“We are losing customers? Why?” Beth Weltman, the Chief Operating Officer and coowner of Haven Malware, said. Viruses, worms, trojans, and malicious bots create constant calls to Haven’s call center. Beth turned toward Jake Marta, the call center manager, and asked, “Do we have a problem with answering customer questions? Is it a training problem? Can we forecast customer demand? A problem of call center capacity? Do our customers wait too long?” “We don’t know,” Jake replied in a hotly debated company meeting. No one else spoke. “Then analyze the data and find out! I want answers at next week’s meeting,” Beth shouted, leaving the conference room. Jake lowered his head and realized that he didn’t know how to analyze the data. He was an excellent manager of people, recruiting, training, and promotion. He was a people person, not a statistician. Employees liked him; he was funny, friendly, and did what he promised. But he had a weakness he kept to himself. He didn’t understand basic statistics, probability, or more advanced methods like regression analysis. Jake went back to his office and looked through his office window at the one-hundred plus cubicles in the call center. His employees were constantly trained in the latest Haven malware revisions and issues. Once he arrived home that evening, he hugged his spouse and two young kids. He was a family man, but tonight after dinner, he went to his basement office, closed the door, and pulled out two old college textbooks—one on statistics and a second book on Excel modeling. Jake liked one of the quotes in his statistics book. It said, “In God we trust; all others bring data analysis!” Jake wanted to answer his boss’s query, “Do our customers wait too long?” For the next five days, averaging five hours of sleep a day, he studied these books night and day. He collected a sample of data on customer wait times in seconds for the call center operation. The data set Haven Malware Case Data shows these data for 500 customer calls. He also planned to examine more detailed data on call volumes and patterns by hour of the day and service processing times at a later time. Jake planned to show up for the meeting tomorrow with basic statistics and histograms, insights into customer waiting times, and options to help solve the problems of long waiting times and lost sales.

Case Questions for Discussion:

1. Using the data for customer wait times and the Statistical Analysis Excel template, what can Jake tell his boss, Beth Weltman, about customer wait times?

2. What options should Jake investigate and propose to Beth?