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FIT5158 Customer relationship management and data mining - Semester 1, 2012

This unit provides an understanding of the business value of customer relationship management and how data mining technology can be used to improve organizational interaction with customers. Building a business around the customer relationship is the aspiration of many modern organizations. Customer relationship management and data mining has been combined together to provide the required concepts, techniques, technology and tools to achieve this goal. The unit discuss how IT and IT based techniques can be used for customer segmentation, clustering and classification, market basket analysis and association rule mining in addition to traditional CRM.

Mode of Delivery

  • Caulfield (Day)
  • Sunway (Day)

Contact Hours

2 hrs lectures/wk, 1 hr laboratory/wk


Students will be expected to spend a total of 12 hours per week during semester on this unit.
This will include:

  • two-hour lecture and
  • one-hour tutorial (or laboratory) (typically requiring advance preparation)
  • a minimum of 3 hours of personal study per one hour of contact time in order to satisfy the reading and assignment expectations.

  • You will need to allocate up to 5 hours per week in some weeks for use of a computer, including time for newsgroups/discussion groups.

Unit Relationships


FIT9004 or FIT9017

Chief Examiner

Campus Lecturer


David Dowe

Consultation hours: To Be Discussed in Lectures and Confirmed


Elsa Phung



Minh Le Viet

Sumith Matharage

Academic Overview


At the completion of this unit students will be able to:
  • use software tools and techniques for identifying business opportunities;
  • plan direct marketing campaigns and product introductions;
  • analyse and understand customer churn with data mining tools;
  • create stable and accurate predictive models and interpret results;
  • provide advise to management on CRM;
  • advise management on data mining techniques and tools.

Graduate Attributes

Monash prepares its graduates to be:
  1. responsible and effective global citizens who:
    1. engage in an internationalised world
    2. exhibit cross-cultural competence
    3. demonstrate ethical values
  2. critical and creative scholars who:
    1. produce innovative solutions to problems
    2. apply research skills to a range of challenges
    3. communicate perceptively and effectively

Assessment Summary

Examination (3 hours): 60%; In-semester assessment: 40%

Assessment Task Value Due Date
Assignment 1 - SQL Server and Data Warehousing 20% Week 6, Thursday 5 April 2012
Assignment 2 - ``Data Mining'' 20% Week 11, Friday 18 May 2012
Examination 1 60% To be advised

Teaching Approach

Lecture and tutorials or problem classes
This teaching and learning approach provides facilitated learning, practical exploration and peer learning.


Our feedback to You

Types of feedback you can expect to receive in this unit are:
  • Informal feedback on progress in labs/tutes
  • Graded assignments without comments

Your feedback to Us

Monash is committed to excellence in education and regularly seeks feedback from students, employers and staff. One of the key formal ways students have to provide feedback is through SETU, Student Evaluation of Teacher and Unit. The University's student evaluation policy requires that every unit is evaluated each year. Students are strongly encouraged to complete the surveys. The feedback is anonymous and provides the Faculty with evidence of aspects that students are satisfied and areas for improvement.

For more information on Monash's educational strategy, and on student evaluations, see:

Previous Student Evaluations of this unit

More mathematical material - such as probability and even logarithms - will be introduced.

The might be more examples introduced in lectures, and the tutorials might be more practical.

If you wish to view how previous students rated this unit, please go to

Required Resources

Please check with your lecturer before purchasing any Required Resources. Prescribed texts are available for you to borrow in the library, and prescribed software is available in student labs.

WEKA Data Mining software
SQL Server (2008)
This and other any software needed - such as Minimum Message Length (MML) software for clustering and/or decision trees/graphs - will be made available or provided.

Students should also have at least some degree of mathematical literacy - including, e.g., notions of probability and logarithm.

Unit Schedule

Week Activities Assessment
0   No formal assessment or activities are undertaken in week 0
1 CRM and Customer Intelligence No assessment, no tute/lab
2 Storing Data for Customer Intelligence First tute/lab, no assessment
3 Data Warehousing with SQL Server 2005  
4 Dimensional Modelling  
5 Data Warehouse and Analytical CRM  
6 Online Analytical Processing Assignment 1 due Week 6, Thursday 5 April 2012
7 Introduction to Business ``Data Mining''  
8 Customer Relationship Management (CRM)  
9 Decision Trees  
10 Neural Networks  
11 Collaborative Filtering and User Profiling Assignment 2 due Week 11, Friday 18 May 2012
12 Customer Life Cycle and Data Mining  
  SWOT VAC No formal assessment is undertaken SWOT VAC
  Examination period LINK to Assessment Policy:

*Unit Schedule details will be maintained and communicated to you via your MUSO (Blackboard or Moodle) learning system.

Assessment Requirements

Assessment Policy

Faculty Policy - Unit Assessment Hurdles (

Assessment Tasks


  • Assessment task 1
    Assignment 1 - SQL Server and Data Warehousing
    As per the title, this might involve some OnLine Analytical Processing (OLAP) and a written report.  The mathematics should be quite friendly.

    Further details will be provided.
    Criteria for assessment:
    Perform some individual practical task based on the content covered in classes. Write some sort of report analyzing the given task based on the obtained results.

    Further details will be provided.

    Due date:
    Week 6, Thursday 5 April 2012
    You will be expected to submit your assignment in both printed hard copy and soft electronic copy - unless explicitly told otherwise by both your lecturer and your tutor.

    Your work is expected and required to be your own work. See "Other Information" for the consequences of plagiarism, etc.
  • Assessment task 2
    Assignment 2 - ``Data Mining''
    As per the title, there will be some analysis of some data, some of which might entail varying degrees of mathematics.

    Further details will be provided.
    Criteria for assessment:

    Perform some practical task based on the content covered in classes. Write a business report analyzing the given task based on the obtained results - possibly with some sort of cost-benefit analysis involving some probabilities and other mathematics.

    Further details will be provided.

    Due date:
    Week 11, Friday 18 May 2012
    You will be expected to submit your assignment in both printed hard copy and soft electronic copy - unless explicitly told otherwise by both your lecturer and your tutor.

    If there is hypothetically any group work, then students would form their groups in class and then - at the end of the assignment - comment on the degree of contribution of the various group members.

    Your work is expected and required to be your own work. See "Other Information" for the consequences of plagiarism, etc.


  • Examination 1
    3 hours
    Type (open/closed book):
    Closed book
    Electronic devices allowed in the exam:

Assignment submission

It is a University requirement ( for students to submit an assignment coversheet for each assessment item. Faculty Assignment coversheets can be found at Please check with your Lecturer on the submission method for your assignment coversheet (e.g. attach a file to the online assignment submission, hand-in a hard copy, or use an online quiz).

Online submission

If Electronic Submission has been approved for your unit, please submit your work via the VLE site for this unit, which you can access via links in the portal.

Extensions and penalties

Returning assignments

Referencing requirements

Students are encouraged but not required to write in LaTeX.

In any event, referencing styles such as any of those used by the Computer Journal, the Artificial Intelligence Journal, the Intelligence Journal, Springer LNAI/LNCS (Lecture Notes in Artificial Intelligence / Lecture Notes in Computer Science), IEEE, Journal of the ACM should be fine.

Make your work readable, intelligible and coherent.

Other Information


Student services

The University provides many different kinds of support services for you. Contact your tutor if you need advice and see the range of services available at For Sunway see, and for South Africa see

The Monash University Library provides a range of services and resources that enable you to save time and be more effective in your learning and research. Go to or the library tab in portal for more information. At Sunway, visit the Library and Learning Commons at At South Africa visit

Academic support services may be available for students who have a disability or medical condition. Registration with the Disability Liaison Unit is required. Further information is available as follows:

  • Website:;
  • Email:
  • Drop In: Equity and Diversity Centre, Level 1 Gallery Building (Building 55), Monash University, Clayton Campus, or Student Community Services Department, Level 2, Building 2, Monash University, Sunway Campus
  • Telephone: 03 9905 5704, or contact the Student Advisor, Student Commuity Services at 03 55146018 at Sunway


Reading List

"Managing Customer Relationships: A Strategic Framework", by D. Peppers and M. Rogers [ISBN-10: 0470423471 | ISBN-13: 978-0470423479], 2nd edition, 2011.

Practical Business Intelligence with SQL Server 2005, by John C. Hancock and Roger Toren, Addison Wesley, 2006

The Microsoft Data Warehouse Toolkit, by Joy Mundy and Warren Thornthwaite, John Wiley & Sons, 2006

G K Gupta, Introduction to Data Mining with Case Studies, Prentice-Hall of India Private Limited, New Delhi India, 457pp.

This next reference is heavy mathematically, but it is most probably the future of ``data mining'': C S Wallace (2005), Statistical and Inductive Inference by Minimum Message Length, Springer, 432pp.

Here are some further references of possible interest:

William H. Inmon, "Building the data warehouse", 4th edn, chaps 1 and 2.

Efrain Turban et al., "Business intelligence: a managerial approach", chap. 1

Michael Berry and Gordon Linoff, "Data Mining Techniques", 2nd edn, chap. 1

Chris Todman, "Designing a data warehouse supporting CRM", chaps 1 and 2