Course coordinator
Dr Aaron Tkaczynski
Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Business School
The digital age has fundamentally altered the way marketing teams collect, process, analyse and disseminate market intelligence. In this course, students will learn market intelligence, analytic techniques, and research practices in order to develop and execute marketing strategies and demonstrate return on investment. Students will acquire critical analysis and decision-making skills to prepare them for the key information and decisions they will make in their career in marketing.
Businesses are collecting more data than ever. Marketing is a business function at the forefront of this data wave. This course will help you to extract key information from data to enhance decision-making for marketing issues. You will be learning about web data, segmentation, choice-based analysis, artificial intelligence in machine learning and data visualisation. MKTG2510 is an applied course comprising of practical seminars and computer-based tutorial sessions.ﾠ
Course requirements
Prerequisites
You'll need to complete the following courses before enrolling in this one:
MKTG1501
Course contact
Course staff
Lecturer
Dr Aaron Tkaczynski
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Please note: Teaching staff do not have access to the timetabling system to help with class allocation. Therefore, should you need help with your timetable and/or allocation of classes, please ensure you email business.mytimetable@uq.edu.au from your UQ student email account with the following details:
- Full Name
- Student ID
- Course Code
Aims and outcomes
This course aims to give you knowledge and skills in using data to help make marketing decisions and evaluate their success.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Understand how to use data from a range of sources to support marketing decision making.
LO2.
Critically evaluate marketing intelligence information sources.
LO3.
Apply marketing intelligence and analytical techniques to solve marketing problems and demonstrate ROI.
LO4.
Work as a team to analyse, organise and communicate marketing intelligence to a managerial audience.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Paper/ Report/ Annotation, Project |
Marketing Performance Report (A1)
|
35% |
11/04/2025 3:00 pm |
| Paper/ Report/ Annotation |
Market Research Report (A2)
|
40% |
23/05/2025 3:00 pm |
| Poster, Product/ Design |
Data Visualisation Report - Infographic (A3)
|
25% |
9/06/2025 3:00 pm |
Assessment details
Marketing Performance Report (A1)
- Mode
- Product/ Artefact/ Multimedia
- Category
- Paper/ Report/ Annotation, Project
- Weight
- 35%
- Due date
11/04/2025 3:00 pm
- Other conditions
- Longitudinal.
- Learning outcomes
- L01, L02, L04
Task description
Students will complete the Marketing Foundations: Analytics Certification on the LinkedIn Learning platform. This module takes between 1-2 hours to complete and can be completed progressively over the four weeks of the course. The completed certification will need to be submitted alongside the Marketing Performance Report.
Students will then complete Microsoft Excel exercises and prepare a Marketing Performance Report that demonstrates an understanding of key metrics used to assess marketing performance and generate insights based on the data. The Performance report written in Microsoft Word will particularly focus on techniques and knowledge from Lectures 2 to 5 of the course.
AI Statement:
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Via Blackboard submission link
Deferral or extension
You may be able to apply for an extension.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Market Research Report (A2)
- Team or group-based
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 40%
- Due date
23/05/2025 3:00 pm
- Other conditions
- Peer assessed.
- Learning outcomes
- L01, L02, L03, L04
Task description
During the semester, students will form groups of 4 or 5 students. Students will work as a team to analyse and report on market research data collected for the course. The market research report will:
- Present the results of data analysis in the form of a written report
- Provide strategic directions based on the results of their analysis
- Reflect upon the possible future directions of the analysis
The report will focus on analysis techniques learned throughout the course, with particular focus on Lectures 6 to 9 of the course.
All teams are required to complete a peer evaluation process during and at the end of the project, which may impact individual marks. Details are to be found on Blackboard.
Although not essential, it is preferred that students form groups with members from their own tutorial.
AI Statement:
Artificial Intelligence (AI) provides emerging tools that may support students in completing this assessment task. Students may appropriately use AI in completing this assessment task. Students must clearly reference any use of AI in each instance. A failure to reference generative AI use may constitute student misconduct under the Student Code of Conduct.
AI Course Context: This assessment involves the analysis of data and interpretation of results. AI will not be useful in the analysis and interpretation of the data. AI can be used in editing and creation of the report but must be referenced in the AI Statement incorporated in the directions for this assignment. All members will be responsible for the content of the report and the truthful reference of AI use in the AI statement.
Submission guidelines
Via Blackboard submission link
Deferral or extension
You may be able to apply for an extension.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Data Visualisation Report - Infographic (A3)
- Mode
- Product/ Artefact/ Multimedia
- Category
- Poster, Product/ Design
- Weight
- 25%
- Due date
9/06/2025 3:00 pm
- Other conditions
- Longitudinal.
- Learning outcomes
- L02, L03, L04
Task description
Students will complete the Data Visualisation for Marketers Marketing Certification on the LinkedIn Learning platform.
This module takes approx. 1 hour to complete.
The completed certification will need to be submitted alongside the Data Visualisation Report.
This assignment involves organising marketing information into a data visualisation infographic.
The assignment will involve designing an infographic lift-out that tells a narrative of a topic using written communication and data visualisation techniques. This semester we will also be using ChatGPT to explore the difference between our research and AI tools and reflect on the impact on Marketing in the future.
AI Statement:
Artificial Intelligence (AI) provides emerging tools that may support students in completing this assessment task. Students may appropriately use AI in completing this assessment task.. Students must clearly reference any use of AI in each instance. A failure to reference generative AI use may constitute student misconduct under the Student Code of Conduct.
AI Course Context: This assessment involves AI as part of the assessment process. Students should not use AI for the research and content for the Data Infographic (Step 1). AI is specifically used in Step 2, to learn about prompt types and content provided by AI on the topic. The reflection (Step 3) should not use AI. Evidence of the use of AI for Step 1 and 3 may constitute student misconduct, and reflections completed by AI will not count as a reflection of the individual student.
Submission guidelines
Via Blackboard submission link
Deferral or extension
You may be able to apply for an extension.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 29 |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 30 - 46 |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 47 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
Grades will be allocated according to University-wide standards of criterion-based assessment.
Supplementary assessment
Supplementary assessment is available for this course.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Library resources are available on the UQ Library website.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Week 1 |
Lecture |
L1 - Course Introduction Learning outcomes: L01, L02 |
Week 2 |
Tutorial |
T1: Tutorial Introduction Learning outcomes: L01 |
Lecture |
L2: Marketing Data Platforms Learning outcomes: L01, L02 |
|
Week 3 |
Tutorial |
T2: Marketing Data Platforms Learning outcomes: L01, L02 |
Lecture |
L3: Digital data Learning outcomes: L01, L02, L03, L04 |
|
Week 4 |
Tutorial |
T3: Meaning from Web Data Learning outcomes: L01, L02, L03 |
Lecture |
L4: Marketing metrics Learning outcomes: L01, L02, L03, L04 |
|
Week 5 |
Tutorial |
T4: Reporting Marketing metrics Learning outcomes: L02, L03, L04 |
Lecture |
L5: Marketing Mix Analysis Learning outcomes: L01, L02, L03, L04 |
|
Week 6 |
Tutorial |
T5: Marketing Mix Analysis Learning outcomes: L01, L02, L03 |
Lecture |
L6: Research Design and Market Segmentation Learning outcomes: L01, L02, L03, L04 |
|
Week 7 |
Tutorial |
T6: Research Design and Market Segmentation Learning outcomes: L01, L02, L03 |
Lecture |
L7: Experimental Design and A/B Testing Learning outcomes: L01, L02, L03, L04 |
|
Week 8 |
Tutorial |
T7: Experimental Design and A/B Testing Good Friday Public Holiday - Friday 18 April 2025 - Check Blackboard for announcements about affected classes. Learning outcomes: L02, L03, L04 |
Lecture |
L8: Best Worse Case Scenario Learning outcomes: L01, L02, L03, L04 |
|
Mid-sem break |
No student involvement (Breaks, information) |
In-semester break |
Week 9 |
Tutorial |
T8: Best Worse Case Scenario Learning outcomes: L01, L02, L03 |
Lecture |
L9: Preference Modelling Learning outcomes: L01, L02, L03, L04 |
|
Week 10 |
Tutorial |
T9: Best Worse Case Scenario Labour Day Public Holiday - Monday 5 May 2025 - Check Blackboard for announcements about affected classes. Learning outcomes: L02, L03, L04 |
Lecture |
L10: Guest Lecture (Preference and Visual Design) Learning outcomes: L01, L03, L04 |
|
Week 11 |
Tutorial |
T10: Independent Study (Tutorials designed for assessment help) Learning outcomes: L03, L04 |
Lecture |
L11: Ethics and Artificial Intelligence Learning outcomes: L01, L03, L04 |
|
Week 12 |
Tutorial |
T11: Ethics and Artificial Intelligence Learning outcomes: L03, L04 |
Lecture |
L12: Infographics and Marketing Intelligence Learning outcomes: L01, L02 |
|
Week 13 |
Tutorial |
T12: Infographics and Marketing Intelligence Learning outcomes: L02, L03, L04 |
Lecture |
L13: Consultation Learning outcomes: L02, L03, L04 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.