Analytics And Intelligent Systems

NUS ISS AIS Practice Group

Managing Customer Loyalty through Acquisition, Retention and Experience Efforts: An Empirical Study on Service Consumers in India — June 5, 2017

Managing Customer Loyalty through Acquisition, Retention and Experience Efforts: An Empirical Study on Service Consumers in India

Recent developments in the marketing literature highlight the significance of consumer relationship management (CRM) in driving consumer loyalty (CL). In order to provide a clear understanding of the impact of CRM on CL, this study develops an integrated framework of CRM activities: Acquisition, retention, and experience to manage Customer Loyalty through direct and indirect approaches (with the mediation of satisfaction, trust, and commitment). The article utilizes a survey-based empirical study of 600 consumers from three service sectors (health, retail, and wellness). The findings of the study suggest that a firm that pays more attention to manage consumer experiences would be significantly benefited from the implementation of CRM programs. Consumer experience efforts have the positive impact on CL through commitment in all three sectors. Service manager should have clarity and consciousness that consumers are not looking for just traditional CRM benefits such as value proportion, reward points and so on but specifically seek for a pleasant experience of various touchpoints. Various frameworks of acquisition of CRM activities to manage Customer Loyalty has been analyzed.

Key Words:
Consumer loyalty, Consumer relationship management, Consumer experience management, Satisfaction, Trust, Commitment

Managing customer Loyalty

Source : Managing Consumer Loyalty through Acquisition, Retention and Experience Efforts: An Empirical Study on Service Consumers in India

Submitted by TEAM MARS

  1. Mutharasan Anbarasan(A0163257A)
  2. Raghavan Kalyanasundaram(A0163316L)
  3. Saravanan Kalastha Sekar(A0163309H)
  4. Seshan Sridharan(A0148476R)
  5. Sindhu Rumesh Kumar(A0163342M)
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Applying Multiple Linear Regression and Neural Network to Predict Bank Performance — April 29, 2017

Applying Multiple Linear Regression and Neural Network to Predict Bank Performance

Journal Summary:

This study uses multiple linear regression technique and feed forward artificial neural network in predicting bank performance. It aims to predict bank performance using multiple linear regression and artificial neural network. The study then evaluates the performance of the two techniques with a goal to find a powerful tool in predicting the bank performance.

Keywords: Bank performance, Multiple linear regression, Neural Network, Malaysia

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journal

Submitted By:

Adarsh Kumar
Aravind Somasundaram
Kalastha Sekar Saravanan
Kriti Srivastava