Sizing Workforce for Call Centers
Optimizing workforce sizing through advanced forecasting and chatbots automation. By developing predictive models for contact volume, optimizing handling times, and implementing dynamic scheduling that considered employee constraints and peak times, we achieved remarkable efficiency gains. Automation through chatbots, particularly in routine and non complex contact types, further streamlined operations. The result was not only cost savings and improved customer experiences but also increased employee satisfaction. This use case showcases our commitment to harnessing AI for transformative solutions, ensuring adaptable and responsive call center operations on a global scale.
Challenges Faced
The client, operating call centers, encountered the challenge of efficiently sizing the workforce to handle varying contact volumes, considering factors such as average handling time, employee constraints, shifts, and peak times. Challenges included managing high volumes fluctuation per hour of the day and day of the week, addressing employees' breaks and shifts, and optimizing workforce allocation based on contact types during peak and valley times.
Our Approach
Contact Volume Forecasting:
Developed predictive models for forecasting contact volumes, considering historical data, seasonality, and external factors influencing call center traffic.
Utilized advanced algorithms to provide accurate predictions for different timeframes, allowing proactive workforce planning.
Average Handling Time Optimization:
Analyzed historical data to optimize the average handling time for different types of contacts.
Implemented strategies to reduce handling time through training programs and process improvements.
Employee Constraints Management:
Developed a dynamic scheduling system that considered employee breaks, shifts, and constraints to ensure optimal workforce utilization.
Implemented automation for break scheduling, minimizing disruptions to operational efficiency.
Peak and Valley Time Strategies:
Identified peak and valley times through data analysis and implemented tailored workforce strategies to handle fluctuations in contact volumes.
Utilized clustering algorithms to categorize different times of the day for targeted staffing levels.
Clusterization and Automation:
Employed clusterization techniques to group similar types of contacts and optimize resource allocation for each cluster.
Automated routine and repetitive contact types through chatbots to enhance efficiency and allow human agents to focus on more complex interactions.
Achievements
Efficient Workforce Sizing: The predictive models and dynamic scheduling system led to an efficient allocation of resources, ensuring the right number of agents at all times to meet demand.
Improved Employee Satisfaction: Optimized scheduling, automated breaks, and handling time improvements contributed to a better work environment, increasing employee satisfaction and retention.
Enhanced Customer Experience: Automation of routine contacts and optimized handling times resulted in a faster and more effective customer service experience.
Cost Savings: Efficient workforce allocation and automation led to cost savings by minimizing overstaffing during low-demand periods.
Long Term Impact
The implemented solution not only addressed immediate workforce sizing challenges but also created a foundation for adaptive and responsive call center operations. Continuous monitoring and refinement of the system ensured ongoing improvements, aligning workforce size with evolving contact patterns and operational needs.