Notice
As part of Phase 3 of our return to campus plan, most units will now run tutorials, seminars and other small group learning activities on campus for the second half-year, while keeping an online version available for those students unable to return or those who choose to continue their studies online.
To check the availability of face to face activities for your unit, please go to timetable viewer. To check detailed information on unit assessments visit your unit's iLearn space or consult your unit convenor.
| Unit convenor and teaching staff |
Unit convenor and teaching staff
Ivan Ho
Contact via Email or iLearn
Room 145, 3 Management Drive
Friday 11am-12pm
|
|---|---|
| Credit points |
Credit points
10
|
| Prerequisites |
Prerequisites
(MKTG101 or MKTG1001) and (MKTG1003 or MKTG203)
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| Corequisites |
Corequisites
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| Co-badged status |
Co-badged status
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| Unit description |
Unit description
The face of marketing is becoming increasingly more technology- and data-driven. With growing access to technology, an ocean of data, and ever-increasing number of channels, companies are demanding for marketing talent that can help them navigate this complexity. This unit develop students' knowledge and capabilities to interrogate customer data to uncover marketing insights; to use automation to optimise the marketing mix; and the deployment of intelligent work flow design to facilitate greater agility in marketing executions. Students will develop knowledge of software and technology platforms, skills in analytical thinking, problem solving and knowledge of sustainability in developing a marketing strategy. |
Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
On successful completion of this unit, you will be able to:
Late assessment submissions must also be submitted through the appropriate submission link in iLearn. No extensions will be granted unless an application for Special Consideration is made and approved. 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. Late submissions will not be accepted after solutions have been discussed and/or made available.
Note: Further information on submitting an Application for Special Consideration can be found at https://students.mq.edu.au/study/my-study-program/special-consideration
| Name | Weighting | Hurdle | Due |
|---|---|---|---|
| Case Study/Analysis | 40% | No | Week 13 |
| Essay (Research-Based) | 40% | No | Week 10 |
| Online participation | 20% | No | Week 5 and 8 |
Assessment Type 1: Case study/analysis
Indicative Time on Task 2: 15 hours
Due: Week 13
Weighting: 40%
This is a problem-based case study given to students, where they have to critique the application of technologies in marketing, with a special focus on the ethical and sustainable use of customer data in developing marketing strategies. This is an individual-based assignment. 2,000 words
Assessment Type 1: Essay
Indicative Time on Task 2: 17.5 hours
Due: Week 10
Weighting: 40%
This is an individual-based essay in which students have to research and write on how artificial intelligence, automation and workplace agility have been applied in recent years to enhance marketing strategies and its executions. 4,000 words
Assessment Type 1: Participatory task
Indicative Time on Task 2: 17.5 hours
Due: Week 5 and 8
Weighting: 20%
Topics related to marketing technologies will be given to students throughout the unit. Students will be asked to conduct research on these topics and participate in online discussions in which they evaluate the efficiency and effectiveness of technologies in enhancing marketing strategies.
1 If you need help with your assignment, please contact:
2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation
Teaching and Learning Strategy
This unit is offered in weekday mode, with a combination of weekly pre-recorded lecture (2-hour x 13 weeks) and tutorials (1-hour x 12 weeks)
Students are expected to be active and engaged learners, contributing fully unit activities and topic discussions
Learning activities include individual and group tasks that are to be completed during private study and in tutorials
Scheduled Learning Activities
Students are expected to actively participate in this unit and in classes, and to be prepared to work in small groups during tutorials
Non-Scheduled Learning Activities
Students are expected to read all learning materials provided in preparation for the lectures and tutorials (12 hours), complete all assigned readings (25 hours), and conduct research for the assessment tasks (25 hours)
Prescribed Textbook
There is no prescribed textbook for this unit
Recommended Texts
Sterne, J. (2017). Artificial intelligence for marketing: practical applications. John Wiley & Sons.
Recommended Journal Articles
Balducci, B., & Marinova, D. (2018). Unstructured data in marketing. Journal of the Academy of Marketing Science, 46(4), 557-590. doi: https://doi-org.simsrad.net.ocs.mq.edu.au/10.1007/s11747-018-0581-x
Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24-42. doi: https://doi.org/10.1007/s11747-019-00696-0
Grewal, D., Hulland, J., Kopalle, P. K., & Karahanna, E. (2020). The future of technology and marketing: a multidisciplinary perspective. Journal of the Academy of Marketing Science, 48, 1-8. doi: https://doi.org/10.1007/s11747-019-00711-4
Grewal, D., Noble, S. M., Roggeveen, A. L., & Nordfalt, J. (2020). The future of in-store technology. Journal of the Academy of Marketing Science, 48(1), 96-113. doi: https://doi.org/10.1007/s11747-019-00697-z
Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155-172. doi: https://doi.org/10.1177/1094670517752459
Huang, M. H., & Rust, R. T. (2020). Engaged to a Robot? The Role of AI in Service. Journal of Service Research. doi: https://doi.org/10.1177/1094670520902266
Thomaz, F., Salge, C., Karahanna, E., & Hulland, J. (2020). Learning from the Dark Web: leveraging conversational agents in the era of hyper-privacy to enhance marketing. Journal of the Academy of Marketing Science, 48(1), 43-63. doi: https://doi.org/10.1007/s11747-019-00704-3
Recommended Learning Resources
Macquarie University policies and procedures are accessible from Policy Central (https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/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 the Student Policy Gateway (https://students.mq.edu.au/support/study/student-policy-gateway). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.
If you would like to see all the policies relevant to Learning and Teaching visit Policy Central (https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/policy-central).
Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/study/getting-started/student-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 ask.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au
Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
Learning Skills (mq.edu.au/learningskills) 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.
Students with a disability are encouraged to contact the Disability Service who can provide appropriate help with any issues that arise during their studies.
For all student enquiries, visit Student Connect at ask.mq.edu.au
If you are a Global MBA student contact globalmba.support@mq.edu.au
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.
MKTG2006 Marketing Technologies is a new unit.