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Evaluating Customer Service Representative Staff Allocation and Meeting Customer Satisfaction Benchmarks: DEA Bank Branch AnalysisMin, Elizabeth Jeeyoung 14 December 2011 (has links)
This research employs a non-parametric, fractional, linear programming method, Data Envelopment Analysis to examine the Customer Service Representative resource allocation efficiency of a major Canadian bank’s model. Two DEA models are proposed, (1) to evaluate the Bank’s national branch network in the context of employment only, by minimizing Full Time Equivalent (FTE) while maximizing over-the-counter (OTC) transaction volume; and (2) to evaluate the efficacy of the Bank’s own model in meeting the desired customer satisfaction benchmarks by maximizing fraction of transactions completed under management’s target time. Non-controllable constant-returns-to-scale and variable-returns to-scale model results are presented and further broken down into branch size segments and geographical regions for analysis. A comparison is conducted between the DEA model results and the Bank’s performance ratios and benchmarks, validating the use of the proposed DEA models for resource allocation efficiency analysis in the banking industry.
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Evaluating Customer Service Representative Staff Allocation and Meeting Customer Satisfaction Benchmarks: DEA Bank Branch AnalysisMin, Elizabeth Jeeyoung 14 December 2011 (has links)
This research employs a non-parametric, fractional, linear programming method, Data Envelopment Analysis to examine the Customer Service Representative resource allocation efficiency of a major Canadian bank’s model. Two DEA models are proposed, (1) to evaluate the Bank’s national branch network in the context of employment only, by minimizing Full Time Equivalent (FTE) while maximizing over-the-counter (OTC) transaction volume; and (2) to evaluate the efficacy of the Bank’s own model in meeting the desired customer satisfaction benchmarks by maximizing fraction of transactions completed under management’s target time. Non-controllable constant-returns-to-scale and variable-returns to-scale model results are presented and further broken down into branch size segments and geographical regions for analysis. A comparison is conducted between the DEA model results and the Bank’s performance ratios and benchmarks, validating the use of the proposed DEA models for resource allocation efficiency analysis in the banking industry.
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