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Marketing Analytics (MKTG90039)
Graduate courseworkPoints: 12.5On Campus (Parkville)
For information about the University’s phased return to campus and in-person activity in Winter and Semester 2, please refer to the on-campus subjects page.
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
February
A/Prof Stephan Ludwig stephan.ludwig@unimelb.edu.au
Please refer to the LMS for up-to-date subject information, including assessment and participation requirements, for subjects being offered in 2020.
Overview
Availability | February |
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Fees | Look up fees |
It has become increasingly important to know how marketing actions translate into revenue and profit growth. Within the big data phenomenon, new ways of analysing data (e.g. text mining), running social media experiments and gleaning insights on customers’ digital behaviours have taken centre stage to inform business decision making. Simply put, market research, the methods that surround it, and the inferences derived from it have put marketing “on the map.” Although these methods are here to stay, as big data becomes mainstream, it is fundamentally altering the way we collect and analyse data to demonstrate ROI.
“Marketing Analytics” does not teach how to do marketing. Instead, Marketing Analytics is a purely data-driven subject, which explores the new ways to harness digital customer and market data and enhance marketing decision-making. In this subject, students will (i) learn reflective and predictive marketing analytics, (ii) learn how to choose which approach to use to uncover what type of market insight, (iii) apply new learnings hands-on to real-life datasets (iv) and draw managerial implications on ROI, customer satisfaction, and virality.
Intended learning outcomes
On completion of this subject, students should be able to:
- Understand how to derive insights from new secondary data (e.g. text mining).
- Use predictive analytics (e.g., online field experiments) to test the viability of different marketing actions.
- Understand how to predict the impact of marketing actions using count-, choice- at multi-level models.
- Segment markets of customers using a variety of segmentation methods and choose segments to target using a set of criteria.
- Conduct market structure analysis by mapping customers' perceptions of brands and products in a market, and translate the maps into different positioning choices.
- Model the impact of alternative marketing mix combinations on sales, accounting for moderational shifts and interactions to optimise the mix.
Generic skills
- High level of development: problem solving; statistical reasoning; application of theory to practice; interpretation and analysis; synthesis of data and other information; evaluation of data and other information; use of computer software; accessing data and other information from a range of sources.
- Moderate level of development: written communication; critical thinking; receptiveness to alternative ideas.
- Moderate level of development: collaborative learning; team work.
Last updated: 3 November 2022
Eligibility and requirements
Prerequisites
Code
Name
Teaching period
Credit Points
BUSA90060
Data Analysis
12.5
BUSA90243
Marketing
12.5
and
70% minimum grade required in each prerequisite subject
Corequisites
None
Non-allowed subjects
None
Inherent requirements (core participation requirements)
Melbourne Business School welcomes applications from eligible students for a variety of graduate degrees offered by its programme portfolio. These degrees require following attributes for academic study:
• The ability to explain and evaluate concepts, theories, and business operations of organisations
• Ability to use analytic techniques to solve business problems
Melbourne Business School welcomes applications from students with disabilities and takes reasonable steps to implement adjustments to provide equal participation opportunities for students with disability.
Last updated: 3 November 2022
Assessment
Due to the impact of COVID-19, assessment may differ from that published in the Handbook. Students are reminded to check the subject assessment requirements published in the subject outline on the LMS
Description | Timing | Percentage |
---|---|---|
Final examination
| End of term | 50% |
Syndicate presentation: Each syndicate has 5 – 6 syndicate members and students are assessed as a group.
| Week 6 | 30% |
Syndicate assignment. Each syndicate has 5 – 6 syndicate members and students are assessed as a group.
| Week 8 | 20% |
Last updated: 3 November 2022
Dates & times
- February
Principal coordinator Stephan Ludwig Mode of delivery On Campus (Parkville) Contact hours Total time commitment 170 hours Pre teaching start date 16 February 2020 Pre teaching requirements students are required to complete approximately 15 hours of reading to prepare for the subject during pre-teaching period Teaching period 23 February 2020 to 22 March 2020 Last self-enrol date 17 February 2020 Census date 28 February 2020 Last date to withdraw without fail 13 March 2020 Assessment period ends 25 March 2020 February contact information
A/Prof Stephan Ludwig stephan.ludwig@unimelb.edu.au
Last updated: 3 November 2022
Further information
- Texts
Prescribed texts
There are no specifically prescribed or recommended texts for this subject.
Last updated: 3 November 2022