Case Article—Analysis of Call Center Data at Patelco Credit Union

Narendra Agrawal ()
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Narendra Agrawal: Department of Information Systems and Analytics, Leavey School of Business, Santa Clara University, Santa Clara, California 95053

INFORMS Transactions on Education, 2023, vol. 24, issue 1, 43-46

Abstract: This case focuses on operational challenges faced by customer service centers, or call centers. The specific context for these cases is Patelco, which is a California based credit union. Patelco is facing a rising number of complaints about customer service, specifically, about the long delay customers had to face when they called one of Patelco‘s four call centers. The purpose of this case is to expose students to the statistical analysis of some of the raw data, obtained from such an environment, to support the investigation of call center performance. Important managerial insights can be drawn by summarizing the data graphically as well as quantitatively. In particular, the data can be used to show students how to perform a number of useful hypothesis tests, which are often needed to answer important questions that arise when assessing the performance of any system. The outcomes from these analyses can provide important managerial insights. In the operations management literature, and in practice, a number of modeling assumptions are made about the distribution of data to facilitate quantitative analysis. However, these assumptions are often not statistically validated. This case study gives students an opportunity to test such assumptions. This case can be used in graduate and undergraduate classes in Operations Management, Supply Chain Management and Service Operations to review statistical concepts related to the topic of queuing analysis, or in a Statistics class to illustrate data analysis and statistical tests in a real-world context.

Keywords: cases; teaching statistics; teaching service operations management; teaching production/operations management; teaching supply chain management; banking industry; call centers (search for similar items in EconPapers)
Date: 2023
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