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.
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:
David Dowe
Consultation hours: To Be Discussed in Lectures and Confirmed
Elsa Phung
Minh Le Viet
Sumith Matharage
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 |
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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
https://emuapps.monash.edu.au/unitevaluations/index.jsp
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.
| 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: http://policy.monash.edu.au/policy-bank/ academic/education/assessment/ assessment-in-coursework-policy.html |
*Unit Schedule details will be maintained and communicated to you via your MUSO (Blackboard or Moodle) learning system.
Faculty Policy - Unit Assessment Hurdles (http://www.infotech.monash.edu.au/resources/staff/edgov/policies/assessment-examinations/unit-assessment-hurdles.html)
Further details will be provided.
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.
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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.
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http://policy.monash.edu.au/policy-bank/academic/education/index.html
Key educational policies include:
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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