24331 Marketing Analytics and Decisions
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Subject handbook information prior to 2024 is available in the Archives.
Credit points: 6 cp
Subject level:
Undergraduate
Result type: Grade and marksRequisite(s): ((24108 Marketing Foundations OR 24109 Marketing and Customer Value) AND 24309 Marketing Research)
These requisites may not apply to students in certain courses.
There are course requisites for this subject. See access conditions.
Description
This subject introduces the theories and techniques of marketing analytics in the context of various marketing decision-making environments. It discusses and explains the major theories and analytical models in areas of new product development, segmentation, targeting and positioning. It places particular emphasis on the application of statistical and computational models to decision-making issues likely to confront marketing managers today and in the future.
Subject learning objectives (SLOs)
| 1. | explain the role that marketing analytics plays in enhancing marketing decision-making in an increasingly complex business environment |
|---|---|
| 2. | apply analytical, quantitative and computer modeling techniques in managerial marketing decisions |
| 3. | interpret and communicate results of quantitative analyses for marketing decision-making |
Contribution to the development of graduate attributes
This subject is designed to introduce students to a variety of computer-aided decision models used in managerial 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, customer lifetime analysis, and sales forecasting. Software packages such as MS Excel, Tableau and R are used to illustrate the lecture topics and allow students to develop practical data analysis skills to tackle various marketing problems. The course will be particularly valuable to students planning careers in marketing research and marketing management. This subject will develop skills linked to the Faculty’s graduate attribute that looks at developing critical thinking, creativity and analytical skills.
This subject contributes to the development of the following graduate attributes:
- Intellectual rigour and innovative problem solving
- Communication and collaboration
- Professional and technical competence
Teaching and learning strategies
The subject is based on dynamic and interactive lecture and tutorial sessions. Weekly activities include (1) in-class discussions, (2) lectures, and (3) tutorials. The UTS Learning Management System will be used to share information and encourage interaction between teaching staff and students.
In-class discussions
Four practice quiz questions will be made available to students prior to each actual quiz, and will form the basis of in-class discussions. In the remaining weeks, in-class discussions will be centred on the three case studies and/or each group’s research project.
Lectures
Students are expected to attend all lectures. Lectures introduce and describe the key concepts and analytical tools through active and collaborative learning activities such as short quizzes, critical debate, and buzz groups. Students can share their insights through collaborative student discussions.
Tutorials
Tutorials provide an interactive opportunity to extend and apply the subject content taught in lectures. Tutorials are built around in-class exercises via the use of computer-aided analytical tools. During tutorials, students will use both Marketing Engineering for Excel and SPSS Statistics to complete the assigned in-class exercises.
UTS Learning Management System
The learning management system is used to disseminate learning resources, including the subject outline, practice quizzes, lecture slides, workshop exercises, group assignment briefs & file exchange, and any other supplementary information. Students are responsible for checking the site regularly. All announcements posted online will also be emailed to students so that they stay informed.
Feedback
Informal feedback from both the class instructor and classmates throughout the session will be given in weekly in-class discussions, lectures, and tutorials to clarify students’ understandings of key data analysis concepts. Formal feedback will be provided on assessment items of individual in-class quiz, final exam, and group case study & research project report.
Content (topics)
- Market response modelling
- Segmentation and classification models
- Positioning map analysis
- Conjoint analysis for new product development
- Best-worst scaling
- GE/McKinsey portfolio model
- New product forecasting models
Assessment
Assessment task 1: In-Class Assessment (Individual)*
| Intent: | This assessment is designed to provide feedback to students regarding their development of the graduate attribute business knowledge and concepts |
|---|---|
| Objective(s): | This addresses subject learning objective(s): 1 and 2 |
| Weight: | 30% |
| Criteria: | The in-class assessment will be marked based on the following criteria:
*Note: Late submission of the assessment task will not be marked and awarded a mark of zero. |
Assessment task 2: Case Presentation & Project Report (Group) *
| Intent: | This assessment task is designed to develop the following graduate attributes: critical thinking, creativity and analytical skills and communications and interpersonal skills |
|---|---|
| Objective(s): | This addresses subject learning objective(s): 1, 2 and 3 |
| Weight: | 30% |
| Criteria: | The case presentation will be graded on the following criteria:
The group project report will be graded on the following criteria:
*Note: Late submission of the assessment task will not be marked and awarded a mark of zero. |
Assessment task 3: Final exam (Individual)
| Intent: | The final exam will provide students with feedback regarding their development of the following graduate attributes: business knowledge and concepts; critical thinking, creativity and analytical skills |
|---|---|
| Objective(s): | This addresses subject learning objective(s): 1, 2 and 3 |
| Weight: | 40% |
| Criteria: | The final exam will be graded on the following criteria:
|
Minimum requirements
Students must achieve at least 50% of the subject’s total marks.
Required texts
Winsoton, Wayne L. (2014), Marketing Analytics: Data-Driven Techniques with Microsoft Excel, John Wiley & Sons, Indianapolis, IA (Ebook is also available from publisher's site).
References
- Grigsby, Mike (2018), Marketing Analytics, Koganpage, London UK
- Lilien, Gary L., Arvind Rangaswamy and Arnaud De Bruyn (2017), Principles of Marketing Enginnering, 3rd Edition, State College, PA: DecisionPro, Inc (Ebook is also available via Google play store).
- Faculty of Business (2014), Guide to Writing Assignments, 3.1 Edition, Faculty of Business, University of Technology, Sydney.
- Zikmund, William, Steve D'Alessandro, Hume Winzar, Ben Lowe, and Barry Babin (2014), Marketing Research, Third Asia-Pacific Edition, Melbourne, VIC: Cengage Learning.
Other resources
Canvas is a web-based learning tool. In this subject, Canvas is used for asking and answering questions (via Discussion Forums), (1) keeping up to date via Announcements; (2) accessing learning resources via Subject Materials; (3) asking and answering questions via Discussion Forums; and (4) checking your grades via Tools.