution rates were essentially the same. This was irrespective of whether the agent was in the before or after control group. The average contact resolution for the two different situations showed no statistically significant difference.

Agent Satisfaction Remained Constant

The research also looked at agent satisfaction, and surprisingly, this didn’t change based on whether the agent was in the before or after test group. Despite an apparent lower workload for agents in the after control group, their satisfaction didn’t seem to increase, implying that workload may not be the primary driver of agent satisfaction.

Same Number of Calls Handled

The experiment debunked the assumption that agents in the after control group handled fewer calls than those in the before group. The statistics revealed that both groups handled approximately the same number of calls per half hour, debunking the assumption that performance could be attributed to the number of calls handled.

Consistent Net Promoter Score Across Groups

The Net Promoter Score (NPS), a key indicator of customer satisfaction, remained consistent across all groups. The test, before, and after control groups had very similar NPS scores, suggesting that customers didn’t perceive any difference in the quality of service provided by agents from different groups.

Time of Day and Month Impact on Performance

The study revealed a time-related pattern in some metrics. Occupancy rate, which is the amount of time an agent spends taking calls, declined over the month, and so did the average resolution. However, other metrics like handle time and the mean NPS remained in statistical control throughout the month. The decline in certain metrics over time indicated the presence of special causes of variation, which were outside the experiment’s scope but valuable for future improvement initiatives.

Rater Bias in Satisfaction Ratings

When analyzing agent satisfaction, a significant finding was the presence of rater bias. When comparing the ratings of different raters, one rater, “Rater X,” exhibited inconsistent scoring. By eliminating this rater’s scores, the team achieved consistency in satisfaction ratings.

Occupancy and Agent Satisfaction

A crucial revelation was the decline in agent satisfaction as occupancy increased. Agents who didn’t have to work as much seemed a bit happier than those who had to handle a higher volume of calls, indicating the need for workload management to enhance agent satisfaction.

Management’s Reaction and Future Implications

The management team reacted with “cautious optimism” to the findings, particularly to the 2% difference in contact resolution. The agent satisfaction findings, related to occupancy rates, were a cause for concern. However, this issue was deemed out of scope for the experiment and marked for future investigation.

Some call centers decided to apply these findings, confirming the results with larger groups and deriving good results from it. As the benefits of such a Six Sigma approach become clearer, it’s likely that more call centers will adopt these strategies. This shift will ultimately lead to more streamlined operations, improved agent performance, and enhanced customer satisfaction. The power of Six Sigma, coupled with its robust analytical tools, can play an instrumental role in transforming the call center industry.

Conclusion

The Six Sigma methodology offers a powerful approach to optimizing call center operations. This case study demonstrates how it can be effectively applied to analyze and improve key performance metrics, providing valuable insights that lead to better decision-making and operational strategies. As more businesses embrace Six Sigma, the opportunities for improvement and optimization in the call center industry are vast and promising.