Marketing decision-making is growing in importance in the business world. More than ever before, organisations are placing greater emphasis on the marketers' ability to evaluate, anticipate, and illustrate the contribution of marketing to organisational performance. Increasingly, senior managers are requiring greater rigour and accountability for investments in marketing activities. Within marketing, there is a realisation that practitioners need to be able to justify their strategies, tactics and the associated outcomes, using relevant metrics. Marketing analytics seeks to build a link between the marketing activity of the organisation and the outcomes that result from it. The focus of this unit is on developing, analysing, and evaluating appropriate models to measure the performance of marketing activities. It will develop students' knowledge of key strategic and technical decision-making models and metrics that form the foundation of marketing analytics. Students will gain knowledge and skills to predict the outcome of marketing plans in order to boost return on marketing investment.
|Unit name||Marketing Insights into Big Data|
|College/School||College of Business & Economics
Tasmanian School of Business and Economics
|Coordinator||Doctor Denni Arli|
|Delivered By||University of Tasmania|
|Location||Study period||Attendance options||Available to|
|ECA Melbourne||Semester 2||On-Campus||International|
- International students
- Domestic students
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|Study Period||Start date||Census date||WW date||End date|
* The Final WW Date is the final date from which you can withdraw from the unit without academic penalty, however you will still incur a financial liability (refer to How do I withdraw from a unit? for more information).
Unit census dates currently displaying for 2024 are indicative and subject to change. Finalised census dates for 2024 will be available from the 1st October 2023. Note census date cutoff is 11.59pm AEST (AEDT during October to March).
- Critically examine different methods of data analysis and presentation for social networks, complex systems and relational links.
- Apply intermediate skills in spreadsheets and data visualisation software to identify trends and relationships among factors in industry and society.
- Analyse government, industry and social media data to identify relationships and trends.
- Critically evaluate conclusions drawn from different data and analytic tools.
- Create interactive models using appropriate software and effectively communicate results and findings to aid decision-makers in understanding interrelationships and trends.
|Field of Education||Commencing Student Contribution 1,3||Grandfathered Student Contribution 1,3||Approved Pathway Course Student Contribution 2,3||Domestic Full Fee 4|
1 Please refer to more information on student contribution amounts.
2 Please refer to more information on eligibility and Approved Pathway courses.
3 Please refer to more information on eligibility for HECS-HELP.
4 Please refer to more information on eligibility for FEE-HELP.
Please note: international students should refer to What is an indicative Fee? to get an indicative course cost.
Introduction Lecture: 1hr, once in a term
Weekly recorded lecture: 1hr, 13 weeks
Workshop: 3hrs, 4 times in a semester
|Assessment||Big Data Reporting using Correlation Analysis (30%)|Data Visualisation (35%)|Descriptive Statistics Analysis. (35%)|
|Timetable||View the lecture timetable | View the full unit timetable|
Required readings will be listed in the unit outline prior to the start of classes.
|Links||Booktopia textbook finder|
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