MKTG3008 – Marketing and Customer Insights

2021 – Session 2, Special circumstances

Session 2 Learning and Teaching Update

The decision has been made to conduct study online for the remainder of Session 2 for all units WITHOUT mandatory on-campus learning activities. Exams for Session 2 will also be online where possible to do so.

This is due to the extension of the lockdown orders and to provide certainty around arrangements for the remainder of Session 2. We hope to return to campus beyond Session 2 as soon as it is safe and appropriate to do so.

Some classes/teaching activities cannot be moved online and must be taught on campus. You should already know if you are in one of these classes/teaching activities and your unit convenor will provide you with more information via iLearn. If you want to confirm, see the list of units with mandatory on-campus classes/teaching activities.

Visit the MQ COVID-19 information page for more detail.

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff
Firouzeh Taghikhah
Credit points Credit points
Prerequisites Prerequisites
40cp at 2000 level or above including MKTG2002 or MKTG202
Corequisites Corequisites
Co-badged status Co-badged status
Unit description Unit description

The digital revolution has created an enormous volume of data about markets, customers and the business environment which marketers have sought to incorporate into their strategic decision-making. Yet, raw data on its own adds very little to the strategic decision process. Marketers need to understand how to organise and analyse available data to generate actionable insights. Such insights are useful in anticipating future consumer needs, identifying trends, forecasting market conditions, gauging competition and making informed predictions about an ever-changing environment. Marketers then utilise these insights to build compelling narratives and to provide actionable recommendations for important marketing decisions.

In this unit students will investigate appropriate data, data sources and analytic techniques required to generate input for key marketing decisions regarding markets and customers. Students will assess suitable data analysis techniques and evaluate generated output to develop insights and determine potential marketing decision options. Additionally, students will appraise these key options by estimating likely impacts and integrating these impacts with practical organisational issues.

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at

Learning Outcomes

On successful completion of this unit, you will be able to:

  • ULO1: Utilise data sources and variables to investigate marketing problems/opportunities.
  • ULO2: Apply models/frameworks to generate marketing insights from data.
  • ULO3: Synthesise insights and communicate recommendations to marketing decision-makers.

General Assessment Information

Late Submission Statement:

Sometimes unavoidable circumstances occur that might prevent you from submitting an assignment on time and, in that case, you may be eligible to lodge a Special Consideration request.

Unless a Special Consideration request has been submitted and approved, please note that no extensions to assignment deadlines will be granted. Assignments that are submitted late will attract a late penalty:

  1. There will be a deduction of 10% of the total available marks made from the total awarded mark for each 24 hour period or part thereof that the submission is late.
  2. No assignment will be accepted more than 72 hours after the due date and time (incl. weekends) after the original due date.
  3. No late submissions will be accepted for timed assessments (e.g., quizzes, online tests) or for tasks with a weighting of 10% or less of the total unit assessment.


Assessment Tasks

Name Weighting Hurdle Due
Customer Insights Critique 25% No Week 7
Report 50% No Week 12
Online Assignment 25% No Weeks 2 -11

Customer Insights Critique

Assessment Type 1: Case study/analysis
Indicative Time on Task 2: 12 hours
Due: Week 7
Weighting: 25%


Students will critique a provided Insights Analysis. They will apply models/frameworks to provided data sources to generate insights and then synthesise insights to communicate recommendations. Length: 1500 words


On successful completion you will be able to:
  • Apply models/frameworks to generate marketing insights from data.
  • Synthesise insights and communicate recommendations to marketing decision-makers.


Assessment Type 1: Report
Indicative Time on Task 2: 25 hours
Due: Week 12
Weighting: 50%


Students will use customer datasets and apply models/frameworks to generate insights and then synthesise the insights to communicate recommendations in response to marketing problems/opportunities. Length: 2500 words


On successful completion you will be able to:
  • Apply models/frameworks to generate marketing insights from data.
  • Synthesise insights and communicate recommendations to marketing decision-makers.

Online Assignment

Assessment Type 1: Participatory task
Indicative Time on Task 2: 12 hours
Due: Weeks 2 -11
Weighting: 25%


Students will provide a written response to a weekly question that is posted to iLearn. Students’ responses need to use data sources to investigate marketing problems/opportunities and connect content to models/frameworks. Each response will be a minimum of 100 words and a maximum of 200 words.


On successful completion you will be able to:
  • Utilise data sources and variables to investigate marketing problems/opportunities.
  • Apply models/frameworks to generate marketing insights from data.

1 If you need help with your assignment, please contact:

  • the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
  • the Writing Centre for academic skills support.

2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation

Delivery and Resources


  • 3 hours per week consisting of 1 hour of online lecture and one 2-hour workshop each week
  • The timetable for classes can be found on the University website at:

Required and Recommended Texts and/or Materials

Prescribed Text:

  • Joseph Hair, Dana E. Harrison and Haya Ajjan (2021) Essentials of Marketing Analytics (International Student Edition) ” McGraw-Hill Education - ISBN13: 9781264263608; ISBN10: 1264263600

Other Useful Texts:

  • Ömer Artun and Dominique Levin (2015) “Predictive Marketing: Easy Ways Every Marketer Can Use Customer Analytics and Big Data” Wiley, ISBN 978-1-119-03736-1 (hardback); ISBN 978-1-119-03732-3 (ePDF); ISBN 978-1-119-03733-0 (ePub)
  • Mike Grigsby (2018) “Marketing Analytics: A Practical Guide to Improving Consumer Insights Using Data Techniques” 2nd Edition, Kogan Page, ISBN 978 0 7494 8216 9; E-ISBN 978 0 7494 8217 6

Technology Used and Required:

  • Students will need to have access to a personal computer, with access to the Internet and word processing software.
  • Software which will feature in the unit: MS-Word, MS-PowerPoint, MS-Excel, SPSS statistical software, SQL software, IBM Cognos Analytics, Tableau, RapidMiner, Python, TextBlob, Vader

Unit Web Page

  • The web page for this unit can be found at: iLearn
  • All announcements and resources will be available on the web site. Resource materials include lecture slides, practice questions, case studies and other materials. There is also a forum for student interaction and contact with faculty. You should consult the unit Website several times per week for messages and updates.

Learning and Teaching Activities

  • This unit will be conducted via a 1-hour online lecture and a 2-hour weekly workshop each week. The unit topics will be divided into 4 main modules with each module typically consisting of 3 weeks. Each workshop will consist of activities including case study discussions, data analysis and interactive learning tasks.

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central ( Students should be aware of the following policies in particular with regard to Learning and Teaching:

Students seeking more policy resources can visit Student Policies ( It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.

To find other policies relating to Teaching and Learning, visit Policy Central ( and use the search tool.

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct:


Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit or if you are a Global MBA student contact

Student Support

Macquarie University provides a range of support services for students. For details, visit

Learning Skills

Learning Skills ( provides academic writing resources and study strategies to help you improve your marks and take control of your study.

The Library provides online and face to face support to help you find and use relevant information resources. 

Student Services and Support

Students with a disability are encouraged to contact the Disability Service who can provide appropriate help with any issues that arise during their studies.

Student Enquiries

For all student enquiries, visit Student Connect at

If you are a Global MBA student contact

IT Help

For help with University computer systems and technology, visit

When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.

Changes from Previous Offering

This unit is being offered for the first time in S2, 2021

Changes since First Published

Date Description
16/07/2021 There is further clarification of the procedure for late assessments