Marketing Insights into Big Data unit (BMA708)

Melbourne Study Centre, Ultimo Study Centre, Hobart, Online

Introduction

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.

Summary

Unit name	Marketing Insights into Big Data
Unit code	BMA708
Credit points	12.5
College/School	Tasmanian School of Business and Economics
Discipline	Marketing
Coordinator	Doctor Denni Arli
Delivered By	University of Tasmania and Third Party(ies): ECA
Level	Postgraduate

Availability

Location	Study period	Attendance options		Available to
Melbourne Study Centre	Semester 2
Ultimo Study Centre	Semester 2
Hobart	Semester 2
Online	Semester 2

Note

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Key Dates

Study Period	Start date	Census date	WW date	End date
Semester 2	5/7/2026	27/7/2026	30/8/2026	24/10/2026

* 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 2026 are indicative and subject to change. Finalised census dates for 2026 will be available from the 1st October 2025. Note census date cutoff is 11.59pm AEST (AEDT during October to March).

About Census Dates

Learning Outcomes

• Analyse big data using a range of statistical techniques.
• Create effective data visualisations to identify meaningful relationships.
• Communicate actionable marketing insights derived from data analysis

Fee Information

Field of Education	Commencing Student Contribution 1,3	Grandfathered Student Contribution 1,3	Approved Pathway Course Student Contribution 2,3	Domestic Full Fee 4
080505	$2,174.00	$1,703.00	not applicable	$3,284.00

• Available as a Commonwealth Supported Place
• HECS-HELP is available on this unit, depending on your eligibility³
• FEE-HELP is available on this unit, depending on your eligibility⁴

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.

If you have any questions in relation to the fees, please contact UniConnect or more information is available on StudyAssist.

Please note: international students should refer to What is an indicative Fee? to get an indicative course cost.

Teaching

Teaching Pattern	Workshops will be delivered weekly to support student learning and engagement across both delivery modes. In-person workshops will be conducted on campus.  Online workshops will be delivered  via Zoom.
Assessment	Big Data Reporting using T-test, Correlation and Regression Analysis (30%)|Data Visualisation (35%)|Descriptive Statistics Analysis. (35%)
Timetable	View the lecture timetable | View the full unit timetable

Textbooks

Required	Required readings will be listed in the unit outline prior to the start of classes.

The University reserves the right to amend or remove courses and unit availabilities, as appropriate.