24750 Marketing Decision Analysis

Faculty of Business: Marketing
Credit points: 6 cp

Subject level: Postgraduate

Result Type: Grade and marks

Requisite(s): ((24734 Marketing Management OR 24746 Marketing: Concepts and Applications) AND (24720 Marketing Research OR 24737 e-Business Marketing) AND 24730 Advanced Marketing Management)
These requisites may not apply to students in certain courses.
There are also course requisites for this subject. See access conditions.

Handbook description

This subject studies marketing management decision processes and procedures from a product manager's viewpoint. An increasingly complex marketing environment offers product managers new challenges and opportunities. To take advantage of the opportunities and meet the challenges, computer-aided decision procedures provide additional conceptual and applied tools for decision making. This course builds and expands on the material covered in earlier postgraduate Marketing courses by way of learning about computer models which aid a product manager in the task of managing markets.

Subject objectives/outcomes

On successful completion of this subject, students should be able to:

Understand the role that modern analytical techniques and computer models can play in enhancing marketing decision making in an increasingly complex marketing environment.
Master the basis of measurement and identify appropriate modelling approaches for different marketing problems.
Demonstrate an appreciation of a range of multivariate statistical techniques in terms of their purpose, limitations, practical applications, and interpretation.
Use statistical software tools (SPSS and Excel) to apply the models and methods taught in the subject to real world marketing problems.

Contribution to graduate profile

This subject is designed to introduce students to a variety of computer-aided decision models used in marketing. It deals with concepts, methods, and applications of decision models used to address important marketing issues such as market segmentation, targeting and positioning, new product design and development, and sales forecasting. The subject will be particularly valuable to students planning careers in marketing research and product management.

Teaching and learning strategies

The subject will be taught using a combination of lectures and tutorials in a computer lab. Lectures will normally be of one-hour duration. The structure will vary slightly depending upon the material to be covered. However, they will usually comprise of presentation of theoretical underpinnings of various computer-aided decision models and interpretation of data analysis results. Tutorial sessions will normally consist of individual exercises designed to teach students how computer based statistical packages, such as SPSS for Windows, and spreadsheet software, such as Microsoft Excel, may be used to illustrate the lecture topics and allow students to develop practical data analysis skills to tackle various marketing problems. Relevant data files and other course material are posted on UTS Online website: http://online.uts.edu.au

Content

Introduction to the subject including its aims, structure, and rationale.
Benefits of using marketing decision models.
Overview of statistical theories, including univariate, bivariate, and multivariate statistics.
Market demand and sales forecasting techniques.
Forecasting the sales of new products: the Bass model.
New product design using conjoint analysis.
Market segmentation and targeting using cluster analysis.
Market positioning using perceptual mapping techniques.
Introduction of individual product choice behaviour models.
Resource allocation decision models using linear programming.

Assessment

In-Class Tests (Individual)	30%
During lectures, two in-class tests will be administered. Generally, these tests will consist of multiple-choice questions and/or a marketing problem that requires the use of some analysis techniques. Since some basic mathematical computations may be required, students are advised to bring a scientific calculator when attending lectures. In order to perform well in these tests, students need to acquire a deep understanding of assigned readings, lecture topics, and tutorial materials. The in-class quizzes will cover all lecture and tutorial materials up to the week of the examination. In the case of the second quiz, it will cover the lecture and tutorial materials up to the week of the exam but not including materials covered in the first in-class quiz. This will enable students to demonstrate objectives 1-4.
Take-Home Tests (Individual)	40%
There are two take-home tests: One is at the mid-term and the other at the end of the semester. Take-home tests will evaluate students' knowledge and understanding of various marketing decision analysis techniques. Students are required to finish these two tests independently and submit their reports before the deadlines. This will enable students to demonstrate objectives 1-4.
Group Project (Group)	30%
There is one group project that relates to market segmentation and new product development. Students are expected to work in groups with up to five members. Note that all students will be required to sign a formal statement on a cover sheet, certifying the relative contributions of each member of a group. Within groups, there may be some variation in contribution. Marks will be adjusted accordingly. Groups failing to include the cover sheet fully signed will incur a penalty of 10%. This will enable students to demonstrate objectives 1-4.

Recommended text(s)

Lilien, Gary L. and Arvind Rangaswamy (2002), Marketing Engineering: Computer-Assisted Marketing Analysis and Planning, 2nd ed., Sydney: Pearson Education International.

Indicative references

Coakes, Sheridan J. and Lyndall G. Steed (2001), SPSS: Analysis without Anguish (Version 10), Brisbane: John Wiley and Sons Australia, Ltd.

Hair, Joseph F. Jr., Rolph E. Anderson, Ronald L. Tatham, and William C. Black (1998), Multivariate Data Analysis, 5th ed., Upper Saddle River, NJ: Prentice-Hall, Inc.

Malhotra, Naresh K., John Hall, Mike Shaw, Peter Oppenheim (2002), Marketing Research: An Applied Orientation, 2nd ed., Sydney: Pearson Education Australia Pty Ltd.