The goal of this project is to develop a robust forecasting model using machine learning models that accurately predicts daily and monthly interaction volumes across all inbound communication channels.
Additionally, it will determine the optimal number of agents required to efficiently handle these interactions. By achieving this, we aim to:
- Optimize workforce allocation across different teams and time periods (hourly or daily).
- Improve resource utilization while maintaining service quality.
- Enhance operational efficiency and overall customer experience.
In a dynamic contact center environment, managing inbound interactions effectively is crucial for maintaining high service levels and customer satisfaction. However, fluctuations in daily and monthly interaction volumes across various communication channels—voice calls, emails, live chat, and social media—present a significant challenge. Without accurate forecasting and optimal workforce planning, businesses risk overstaffing (leading to unnecessary costs) or understaffing (resulting in long wait times and poor customer experience).
Audience: contact center, the bank , customers
