Amazon: Facing low customer satisfaction in Singapore

• Main Authors: ANG, Marcus, CHEN, Yongchang
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Online retail; Customer satisfaction; Analytics; Computer-based modelling; Regression analysis Business performance management; E-Commerce; Operations and Supply Chain Management
• Online Access: https://ink.library.smu.edu.sg/cases_coll_all/287; https://smu.sharepoint.com/sites/admin/CMP/cases/SMU-19-BATCH%20%5BPDF-Pic%5D/SMU-19-0043%20%5BAmazon%20Customer%20Satisfaction%5D/SMU-19-0043%20%5BAmazon%20Customer%20Satisfaction%5D.pdf
• Institution: Singapore Management University

This case is set in 2018. The Institute of Service Excellence (ISE) at Singapore Management University conducted surveys in Singapore to measure customer satisfaction across 20 industries comprising more than 100 companies, and released the results as Customer Satisfaction Index of Singapore (CSISG) every quarter. In the latest CSISG report of the e-commerce sub-sector, Amazon was ranked last in terms of customer satisfaction in Singapore. The results were surprising as Amazon, the global leader in online retail, usually topped the customer satisfaction surveys conducted in the US. Puzzled by its lacklustre performance, James Mckally, senior partner at a Singapore-based marketing consultancy on e-retail, had requested ISE for a detailed analysis. John Lim, as the lead analyst at ISE, was in the process of reviewing the data collected and the analysis that had been generated, in order to derive some useful insights before the proposed meeting. This case aims to reinforce students’ skills in data processing, numerical computations, and use of Excel with the default Data Analysis Add-in tool. Students will learn to derive a suitable regression model, conduct regression analysis and use techniques such as spreadsheet modelling. They will be able to understand the impact of data analysis in making decisions; propose a regression model to explain the explanatory variables; and evaluate a company’s performance using data analysis.