Artificial Intelligence & Marketing Decision Support Systems

University of Washington

Summer 1997

BBUS 429

 

 

Professor P.V. (Sundar) Balakrishnan

Office: Room 210

 

Tel. #: 685-5384

Email: sundar@u.washington.edu

Course Objectives:

Business is in the midst of a technological revolution. To be successful, students have to be at the forefront of this new technology. This means taking a "hands-on" approach to developing the requisite skills necessary to become "marketing engineers".

This course deals with concepts, methods, applications of decision modeling to address various marketing issues. Unlike conventional capstone business courses that focus on conceptual material this course will attempt to provide skills to translate conceptual understanding into developing specific operational models for improved decision-making - a skill in increasing demand in corporations today.

Using PC and UNIX-based computer software, students will develop Spreadsheet Models and Artificial Intelligence based Decision Support Systems for varying managerial decision contexts.

 

Specifically, course objectives are to:

The course will be of particular relevance to students planning careers in marketing and management consulting. The course is designed for students with some quantitative background as well as some exposure to marketing concepts.

 

 

REQUIRED COURSE MATERIAL

1) Gary L. Lilien, Marketing Management: Analytic Exercises for Spreadsheets, Second edition, 1993. (GL).

2) Various Readings placed on reserve in the library (R);

3) EXSYS Manual and Videotape placed on reserve in the library. .

RECOMMENDED REFERENCE

1) Gary L. Lilien, Philip Kotler, and K. Sridhar Moorthy, Marketing Models, Prentice Hall, 1992. (LKS).

This is a reference book that supplements course materials and class discussions. This text will be useful for answering the more advanced questions.

 

SAMPLE BOOKS FOR REPORT

Each individual is expected to read and report on a book of their choice relating to this course. A sample of books is listed below and some of which are available from the Library (reserve section).

  1. The Selfish Gene, by Richard Dawkins, Oxford University Press.
  2. Who Got Einstein’s Office? By Ed Regis, Addison-Wesley.
  3. Chaos: Making a New Science, James Gleick, Penguin Books.
  4. The Quark and the Jaguar, by Murray Gell-Mann, W.H. Freeman & Co.
  5. Artificial Life, Steven Levy, Vintage Books.

Check with me before you select a book from either this list or find your own.

 

EVALUATION

Genetic Algorithms Term Projects 35%

Assignments & Exams 35%

Class Participation & Presentations 20%

INDIVIDUAL Book Report 5%

 

WORK LOAD

Class sessions will be devoted to probing, extending and applying the material in the readings and the cases. It is your responsibility to be prepared for each session as detailed in the course outline. Each one of you will benefit from belonging to a "study group" that meets and prepares for each session before coming to class.

The course will involve extensive computer-based work in addition to the readings and library research. My expectation is that the time required for out-of-class work will be 3 times the duration of class meeting.

 

Genetic Algorithms Term Projects Guidelines (35%)

  1. Download, and Evaluate a GA freeware
    1. Each group is expected to learn how to effectively search the Internet for information related to GA.
    2. In particular, each group will be expected to download one of the "freeware" GA programs from various "sites" around the world and get it up and running on our computer systems (mainframes or PC).
    3. You will then provide a brief report (2 pages) evaluating this software.

     

  2. Each Group is expected to develop a prototype Genetic Algorithms based Decision Support System (GADSS). The system must attempt to address a specific Marketing Problem.
    1. You have a choice of working on your own problem and data set or working on developing a GADSS to address the problem and data set provided by the Instructor.
    2. The GADSS can developed using the software provided (GENERATOR) or with the program that you have downloaded from another site.
    3. A paper describing the developed systems as well as the diskette containing the developed system should be submitted.
    4. Paper Requirements. The paper should at a minimum discuss the following :
    1. The following factors will be taken into consideration when grading the system:

 

Assignments & Exams:

There are various spreadsheet assignments (mini-cases) to be completed as part of the course. The dates on which the assignments have to be handed in are indicated in the course schedule. The exact nature of the assignments will be announced in class, sufficiently ahead of time. These are group assignments: Please form groups to work on these assignments, and to prepare for class discussions.

  1. All Software assignments that are to be turned in, must be wordprocessed and professional looking. NO handwritten work is permitted on the EXCEL spreadsheet assignments.
  2. These Spreadsheet assignments are from your Text Book and can be done ahead of time.
  3. You are expected to turn them in by end of Class-Period.
  4. One or more of these assignments will be earmarked as Exams and must be completed individually.
  5. Groups will be expected to have worked on these AHEAD of time. You will be given some class time to complete your work. At this point, we will discuss these spreadsheet cases.
  6. A Group will be asked to come to the head of the class to demonstrate their proficiency of the underlying model. They will then moderate a discussion of that assignment. Every Group will get an opportunity to lead the discussion at least once.

 

Class Presentations:

  1. This is one of the Key’s to doing well in this course. All readings as indicated in the Schedule must be done ahead of time. Each group (or sometimes individual) will be asked to present one of the readings each day. The presentations must be thorough and provoke discussions.
  2. In addition to the assigned readings, each group, is responsible for researching and presenting an interesting article describing an application of genetic algorithms. The material presented should preferably have a business oriented or with the potential for business application. The groups will submit one copy of the article and their presentation material.
  3. The quality of the discussions from the floor will help determine your grade for that day.

Class Participation:

Each of you is expected to contribute to class discussions. Do not expect to do well in this course by simply coming to class, taking notes, and synthesizing, recalling, or reproducing these notes for our evaluation. To do well, you must learn from active participation in class discussions.

In evaluating class participation, I will try to assess how your individual contribution enhance both the content and process of a discussion:

If you are unprepared to participate in the day’s discussions, notify me prior to the beginning of the class to avoid any embarrassment.

 

 

 

Software

We will play with a number of different software packages.

  1. We will spend quite a bit of our time working with packages that deal with GENETIC ALGORITHMS. In your Computer Lab I have purchased and had installed a package called GENERATOR, which is an EXCEL Spreadsheet add-on.
  2. In addition, I will demonstrate GENESYS/GENELIN, a package that I have developed for Product Design.
  3. Play with an Expert Systems Shell called EXSYS Pro (It is also limited to 50 rules. This is available in the machines in the Computer Lab) and a couple of Expert Systems for Marketing Decisions.

    4. (You can also Purchase EXSYS Professional student version for $70. The Purchase can be done directly from the company EXSYS Inc. The phone number is 1-800-676-8356 or 505-256-8356. This is an expert system development package which is limited to 50 rules but has most of the functionality of the substantially more expensive EXSYS Professional Complete package. Additionally you will get the manuals, tutorials etc.)

  4. In addition, the Text comes shrink-wrapped with a disk containing numerous cases requiring the use of EXCEL spreadsheet.

 

Please Note: All Reports and Papers submitted as part of this course, whether short or long, will also be graded for

 

 

 

Tentative Class Schedule

 

Topic: Introduction

6/23 Search the Internet for GA Software

 

Topic: Basics of Spreadsheet Models

6/25 Read: Chapters 1, 9 {2 if you use LOTUS else skim} (GL)

Software: EXAMPLE (discuss), HW Problem

 

Topic: Optimization Tools of Spreadsheet Models

6/30 Read: Chapters 1, 9 {2 if you use LOTUS else skim} (GL)

Software: EXAMPLE* (Due§ ), MAP (discuss)

 

Topic: Artificial Intelligence Techniques: Overview of Genetic Algorithms

7/2 Read: "Moody’s Evolving Desk" (R)

Skim: "A Gentle Introduction to Genetic Algorithms" (R)

Software: MAP* (Due), GENERATOR

 

Topics: Decision Calculus Models: Fundamentals

7/7 Read: "Models & Manager: The Concept of Decision Calculus (R)

Read: "Commentary on Judgment based Marketing Decision Models", (R)

Read: "Decision Support Systems for Marketing Managers"(R)

Read: "Shake, Rattle, & Roll" (R)

Software: ADBUDG (play); Estimating Parameters

 

Topic: Expert Systems Fundamentals

7/9 Read: Chapter 14 "AI, Expert Systems, and DSS" (R);

Read: Chapter B of EXSYS Manual (R)

Video: EXSYS Watch before class (Library Reference Desk)

Software: EXSYS DEMO (Lab)

 

Topic: Expert Systems Exercise

7/14 Review: EXSYS Manual (R)

Video: EXSYS Watch before class (Library Reference Desk)

Software: EXSYS: Incorporate 3 new Rules (Due)

 

Topic: Demand Assessment & Forecasting Models

7/16 Read: pg. 43-50 (GL)

Software: EXPER*, REGRESS*

 

Topic: Expert Systems Applications

7/21 Read: Chapter 15 "Expert Systems from the Outside" (R); &

Read: "Developing Marketing Expert Systems: An Application to International Negotiations" by Rangaswamy, Burke, Eliashberg & Wind (R)

Read: "A Knowledge-Based System for Advertising Design" by Rangaswamy, Burke, Wind & Eliashberg (R)

Software: Negotex, Adcad

 

Genetic Algorithms for Product Design

7/23 Read: "Triangulation in Decision Support Systems: Algorithms for Product Design" Balakrishnan and Jacob (R)

Read: "Genetic Algorithms for Product Design" Balakrishnan and Jacob (R)

Software: GENELIN; Cruise the Internet for GA Freeware;

 

Artificial Intelligence for Market Segmentation

7/28 Read: "A Gentle Introduction to Genetic Algorithms" (R)

Read: "Comparative Performance of the FSCL Neural Network and K-means algorithm for market segmentation" Balakrishnan, et al. (R)

Software: Modify GA Freeware; copy project data

 

Topic: Genetic Algorithms for Market Segmentation

7/30 Read: "Optimization of Control Parameters for Genetic Algorithms"

Read: GENERATOR Manual (R)

DUE: Book Report

Software: GENERATOR

 

Topic: Genetic Algorithms Applications

8/4 Cruise the Internet and Library

Presentations of research articles by Students

 

Topic: Product & Price Models

8/6 Read: pg. 107-118

Read: Chapter 4, pg. 56-67 (GL)

Software: BASS*; PRICE*

 

Topic: Advertising, Sales Promotion Models:

8/11 Read: pg. 75-81 (GL), Chapter 4, pg. 81-83 (GL):

Software: VIDALE*, PROMO*

 

Topic: Marketing Strategy Models

8/13 Read: Chapter 5, pg. 91-107

Software: GE*, COMPAD*§

 

Genetic Algorithms

8/18 Project Presentations

GA Final System/Reports Due

 

* = analysis/report is due

§ = Individual Write-up.

 

 

 

GRADING OF SPREADSHEET ASSIGNMENTS

 

Criteria

 

    1. Answers the Questions at the end of the Assigned Exercise in a professional looking Report.
      No handwritten marks of anykind will be accepted.

      2. Reports will be graded for organization and writing.

      Do NOT submit pages of appendices and charts that are not directly relevant and not referenced in the text.

       

    2. Show through your discussion that you understand the structure of the embedded model. E.g.,
      What are the assumptions of this model?
      Write-out and describe the equations employed.

      3.  

    3. Indicate in your Report as to how you think the Model Should/Could be improved.
      These should go well beyond the comments relating to the cosmetic aspects.
      Ie., What are the critical assumptions?
      Which should be relaxed first?
      and, how might we conceptually go about doing that?.

      4.  

    4. Provide atleast one actual implementation of the suggested improvements.
      Need to Demonstrate this to the Instructor
      Document this in the Report.