Getting the next swipe: Improving customer loyalty for OCBC bank credit cards

Getting the next swipe: Improving customer loyalty for OCBC bank credit cards

This case is set in 2020. In the latest credit cards sub-sector report from the Customer Satisfaction Index of Singapore (CSISG) survey by the Institute of Service Excellence (ISE) at the Singapore Management University, OCBC Bank’s credit card business was ranked last again in terms of customer sat...

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Bibliographic Details
Main Authors: ANG, Marcus, CHEN, Yongchang, DARENDELI, S. Pinar
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2022
Subjects:
Credit Cards
Customer Loyalty
Credit and Loans Industry
Competitor Analysis
Regression Analysis
Customer Acquisition
Business Administration, Management, and Operations
Online Access:https://ink.library.smu.edu.sg/cases_coll_all/402
https://smu.sharepoint.com/sites/admin/CMP/cases/SMU-21-BATCH%20%5BPDF-Pic%5D/SMU-21-0044%20%5BOCBC%20Bank%20Credit%20Cards%5D/SMU-21-0044%20%5BOCBC%20Bank%20Credit%20Cards%5D.pdf?CT=1644421590750&OR=ItemsView
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Institution: Singapore Management University
Language: English
Description
Summary:This case is set in 2020. In the latest credit cards sub-sector report from the Customer Satisfaction Index of Singapore (CSISG) survey by the Institute of Service Excellence (ISE) at the Singapore Management University, OCBC Bank’s credit card business was ranked last again in terms of customer satisfaction, while an analysis of two key customer loyalty metrics, namely ‘likelihood to use the credit card again’ and ‘likelihood to recommend their card’, saw the bank faring only slightly better for the former, but again ranked last for the latter. Given these findings, Tammy Ang, an analyst at ISE, had been tasked with preparing a business consulting pitch for OCBC Bank’s credit card business. Using her skills in business analytics, Ang hoped to use the data to provide useful insights for the bank to improve its customer loyalty performance. The case reinforces data processing, regression analysis, and numerical computation skills. Specifically, students will solve the problems using Excel, Tableau and R. After completing the assignment questions, the students will be able to: understand the impact of data analysis and visualisation in making decisions, propose a regression model to explain the dependent variable and evaluate its predictive performance, and evaluate the performance of a company based on the results of the regression analysis.

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