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CASE STUDY

$3 Million in Savings Through Contact Center Optimization

 
 

Our client was struggling to maintain their target abandonment rate of less than 5 percent, and hoped to improve that.

Our client was struggling to maintain their target abandonment rate of less than 5 percent, and hoped to improve that. Our team evaluated their current aspects of business and helped implement our WFM system.

More than 750,000 consumers call into this retail client’s contact center per month for after-sales support. The company wanted to improve service delivery and reduce costs, yet it was struggling to maintain its target abandonment rate of less than 5 percent. It partnered with us to develop a multi-phased approach to optimize contact center operations through process improvement and workforce management.

First, a review of all aspects of the business was conducted to determine how best to achieve the results needed for this location. The team reviewed volume forecasting, scheduling, call procedures, quality monitoring, training, and knowledge management. After the assessment, a schedule was created for implementation of the various components needed to address the full range of issues. We then brought together a cross-functional group to facilitate improvement workshops. The sessions addressed three critical issues: reducing call handle time, improving the quality monitoring process, and preparing for a new processing structure.

The company adopted our Workforce Management (WFM) system to improve the forecasting and scheduling of resources within the contact center. In its previous WFM process, the difference between forecasted volumes and actual calls could vary as much as 20 percent, and its customized schedules did not adequately match incoming-call patterns.

Our team reviewed its call patterns in order to more accurately predict call volume, looked at average handling time to determine the total call load for each time period, and examined the center’s shrinkage, service level performance, and agent utilization. With the analysis complete, the company was able to develop a clearer picture of its current resource requirements. This formed the basis of new agent schedules that better matched capacity and call volumes, even in this high-variation environment.

The optimization yielded impressive results. Agent lead-time for quality feedback was reduced from 8 days to 24 hours. Offline work was consolidated, and the company identified an annual savings of $1.8 million in overtime and an additional $1.3 million in excess staffing expense.

 

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