The Challenge
BUPA, a leading international insurance company, faced the challenge of managing a high volume of calls across their UK-wide contact centre. Despite having adequate staffing levels, they struggled with accurately allocating and planning resources due to insufficient insights into call data. They also experienced issues with customers calling incorrect numbers, leading to misrouted calls and inefficiencies.
The Solution
To tackle these issues, a comprehensive approach was implemented. First, a detailed analysis of call characteristics and patterns was conducted. This involved examining call data to accurately determine demand and resource needs. Historical data was then used to create precise data feeds for the Workforce Management System, incorporating variables such as channel, time of day, and call type.
A predictive algorithm was developed based on historical patterns, which helped forecast the required number of Customer Service Agents and the skills they needed. This algorithm used data from various features to anticipate staffing needs more accurately. Additionally, enhanced data analysis capabilities were introduced, allowing for real-time, multi-dimensional insights that improved decision-making and strategic planning.
The Results
The new approach enabled BUPA to more effectively allocate and plan resources, reducing the incidence of misrouted calls and enhancing overall operational efficiency. The predictive algorithm provided accurate staffing forecasts, ensuring that the contact centre could meet demand effectively. The enhanced data analysis capabilities facilitated better-informed decisions and strategic planning, leading to improved service quality and operational performance.