Course coordinator
Dr Aaron Tkaczynski
Course overview
- Study period
- Semester 1, 2026 (23/02/2026 - 20/06/2026)
- 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.
Course Changes in Response to Previous Student Feedback
This course includes a greater focus on the usage of artificial intelligence and machine learning in informing marketing intelligence decisions for 2026 as a request from students to mirror what is happening in industry.
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, Practical/ Demonstration |
Marketing Performance Report (A1)
|
35% |
17/04/2026 3:00 pm |
| Paper/ Report/ Annotation |
Market Research Report (A2)
|
40% |
22/05/2026 3:00 pm |
| Poster, Product/ Design |
Data Visualisation Report - Infographic (A3)
|
25% |
8/06/2026 3:00 pm |
Assessment details
Marketing Performance Report (A1)
- Mode
- Product/ Artefact/ Multimedia
- Category
- Paper/ Report/ Annotation, Practical/ Demonstration
- Weight
- 35%
- Due date
17/04/2026 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 1 to 4 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.
The maximum extension allowed is 14 days. Extensions are given in multiples of 24 hours.
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
22/05/2026 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 5 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:
This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI and MT tools.
Submission guidelines
Via Blackboard submission link
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 14 days. Extensions are given in multiples of 24 hours.
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
8/06/2026 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:
This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI and MT tools.
Submission guidelines
Via Blackboard submission link
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 14 days. Extensions are given in multiples of 24 hours.
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 This first week introduces students to Marketing Intelligence. Evidence-based marketing and a review of descriptive statistics is provided. Learning outcomes: L01, L02 |
Week 2 |
Tutorial |
T1: Tutorial Introduction Activities relating to evidence-based marketing will be conducted. Here, students will also perform ice-breaker activities to meet others within their class. Learning outcomes: L01 |
Lecture |
L2: Marketing Data Platforms Students will learn about marketing data and data platforms. A review of research design and data sources is also provided. Learning outcomes: L01, L02 |
|
Week 3 |
Tutorial |
T2: Marketing Data Platforms This tutorial introduces students to Marketing Intelligence Descriptive Statistics using Microsoft Excel. Students will learn to code data, create pivot tables and perform chi-square tests. Learning outcomes: L01, L02 |
Lecture |
L3: Marketing Metrics This lecture teaches students about marketing metrics. A focus on benchmarking and brand metrics is provided. Students will also learn about customer satisfaction (retention rate) and customer lifetime value. Learning outcomes: L01, L02, L03, L04 |
|
Week 4 |
Tutorial |
T3: Marketing Metrics This this tutorial sees students learn about Marketing Metrics using Microsoft Excel. Here, an understanding of brand equity and customer lifetime value is learnt. Students will also about evaluating a digital Data campaign using Microsoft Excel. Learning outcomes: L01, L02, L03 |
Lecture |
L4: Marketing Mix Analysis This lecture sees students understands about the marketing mix analysis performed by marketing intelligence experts. An understanding of regression analysis is provided to explain the importance of independent and dependent variables when analysing data. Learning outcomes: L01, L02, L03, L04 |
|
Week 5 |
Tutorial |
T4: Marketing Mix Analysis This fourth tutorial introduces students to using Microsoft Excel to perform regression analysis and ANOVA. Students will now have all information required to complete their first assignment. Learning outcomes: L02, L03, L04 |
Lecture |
L5: Digital Data Students will learn about the importance of inbound marketing from a digital marketing perspective. Search engine marketing and data management for both qualitative and quantitative will also be learnt by students. Learning outcomes: L01, L02, L03, L04 |
|
Week 6 |
Tutorial |
T5: Digital Data Analysis Students will learn about analysis digital data through using qualitative analysis techniques such as Word Cloud. An understanding of ANOVA using Microsoft Excel will also be gained by students during this week. Good Friday Public Holiday - Friday 3rd April 2026 - Check Blackboard for announcements about affected classes. Learning outcomes: L01, L02, L03 |
Lecture |
L6: Research Design and Segmentation Analysis This week students learn about sampling and market segmentation within marketing intelligence. It also teaches students how best to position an organisation using customer data. Learning outcomes: L01, L02, L03, L04 |
|
Mid-sem break |
No student involvement (Breaks, information) |
In-semester break |
Week 7 |
Tutorial |
T6: Research Design and Market Segmentation Within this tutorial students will learn about cluster analysis using both Microsoft Excel and SPSS. Learning outcomes: L01, L02, L03 |
Lecture |
L7: Experimental Design and A/B Testing Students will learn about experimentation in marketing intelligence. A focus on A/B testing as a form of experimental design will be provided. Learning outcomes: L01, L02, L03, L04 |
|
Week 8 |
Tutorial |
T7: Experimental Design and A/B Testing Students in this tutorial will learn how to conduct an A/B test using Microsoft Excel. An understanding of student t-tests also within Microsoft Excel will be learnt. Learning outcomes: L02, L03, L04 |
Lecture |
L8: Best Worse Case Scenario This week introduces students to choice modelling. This lecture focuses on best worst scaling. Learning outcomes: L01, L02, L03, L04 |
|
Week 9 |
Tutorial |
T8: Best Worse Case Scenario Students in this week's tutorial will learn how to perform a best case scenario using Microsoft Excel. Data will be provided that relates to students' perceptions of different universities rankings in South East Queensland, Learning outcomes: L01, L02, L03 |
Lecture |
L9: Preference Modelling This week contains the second part of choice modelling. This lecture focuses on preference modelling through the usage of discrete choice experiments. Learning outcomes: L01, L02, L03, L04 |
|
Week 10 |
Tutorial |
T9: Best Worse Case Scenario This week will see students perform a discrete choice analysis using SPSS. Students will be able to identify which attributes that respondents prefer when faced with multiple options. Labour Day Public Holiday - Monday 4th May 2026 - Check Blackboard for announcements about affected classes. Learning outcomes: L02, L03, L04 |
Lecture |
L10: Guest Lecture (Preference and Visual Design) This week will see students learn from a guest industry lecturer the usage of preference modelling and visual design. Learning outcomes: L01, L03, L04 |
|
Week 11 |
Tutorial |
T10: Independent Study (Tutorials designed for assessment help) This week is provided as consultation where students can consult with their tutors about information for their second assignment. Learning outcomes: L03, L04 |
Lecture |
L11: Ethics and Artificial Intelligence This week will have students learning about ethics in marketing intelligence. A focus on Artificial Intelligence is also provided. Learning outcomes: L01, L03, L04 |
|
Week 12 |
Tutorial |
T11: Ethics and Artificial Intelligence This tutorial will see students employ Artificial Intelligence in the form of ChatGPT to complete tasks relating to marketing intelligence. Learning outcomes: L03, L04 |
Lecture |
L12: Infographics and Marketing Intelligence This week students will learn about infographics and their usage for visual presentation in marketing intelligence. Learning outcomes: L01, L02 |
|
Week 13 |
Tutorial |
T12: Infographics and Marketing Intelligence Students will perform tutorial tasks that teach them about designing an infographic in Canva. An opportunity for consultation is also provided. Learning outcomes: L02, L03, L04 |
Lecture |
L13: Consultation This final lecture allows students to communicate with the course co-ordinator about guidance and final feedback for assessment. 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 for Students Policy and Procedure
- AI for Assessment Guide
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.