Outline

Overview

Companies operate in an increasingly challenging market environment, with greater competition, more informed customers, and rapidly changing market trends. Simultaneously, they also have access to more information about their customers, the marketplace and their competitors than ever before. Hence, it is imperative that all marketing professionals understand the market data and how to most effectively deal with it. This unit introduces students to basic principles of marketing analytics and demonstrates how to practically apply these techniques in marketing contexts. The unit focuses on developing analytical senses in actual business situations, by providing hands-on experiences to integrate and apply diverse tools to solve managerial problems using various market data. Through the unit, how marketing analytics can help managers interpret data and transform research findings into actionable business insights is discussed. The unit is taught using a variety of materials and exercises including lectures for key concepts and processes, in-class exercises, and applied research projects to enhance students' understanding and skill.

Unit details and rules

Academic unit	Marketing
Credit points	6
Prerequisites ?	MKTG5001
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Understanding of basic marketing principles, statistics, and how to use analytic skills to solve marketing problems
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Jordan Truong, jordan.truong@sydney.edu.au

Assessment

The census date for this unit availability is 1 September 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Research analysis	Research Report Each group will submit a research report applying the marketing analytics methods discussed throughout the semester.	30%	Formal exam period Due date: 17 Nov 2025 at 23:59 Closing date: 27 Nov 2025	6000 words	AI allowed
Outcomes assessed:
Out-of-class quiz	Multiple Choice Quizzes Students will have weekly quizzes from Week 3 to Week 13, excluding Week 8. Each quiz will contain 10 multiple-choice questions and be worth 1%.	10%	Multiple weeks	15 minutes per quiz	AI allowed
Outcomes assessed:
Presentation	Weekly Class Presentation Students will work in groups of 3, select a topic, and present it during the assigned week. Class presentations will run from Week 3 to Week 12, excluding Week 8.	20%	Multiple weeks	20 minutes including Q&A	AI allowed
Outcomes assessed:
Contribution	Individual Particpation Students are expected to participate and engage in class throughout the semester.	10%	Ongoing	Throughout the semester	AI allowed
Outcomes assessed:
Written test ?	Mid Semster Test Students will complete the mid-semester test in Week 8.	20%	Week 08 Due date: 26 Sep 2025 at 09:40	50 minutes	AI prohibited
Outcomes assessed:
Presentation	Research Report Presentation Each group will present the key findings, insights, and strategic recommendations from their group research report.	10%	Week 13	20 minutes including Q&A	AI allowed
Outcomes assessed:
= group assignment ?

Assessment summary

Class participation: The tutorials are designed to support the lecture content and provide guidance to the planning, execution, discussion, analysis and reporting of the group assessment components.

Individual Assessment: The individual assessment is designed to evaluate your comprehension ofthe unit materials covered throughout the semester.

Group Assessment: In this group assessment, you will collaborate with your team members on acomprehensive marketing analytics project. The process will encompass data collection, data cleaning, analysis, and deriving findings and insights.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100	Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Distinction	75 - 84	Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Credit	65 - 74	Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Pass	50 - 64	Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to Marketing Analytics	Lecture and tutorial (3 hr)
Week 02	Measurement Scales, Descriptive Analysis, and Hypothesis Testing	Lecture and tutorial (3 hr)
Week 03	Test for Difference 1: T-Tests	Lecture and tutorial (3 hr)
Week 04	Test for Difference 2: ANOVA and MANOVA	Lecture and tutorial (3 hr)
Week 05	Test for Association 1: Chi-Square and Correlation	Lecture and tutorial (3 hr)
Week 06	Test for Association 2: Multiple Regression and Assumptions	Lecture and tutorial (3 hr)
Week 07	Test for Association 3: Dummy Variables, Quadratic Relationship and Interaction Effect	Lecture and tutorial (3 hr)
Week 08	Mid-semester test	Lecture and tutorial (3 hr)
Week 09	Test for Interdependence 1: Factor Analysis	Lecture and tutorial (3 hr)
Week 10	Test for Interdependence 2: Cluster Analysis	Lecture and tutorial (3 hr)
Week 11	Automated Text Analysis using LIWC	Lecture and tutorial (3 hr)
Week 12	Unstructured Data Analysis	Lecture and tutorial (3 hr)
Week 13	Course Conclusion & Research Report Presentation	Lecture and tutorial (3 hr)

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Identify the basic principles of marketing analytics and statistical techniques
LO2. Analyse marketing problems and determine suitable analytical methods to solve research questions
LO3. Organize, integrate, and interpret data from multiple sources
LO4. Apply the concept of marketing analytics and develop managerial insights to make effective business decisions
LO5. Collaborate with others to effectively manage a group research project
LO6. Create research reports using appropriate tools and communicate your ideas with others effectively

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

1. Updated effective date and teaching staff details 2. Updated learning activities 3. Updated assessment structure

Additional information

Business School Policies

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

MKTG6024: Marketing Analytics

Semester 2, 2025 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links