Download Business Intelligence and Marketing

Business Intelligence and Marketing Analytics (Version #1)
ระบบธุรกิจอัจฉริยะและการวิเคราะห์ ทางการตลาด
Instructor
Dr. Jongsawas Chongwatpol (ดร.จงสวัสดิ จงวัฒน์ผล)
NIDA Business School, National Institute of Development Administration
118 Seri Thai Road, Bangkapi, Bangkok, 10240, Thailand
Email: [email protected]
Office Phone: 02-727-3843
Course Description
This course will cover a comprehensive analysis of contemporary Business
Intelligence (BI) tools and techniques used in managerial decision-making. This
course will also emphasize on Marketing Intelligence, which is defined as the BI
practice of extracting and analyzing business data to accurate decision making in
determining market opportunities relevant to the enterprise. Many BI
techniques covered in this course include Decision Support Systems (DSS), data
and text mining, knowledge management, business performance management,
expert systems, neural networks, and how these tools and techniques are
related to other types of information systems. Students will develop knowledge
various BI enabling software packages (a general understanding and some
hands-on capabilities).
วิชานีศึกษาเทคนิคต่าง ๆ ทีใช้ ในระบบธุรกิจอัจฉริยะเพือนํามาประกอบการตัดสินใจในการ
บริหาร โดยมุง่ เน้ นการตลาดอัจฉริยะ ซึงหมายถึงการวิเคราะห์ข้อมูลทางธุรกิจเพือการตัดสินใจที
ถูกต้ องในการสร้ างโอกาสทางการตลาดทีสัมพันธ์กบั องค์กร วิชานียังครอบคลุมเทคนิคต่าง ๆ ที
เกียวข้ อง เช่น ระบบสารสนเทศทีช่วยในการตัดสินใจ (Decision Support Systems) การทํา
เหมืองข้ อมูลและเหมืองข้ อความ (Data and Text Mining) การจัดการความรู้ (Knowledge
Management) การวัดประสิทธิภาพทางธุรกิจ (Business Performance Management)
ระบบผู้เชียวชาญ (Expert System) ระบบโครงข่ายประสาทเทียม (Neural Networks) และ
การประยุกต์ใช้ ระบบธุรกิจอัจฉริยะในระบบสารสนเทศประเภทอืน ๆ
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Course Objectives
The main objective of this course is for the student to develop an understanding
of the role of computer based information systems in direct support of
managerial decision making (nowadays commonly referred to as business
intelligence). Specifically, at the end of this course each student should develop:
-
-
-
Knowledge about managerial decision making, business intelligence,
decision support systems and how they relate to other types of information
systems
Knowledge about DSS development methodologies and enabling
technologies (such as Analytical Hierarchy Process, Group Support Systems,
Expert Systems, Neural Networks, Knowledge Management, Data
Warehousing and Data Mining)
Knowledge about DSS enabling software packages − a general
understanding and some hands-on capabilities.
Pre-requisites
Even though there are no pre-requisites for this course, having the basic
understanding of the following concepts would improve your experiences:
- One or more programming languages (such as Visual Basic, Java, C etc.) for
analytical and structural thinking and reasoning purposes only.
Note that there will not be any programming in this class.
- Artificial intelligence, relational databases, web-based Information systems
and general business functions.
Required Text
Decision Support and Business Intelligence Systems, 9th Edition, ©2011, by
Efraim Turban, Dursun Delen, and Ramesh Sharda
Prentice Hall: Upper Saddle River, NJ.
ISBN: 0-13-245323-1 or 978-0-13-245323-3
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Other Materials
Because the field of Decision Support Systems is changing so fast, there will be
additional handouts (technical journal papers and other written materials) for
you to read throughout the semester. This material will be posted on the class
web site or will be distributed in class.
References
Decision Support Systems: Frequently Asked Questions
(by Daniel J. Power, iUniverse, Inc. 2005)
Data Mining: Concepts and Techniques, 2nd Edition
(by Jiawei Han and Micheline Kamber, Morgan Kaufmann, 2006)
Data Mining Techniques (for Marketing, Sales and CRM),
(by Michael J.A. Berry and Gordon S. Linhoff, Wiley 2004)
Business Intelligence: Practices, Technologies, and Management
(by Rajiv Sabherwal and Irma Becerra-Fernandez, Wiley 2011)
Making Better Decisions: Decision Theory in Practice
(by Itzhak Gilboa, Wiley-Blackwell 2011)
Advanced Data Mining Techniques
(by David L. Olson and Dursun Delen, Springer 2008)
The New Know: Innovation Powered by Analytics
(by Thornton May, Wiley 2009)
Software
1. Microstrategy
2. Tableau
3. SIMIO Simulation Software
4. Microsoft Excel (w/ solver add-in)
5. Planner Labs
6. RapidMiner by Rapid-I Consulting.
7. WEKA Data Mining Toolkit by University of Waikato.
8. Expert Choice (demo version) by Expert Choice, Inc.
9. ExSys Expert System Software (demo version) by ExSys Inc.
Participation and Professionalism
10% of your final grade will be coming from your participation in class as well as the way in which you
conduct yourself in class. The success of this course depends on each participant (instructor and
students) to actively contribute to the community of learners. For this reason, all participants are
expected to facilitate the development of this learning community. Attendance in this course is not
based on “seat time,” but instead is based on course activity. Activities that represent attendance
include constructive contributions to discussions and openly reflecting on the learning process.
Attendance
Attendance to the class is not required but highly recommended (cannot participate without
attendance). In any case, you will be held accountable for all that is covered in the class despite valid
reasons for your absence.
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Term Project (Team-based)
There will be a team project involving development of a complete business intelligence solution using
one or more of the DSS tools and techniques. Specifics about the acceptable business problems, which
you will be identifying, analyzing and solving, will be given later in the semester. Depending on the class
size, each team will be made up of three or four people. You will be responsible for identifying,
conceptualizing, designing and developing a valuable solution to a real-world business problem. Each
team will submit a proposal, a progress report and a final report (documenting each and every step of
their development effort), and, if time and venue permits, will present their project (as a team) in class
at the end of the semester.
Term Paper (Team-based)
You will be writing a term paper about the business domain you are planning to address for your term
project. You are expected to utilize technical journals, books, magazine articles and whatever is available
to you on the Internet. In essence, you will get credit for doing something that you should do in any case
for your term project. This study will give you a good understanding about the nature of the problem
you will be analyzing for your term project. It will also give you an understanding of what others have
done to solve this (or similar) kind of business problems.
Guidelines for the course
1. Arrive on time
2. Attend every class, and let the professor know when you will not be attending
3. Prepare ahead of time for class
4. Follow appropriate business etiquette regarding cell phones and class manner
5. Participate! Ask whenever feeling not sure or loss track
Grading Policy
Mid-Term Exam:
Final Exam:
Homework Assignments and Quizzes:
Term Project and term Paper:
Participation & Professionalism:
Total Points:
20%
20%
30%
25%
5%
100%
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Tentative Course Outline
(Some alterations are likely)
Date
Section #1
Section #2
Section #3
Section #4
Section #5
Section #6
Section #7
Section #8
Section #9
Section #10
Section #11
Section #12
Section #13
Section #14
Section #15
Section #16
*Handout
Topic
Introduction to each other and to the course
Decision Making Systems, Modeling, and Support
Decision Support Systems Overview
Modeling and Analysis
Data Warehousing & Business Analytics
Data Mining for Business Intelligence
Data Mining – Algorithms
Mid-term Exam
Business Performance Management (BPM)
Collaborative Computing/GSS
Knowledge Management
Artificial Intelligence and Expert Systems
Advanced Artificial Intelligence Techniques
Management Support Systems: Emerging Trends and Impacts
Project Presentations and Closing Comments
Final Exam
Reading Assignment
Ch 1
Ch 2
Ch 3 and HO*
Ch 4 and HO*
Ch 8 and HO*
Ch 5 and HO*
Ch 6
Ch 9
Ch 10 and HO*
Ch 11 and HO*
Ch 12
Ch 13
Ch 14
Note: The instructor reserves the right, when necessary, to modify the syllabus: alter the grading policy,
change examination dates, and modify the course content. Modifications will be announced and
discussed in class and will be posted on the class website. Students are responsible for those changes.
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Case Studies:
1. Case Study # Chongwatpol, J and Sharda, R (December 2010) SNAP: A DSS to analyze network
service pricing for state networks, Decision Support Systems, 50, 1, 347-359
2. Application of data mining techniques in customer relationship management: A literature review
and classification, Ngai et al., 2009, Expert Systems with Applications, volume 32, issue 2.
3. “How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did” by Kashmir Hill,
www.forbes.com, Feb 16, 2012
4. “Sam’s Club Personalizes Discounts for Buyers” by Andrew Martin, www.nytimes.com, May 30, 2010
5. Data mining to improve industrial standards and enhance production and marketing: An empirical
study in apparel industry, Chih-Hung Hsu, 2009, Expert Systems with Applications, volume 36 (3)
6. The application of data mining techniques in financial fraud detection: A classification framework
and an academic review of literature, Ngai et al., 2011, Decision Support Systems, volume 50, issue 3.
7. Modeling wine preferences by data mining from physicochemical properties, Cortea et al., 2009,
Decision Support Systems, volume 47, issue 4.
8. Data Mining Applications in Healthcare, 2011, Kob and Tan, Journal of Healthcare Information
Management, volume 19, issue 2
9. Cluster analysis using data mining approach to develop CRM methodology to assess the customer
loyalty, Hosseini et al., 2010, Expert Systems with Applications, volume 37, issue 7
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