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	Aekyoung Kim, aekyoung.kim@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Record+)	Final exam Written exam	30%	Formal exam period	2 hours
Outcomes assessed:
Participation	Class participation Participation	10%	Multiple weeks	As needed
Outcomes assessed:
In-semester test (Record+)	Mid-semester exam In-class exam	20%	Week 07 Due date: 15 Sep 2022 at 10:00	1 hour
Outcomes assessed:
Assignment	Research Project n/a	30%	Week 12	3000 words
Outcomes assessed:
Presentation	Presentation n/a	10%	Week 12	15 minutes
Outcomes assessed:
= group assignment ? = Type B final exam ? = Type B in-semester exam ?

Assessment summary

Research Project: In this report, you will discuss your research plan, provide evidence of your data analysis, and report your data findings/insights

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.

Mid-semester & Final exam: The exam will assess your understanding of the unit materials presented during the semester.

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 guide to grades.

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 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.

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

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to Marketing Analytics	Lecture and tutorial (3 hr)
Week 02	Data Implementation	Lecture and tutorial (3 hr)
Week 03	Exploratory Analysis	Lecture and tutorial (3 hr)
Week 04	Segmentation	Lecture and tutorial (3 hr)
Week 05	Factor Analysis	Lecture and tutorial (3 hr)
Week 06	Perceptual Map and Positioning	Lecture and tutorial (3 hr)
Week 07	Mid-semester test	Lecture and tutorial (3 hr)
Week 08	New Product Design	Lecture and tutorial (3 hr)
Week 09	Customer Satisfaction Analysis	Lecture and tutorial (3 hr)
Week 10	Marketing Performance Analysis I	Lecture and tutorial (3 hr)
Week 11	Marketing Performance Analysis II	Lecture and tutorial (3 hr)
Week 12	Digital Marketing and Search Analytics	Lecture and tutorial (3 hr)
Week 13	Data Mining and Machine Learning	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.

The order of lecture contents was changed.

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

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

Media

Student links

About us

Connect

Current students

MKTG6024: Marketing Analytics

Semester 2, 2022 [Normal day] - Remote

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

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

Media

Student links

About us

Connect