Outline

Overview

In today’s data driven environment, marketers who can effectively generate and interpret data insights play a pivotal role. Marketing Analytics provides students with an understanding of the use and application of analytical tools in marketing. Building on knowledge of the marketing research process, Marketing Analytics emphasises the role of data to generate insights to aid marketing decisions. This equips students with knowledge of common statistical techniques and an understanding of data visualisation tools.

Unit details and rules

Academic unit	Marketing
Credit points	6
Prerequisites ?	MKTG1001 and MKTG1002
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Stacey Brennan, stacey.brennan@sydney.edu.au
Lecturer(s)	Amanda Kennedy, amanda.kennedy@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2025

Details
Criteria

Type	Description	Weight	Due	Length
Supervised exam ?	Final Exam Final Exam	30%	Formal exam period	2 hours
Outcomes assessed:
Participation	In-Class Participation In Class-Participation BRC Data Collection Task	10%	Ongoing	Ongoing Weeks 2-13
Outcomes assessed:
Online task	Early Feedback Task Online Multiple Choice Quiz #earlyfeedbacktask	10%	Week 03 Due date: 11 Mar 2025 at 23:59 Closing date: 16 Mar 2025	15 minutes
Outcomes assessed:
Assignment	Client Research Brief and Questionnaire Development Client Research Brief and Questionnaire Development	20%	Week 06 Due date: 06 Apr 2025 at 23:59 Closing date: 20 Apr 2025	1500 words
Outcomes assessed:
Assignment	Group Data Analysis Report Data Analysis Report	30%	Week 13 Due date: 01 Jun 2025 at 23:59 Closing date: 15 Jun 2025	3000 words
Outcomes assessed:
= group assignment ? = AI allowed ? = early feedback task ?

Early feedback task

This unit includes an early feedback task, designed to give you feedback prior to the census date for this unit. Details are provided in the Canvas site and your result will be recorded in your Marks page. It is important that you actively engage with this task so that the University can support you to be successful in this unit.

Assessment summary

Early Feedback Task. Multiple Choice Quiz: This online multiple-choice quiz will test students on the content covered in Weeks 1 and 2. This quiz is designed to give early feedback to students regarding their understanding of the unit content.
Client Research Brief and Questionnaire Development Assignment: Students will explain why research needs to be conducted for a given scenario, create an initial research question, undertake a literature review to source appropraite variables, design and pre-test an effective questionnaire.
Data Analysis Report: Students will form groups of up to 5 members from the same tutorial class. Students will be provided with Qualitative and Quantitative Data and will need to analyse and interpret this data to provide recommendations to their client.
Final Exam: The Final Exam will test students understanding of the key theory and concepts taught in the unit.
Participation: To promote a learning and knowledge sharing atmosphere, students are encouraged to prepare prior and participate during class. Participation marks are awarded for in-class contributions, data collection tasks and the BRC.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2021 (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 guide to grades.

Use of generative artificial intelligence (AI) and automated writing tools

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator.

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions.

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit.

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply.

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site.

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

In accordance with University policy, these penalties apply when written work is submitted after 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 Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

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 (2.5 hr)
Week 02	Utilising Data for Marketing Analytics	Lecture and tutorial (2.5 hr)
Week 03	Survey Design and Data Measurement	Lecture and tutorial (2.5 hr)
Week 04	Qualitative Data Analysis and the use of AI	Lecture and tutorial (2.5 hr)
Week 05	Quantitative Data Preparation	Lecture and tutorial (2.5 hr)
Week 06	Quantitative Data Analysis: Descriptive Analysis	Lecture and tutorial (2.5 hr)
Week 07	Quantitative Data Analysis: Analysing Relationships	Lecture and tutorial (2.5 hr)
Week 08	Quantitative Data Analysis: Comparing Means (Part 1)	Lecture and tutorial (2.5 hr)
Week 09	Quantitative Data Analysis: Comparing Means (Part 2)	Lecture and tutorial (2.5 hr)
Week 10	Quantitative Data Analysis: Comparing Means (Part 3)	Lecture and tutorial (2.5 hr)
Week 11	Quantitative Data Analysis: Regression	Lecture and tutorial (2.5 hr)
Week 12	Data Visualization	Lecture and tutorial (2.5 hr)
Week 13	Final Exam Preparation	Lecture and tutorial (2.5 hr)

Attendance and class requirements

Lecture recording: Lectures will be recorded and will be available on Canvas, however, please note the Business School does not own the system and cannot guarantee that the system will operate or that every lecture will be recorded. There is also no guarantee that the sound quality will be acceptable. It is therefore strongly recommended that students attend all 13 lectures in person.

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.

Required readings

Allen, P., Bennett, K. & Heritage, B. (2023) SPSS Statistics: A Practical Guide, 5th Edition, Cengage Learning.

Recommended Additional Text

Babin, B., D'Alessandro, S., Winzar, H., Lowe, B. and Zikmund, W. (2020) Marketing Research, 5th Asia Pacific Edition, Cengage Learning. [ISBN 9780170438964]. - text used in MKTG1002 or any recent Marketing Research text.

More information regarding these texts will be provided on Canvas.

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. Demonstrate an understanding of marketing analytics concepts and how they can be applied to inform business decision-making.
LO2. Design an effective data collection tool to gather meaningful and relevant data, enabling analytical insights to inform marketing decisions.
LO3. Analyse qualitative and quantitative research data using appropriate analytical methods.
LO4. Interpret qualitative and quantitative data and develop managerial insights to make effective business decisions.
LO5. Collaborate in teams to solve marketing problems by using analytical tools and presenting data-driven recommendations

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.

In response to student feedback the content in the early weeks of the semester has been updated to be more specific to marketing analytics and more distinct from MKTG1002. The lecture has been reduced from 2 hours to 1 hour while the tutorials have been increased from 1 hour to 1.5 hours. A rubric has been created for in class participation to clearly show how these marks are awarded.

Additional information

More information can be found on Canvas.

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

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI) and automated writing tools

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

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

MKTG2113: Marketing Analytics

Semester 1, 2025 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI) and automated writing tools

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

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