Students

MKTG3008 – Marketing and Customer Insights

2022 – Session 2, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff
Cynthia Webster
cynthia.webster@mq.edu.au
Credit points Credit points
10
Prerequisites Prerequisites
40cp at 2000 level or above including MKTG2013 and MKTG2017
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 https://www.mq.edu.au/study/calendar-of-dates

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 Assessment Submission Penalty (written assessments) 

Unless a Special Consideration request has been submitted and approved, a 5% penalty (of the total possible mark) will be applied each day a written assessment is not submitted, up until the 7th day (including weekends). After the 7th day, a grade of ‘0’ will be awarded even if the assessment is submitted. Submission time for all written assessments is set at 11.55pm. A 1-hour grace period is provided to students who experience a technical concern. 

Table 1: Penalty calculation based on submission time

Submission time after the due date (including weekends)

Penalty (% of available assessment task mark)

Example: for a non-timed assessment task marked out of 30

< 24 hours

5%

5% x 30 marks = 1.5-mark deduction

24-48 hours

10%

10% x 30 marks = 3-mark deduction

48-72 hours

15%

15% x 30 marks = 4.5-mark deduction

72-96 hours

20%

20% x 30 marks = 6-mark deduction

Special Consideration To request an extension on the due date/time for a timed or non-timed assessment task, you must submit a Special Consideration application. An application for Special Consideration does not guarantee approval.

The approved extension date for a student becomes the new due date for that student. The late submission penalties above then apply as of the new due date.

Assessment Tasks

Name Weighting Hurdle Due
Customer Insights Critique 25% No Week 7 Sunday Sep 11, 11:00PM
Online Assignment 25% No Weeks 2 -11 by the end of each week (Sunday 11pm)
Report 50% No Week 12 Sunday, 30 October 11.00pm

Customer Insights Critique

Assessment Type 1: Case study/analysis
Indicative Time on Task 2: 12 hours
Due: Week 7 Sunday Sep 11, 11:00PM
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.

Online Assignment

Assessment Type 1: Participatory task
Indicative Time on Task 2: 12 hours
Due: Weeks 2 -11 by the end of each week (Sunday 11pm)
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.

Report

Assessment Type 1: Report
Indicative Time on Task 2: 25 hours
Due: Week 12 Sunday, 30 October 11.00pm
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.

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

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

RECOMMENDED TEXT

  • Winsoton, Wayne L. (2014), Marketing Analytics: Data-Driven Techniques with Microsoft Excel, John Wiley & Sons, Indianapolis, IA (Ebook is also available from publisher's site).
  • Rajkumar Venkatesan, Paul Farris and Ronald T. Wilcox (2015) “Cutting Edge Marketing Analytics: Real World Cases and Data Sets for Hands-On Learning” Pearson Education - ISBN-13: 978-0133552522; ISBN-10: 0133552527
  • 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 NEEDS

  • 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, Tableau, Python, Knime, Vensim

 

DELIVERY FORMAT

  • 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: http://www.timetables.mq.edu.au/

 

EMAIL USE

It is University policy that the University issued email account will be used for official University communication. All students are required to access their University account frequently. Only contact Macquarie University staff (including tutors), using your official MQ student’s account because this is one method used to verify your identity.

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). 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 (https://students.mq.edu.au/support/study/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 (https://policies.mq.edu.au) and use the search tool.

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/student-conduct

Results

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 ask.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au

Academic Integrity

At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

The Writing Centre

The Writing Centre provides resources to develop your English language proficiency, academic writing, and communication skills.

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

Student Services and Support

Macquarie University offers a range of Student Support Services including:

Student Enquiries

Got a question? Ask us via AskMQ, or contact Service Connect.

IT Help

For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/. 

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 since First Published

Date Description
25/07/2022 Penalty percentage

Unit information based on version 2022.04 of the Handbook