The insurer staffs a call center to answer client questions on policy adjustments that had a potential for creating confusion and dissatisfaction. With hundreds of thousands of policies being re-rated on a rolling schedule of notifications, the status quo required a temporary but extended ramp-up of call center staffing that would have increased expenses by millions of dollars. The client asked James (representing an affiliate consulting organization) to perform data analytics that would identify alternatives to the large growth of the call center and still mitigate any customer dissatisfaction and confusion.
This was important to the client because the management knew that a tremendous amount of data existed, but had no systematic way of analyzing the data such that decisions with significant ramifications could be made.
James applied a process-centered approach that looked at the output of the call center, the inputs (calls) triggered by re-rate notification, and the in-process measures. By applying Value-Stream mapping techniques, waste (non-value added) investigation and removal, and root-cause analysis, he was able to demonstrate that by reducing input confusion and providing more automatic alternatives to clients, they could reduce the incoming volume of calls and thus minimize the additional hiring of call center representatives. Additionally, increased efficiency of the call process itself caused each call to be handled more promptly, creating greater capacity within the existing call center staff.
Management was very satisfied with the success of this approach, and allowed the further use of these techniques to other processes within the provision of the Long-Term Care process, and asked James and his business partner to provide Black Belt training and Green Belt training to approximately 20 employees. These trained resources led a number of projects that further improved processes.
The company estimates that the combined effects of these efforts over a 2-year period have benefited the processes by more than $3 million. They have demonstrated their repeated ability to increase throughput capacity without increases in staffing.