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Course:
Business Data Mining & Decision Models
Faculty:
Prof. Prof V. Nagadevara, QMIS Area, Rm. B-201, IIM-B Tel 3144
Text: Readings and handouts to be provided. Other references are listed below.
Term: Quarter 2 (2009-10); Pre-requisites: Core courses. Open to PGSEM, PGP & FPM
students.
Credits: Three.
Overview of Course
Data driven decision making in business has grown enormously in the last few years, and
today this sector is a multi-billion dollar industry. This course covers the principles of
business data processing for decision making in marketing functions.
The specific focus of the course shall be:
1. The course shall provide the students with a data based view of marketing
decisions.
2. The students shall be equipped with the basic business data processing tool
kit;
3. The course covers concepts and fundamentals of Data Mining for better
marketing campaigns and customer management.
Student assignments are oriented toward hands on training in
o Defining the business problem
o Gather right data
o Prepare and process data
o Discovery of trends and extraction of useful information from data.
o Reporting results
Course Outline
Objective:
The course aims primarily at developing the student's conceptual
understanding and technical data processing skills for marketing analysis and planning.
The structure of the course is linked to the various stages of data processing. The
conceptual aspects of this segment of the course involve the meaning and use of business
data and specifically, its use in marketing planning and decision making, and marketing
modeling approaches relevant to very large databases. Practical applications will
primarily be based on IBM's suite of BI s/w available in the IBM-IIMB BI Lab, and
Clementine from SPSS. These segments will involve specific exercises in data
warehousing, OLAP, query and mining, and interpretation for campaign design and
analysis. The perspective of CRM is integrated into these exercises.
Pedagogy:
The pedagogy of the course is a mix of lectures, readings, hands-on
training exercises for student teams, and a term paper.
Workload & Evaluation: The students are evaluated on individual and joint work
throughout the course. The workload and breakdown of grading are as follows:
1. Assignments: There will be an assignment each to be done by student teams of five
members. This will involve both secondary & primary data and Business Intelligence
lab work. The team will be responsible for a written report and will make a
presentation to the class for discussion. There will be peer evaluation of the content
and style of the team presentation effort. 40 percent for the assignments of which 10
percent is for presentation.
2. Term Paper: An individual term paper is due. The students will choose a specific
topic in consultation with the faculty from topics dealt with in the course; and. the
paper will be evaluated based on guidelines provided in advance. 40 percent.
3. Final Exam: At the end of the term there will be a final take-home comprehensive
individual exam on the topics and exercises. 20 percent.
Suggested References:
Books
1.
Marketing Engineering, Lilien and Rangaswamy, 2nd Ed., Pearson Education
2.
Data Warehousing, Data Mining and OLAP, Alex Berson and Stephen J. Smith,
3.
Customer Relationship Management; Seth, Parvathiyar & Shainesh (Eds.)
4.
The Balance Scorecard; Kaplan & Norton
5.
Business Intelligence: The IBM Solution; Mark Whitehorn and Mary Whitehorn,
Springer Publications
6.
Data Mining Techniques-for Marketing, Sales and CRM, 2nd Ed., Michael J. A.
Berry and Gordon S. Linoff, John Wiley & Sons
Periodicals
1.
Sloan Management Review
2.
Harvard Business Review
3.
Decision Sciences
4.
Decision Support Systems
Reference Materials
1.
2.
3.
IBM Documentation
SPSS White Papers on Data Mining
Project Reports.
The list of sessions is given in the table below. Each session will deal with the topic as
well as case on a real life application
Introduction to Data Mining
Cluster Analysis
Classification Trees
Data Warehousing & OLAP
Associations/Market Basket Analysis
Discriminant Analysis/Cluster Analysis
Neural Networks
Genetic Algorithms
Logistic Regression
Web Mining
Text Mining
Product Analysis and Promotion Models
Data Preparation and Data Cleaning for DM Models
Implementing BDM&D Models
Hands-on with IBM's Intelligent Miner and SPSS' Clementine
Project Presentations