Singapore University of Social Sciences

Business Analytics Applications

Business Analytics Applications (ANL309)

Applications Open: 01 October 2020

Applications Close: 15 December 2020

Next Available Intake: January 2021

Course Types: Modular Undergraduate Course, SkillsFuture Series

Language: English

Duration: 6 months

Fees: To be confirmed

Area of Interest: Business Administration

Schemes: Alumni Continuing Education + (ACE+), Lifelong Learning Credit (L2C), Resilience 2020

Funding: SkillsFuture, Union Training Assistance Programme (UTAP)

School/Department: School of Business


This course introduces various applications of business analytics in customer relationship management (CRM). The course covers a wide range of CRM applications, including customer acquisition and segmentation, cross-selling and up-selling, customer loyalty, retention and churn, credit scoring and fraud detection. To be effective, the results of a business analytics project must be actionable. To this end, the course emphasises the cross-industry standard process for data mining (CRISP-DM) in the applications of business analytics to CRM.

Level: 3
Credit Units: 5
Presentation Pattern: Every January


  • Customer Relationship Management (CRM) Concepts
  • Cross-Industry Standard Process for Data Mining (CRISP-DM) Framework
  • Customer Acquisition and Segmentation Concepts
  • Data Mining Models for Customer Acquisition and Segmentation
  • Cross-Selling and Up-selling Concepts
  • Data Mining Models for Cross-Selling and Up-Selling
  • Customer Loyalty, Retention and Churn Concepts
  • Data Mining Models for Customer Loyalty, Retention and Churn Analysis
  • Credit Scoring Conceps
  • Data Mining Models for Credit Scoring
  • Fraud Detection Conepts
  • Data Mining Models for Fraud Detection

Learning Outcome

  • Organise the key concepts relevant to Customer Relationship Management (CRM) analytics.
  • Distinguish the differences between cross-selling and up-selling.
  • Describe customer loyalty, retention and churn.
  • Discuss credit scoring concepts.
  • Examine the different predictive modelling techniques used in fraud detection.
  • Set up the CRISP-DM framework for a data mining problem.
  • Evaluate the application of CRM business analytics.
  • Interpret the credit scores.
  • Implement business analytics techniques with the use of appropriate software.
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