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Marketing Analytics (MKTG90039)
Graduate courseworkPoints: 12.5On Campus (Parkville)
Please refer to the return to campus page for more information on these delivery modes and students who can enrol in each mode based on their location.
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Overview
Availability | April |
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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
70% minimum grade required in each prerequisite subject below:
All of
Code | Name | Teaching period | Credit Points |
---|---|---|---|
BUSA90060 | Data Analysis |
July (On Campus - Parkville)
January (Dual-Delivery - Parkville)
September (On Campus - Parkville)
April (Dual-Delivery - Parkville)
|
12.5 |
BUSA90243 | Marketing |
January (Dual-Delivery - Parkville)
July (On Campus - Parkville)
April (Dual-Delivery - Parkville)
September (On Campus - Parkville)
|
12.5 |
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
Description | Timing | Percentage |
---|---|---|
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% |
Final examination
| End of term | 50% |
Last updated: 3 November 2022
Dates & times
- April
Mode of delivery On Campus (Parkville) Contact hours Total time commitment 170 hours Pre teaching start date 5 April 2021 Pre teaching requirements students are required to complete approximately 15 hours of reading to prepare for the subject during pre-teaching period Teaching period 12 April 2021 to 18 June 2021 Last self-enrol date 6 April 2021 Census date 23 April 2021 Last date to withdraw without fail 28 May 2021 Assessment period ends 25 June 2021
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