Call Center Transformation Case Study
Situation
A rapidly growing insurance company expanding through acquisitions faced severe inefficiencies in its customer service operations. Manual processes and outdated technologies led to high operational costs and poor scalability. The 1,000-agent call center handled approximately 2 million calls per month, and without intervention, the company planned to hire 300 additional agents at an annual cost of $12 million to maintain service levels.
Task
The company needed scalable, enterprise-grade technology to enhance service capabilities, improve efficiency, and reduce costs. A task force of five, reporting to the CIO, was assembled to analyze, strategize, and implement a major call center transformation.
Actions Taken
Conducted on-site observations and "chair-shared" with call center representatives across multiple locations.
Tracked, Logged, and Analyzed 3,000 customer calls, capturing key insights and metrics (e.g., authentication time, dispositioning, transfers, action steps during call, after-call work).
Reviewed agent skills, queues, call flows, and routing logic, identifying inefficiencies and automation opportunities.
Leveraged data-driven insights to pinpoint manual steps suitable for automation.
Developed a business case with metrics and real-life examples to secure C-suite approval and funding.
Implemented the following solutions:
Genesys with optimized call routing capabilities and automation.
System integrations for screen pop functionality, reducing manual CSR input tasks.
Enhanced self-service options for customers, eliminating unnecessary agent interactions.
Results
15% of total inbound calls eliminated through self-service automation (300K calls eliminated monthly). The company canceled its plan to hire 300 additional agents, saving $12M annually.
Customer authentication process fully automated, reducing repetitive non-value added manual tasks and improving the customer experience.
Simple call types automated, allowing agents to focus on complex, highest-value customer interactions.
Key Takeaways & Lessons Learned
Understanding key baseline call center metrics (e.g., AHT, hold time, queue time, after-call work, transfers, first-call resolution) is critical for optimizing operations.
Automating simple call types increases average handle time (AHT) for remaining calls, but total call volume decreases, improving overall efficiency.
Call center volumes follow predictable patterns (e.g., time of day, day of the week, post-holiday spikes). Leveraging data visualization can improve resource planning and call center scheduling.
