MBA621 - Digital Marketing and Analytics

Credit Points: 20 credit points

Workload: 60 hours

Prerequisite: MBA512 Business Data Analysis, MDA513 ICT Practices

Co-requisite: N/A

Aims & Objectives

This is a core unit out of a total of 12 units in the Master of Business Analytics (MBAnalytics). This unit addresses the course learning outcomes and complements other courses in a related field by developing students’ specialised knowledge of key technologies in ICT and data analytics that are applied in the emerging field of digital marketing and analytics. For further course information refer to: This unit is part of the AQF level 9 course.

 This unit offers students an overview of key ICT technologies and online analytical tools that drive the collection of the right data and information to perform the right analysis for improved online marketing plans and decisions. It introduces emerging digital technologies that has profound impacts on the design and evolution of online digital marketplace. The unit also discusses digital marketing strategies, particularly new communication channels such as social networks and the macro technological forces including cybersecurity, blockchain, and digital currencies. It also discusses how consumers are changing their information searches and consumption behaviours to fit with these emerging technologies and trends. Most importantly, it shows how new online digital tools are being exploited by marketers to their advantage by connecting with consumers, gathering more information about their target markets, and converting this information into successful online marketing campaigns. It is expected that through a major group project students can demonstrate their knowledge and skills in digital technologies and business analytics to come up with their solution to a non-trivial digital marketing case.

  • This unit will cover the following topics:
  • Digital Transformation of Marketing
  • Search Engines and the Internet
  • Technological forces I: Cybersecurity
  • Technological forces II: Blockchain and Digital Currencies
  • Digital marketing strategy development
  • Online branding and the marketing mix
  • Data-driven customer relationship marketing
  • Digital user experience design
  • Push and Pull Technologies for Digital Campaigns
  • Digital media communications channels
  • Performance evaluation using web analytics

Learning Outcomes

Course Learning Outcomes

The Course learning outcomes applicable to this unit are listed on the Melbourne Institute of Technology’s website:

 Unit Learning Outcomes

At the completion of this unit students should be able to:

  • Describe the opportunities and challenges of online businesses, and explain the technologies that enable secured and traceable digital marketing including cybersecurity, blockchain, and digital currencies.
  • Apply ICT and business analytics knowledge to develop an integrated digital marketing strategy for an organisation.
  • Evaluate marketing strategies by comparing traditional and emerging approaches that exploit digital media, data, and platform technologies.
  • Plan and manage digital marketing strategy, including website and app development, online communications, and performance evaluation using web analytics to ensure quality user experience.
  • Explore variations of online marketing mix and assess strategies to enhance digital customer relationship management.

This unit will cover the content below:

Week Topic
1 Digital Transformation of Marketing
2 Search Engines and the Internet
3 Technological forces: Cybersecurity
4 Technological forces: Blockchain and digital currencies
5 Digital marketing strategy development
6 Online branding and marketing mix
7 Data-driven customer relationship marketing
8 Digital user experience design
9 Push and Pull Technologies for Digital Campaigns
10 Digital media communications channels
11 Performance evaluation using web analytics
12 Review and Future Trends


Assessment Task Due Date Release Date A B Learning Outcomes Assessed
In-class test (Application of Wks 1-5 topics) Week 6 -   10% a-b
Assignment 1 (Individual) Week 4 Week 1 10%   a
Assignment 2 (Group) Week 11 Week 7 25%   b-d
Laboratory and Problem Based Learning participation & submission Week 2-11 Week 2-11 10%   a-e
Final Examination (3 hours)       45% a-c
TOTALS     45% 55%  

Task Type: Type A: unsupervised, Type B: supervised.


  • Assignment 1 asks students to discuss how enabling ICT technologies, including cybersecurity, blockchain, and digital currencies have been applied to address the challenges and opportunities of digital marketing. Students are required to reference real-world examples in their analysis and reflection. An essay of a maximum of 1,500 words will be submitted.
  • Assignment 2 is a group project where students apply their integrated knowledge in data analytics and coding in Python/R and their ecosystems, complemented by industry-standard applications like Tableau/MS PowerBI, to design a data-driven marketing campaign for a simulated company. Given a large dataset of past sales of the simulated company, each group is required to detail their analysis of the dataset, submit a proposal of their analytics plan, and implement the analytics plan to form the basis of their digital marketing plan. The focus is on a systems approach where analytics form a part of the whole system that delivers the marketing plan. Each group is required to present their marketing plan, supported by data visualisation and storytelling.

Contribution and Participation (in class) (5%)
Students are expected to attend each scheduled session, arrive on time and remain for the entire session. Adherence to this requirement will be reflected in the marks awarded for this assessment. Students are also strongly encouraged to actively participate in the class discussions and tutorial activities aligned with the course learning outcomes and the unit learning outcomes by answering questions, and expressing their opinions, insights and their learnings from the course.

Presentations (if applicable)
For presentations conducted in class, students are required to wear business attire.

Textbook and Reference Materials


  • Dave Chaffey and Fiona Ellis-Chadwick. Digital Marketing: Strategy, implementation and practice, 8th edition, Hoboken, NJ : Pearson, 2022.

Recommended Readings:

  • Marshall Sponder and Gohar F. Khan. Digital analytics for marketing. Routledge, 2018.
  • Janet Driscoll Miller and Julia Lim. Data-First Marketing: Data-Driven Marketing in the Age of Analytics. Wiley, 2020.
  • Brian Vellmure and Wilson Raj. Leverage Marketing Analytics to Improve Customer Experience, Insights from a presentation made to the Customer Experience Professionals Association (CXPA), SAS Institute Inc. World Headquarters, 2012
  • Gary L Lilien., Arvind Rangaswamy., and Arnaud De Bruyn. Principles of Marketing Engineering and Analytics, Decisionpro Inc., State College, PA 16803, 2017.
  • Mckinsey and Company. Marketing and Sales: Big Data Analytics and the Future of Marketing and Sales, 2015
  • Michael Kim, Agil Francis, Pushpamitra Khuntia and Vishal Shukla. Marketing Analytics:  A Smarter Way for Auto and Home Insurers to Gain Competitive Advantage. Research Paper on Future of Work, Cognizant, 2018
  • Michel Wedel and P.K. Kannan. Marketing Analytics for Data-Rich Environments. Journal of Marketing; AMA/MSI Issue, Vol.80, American Marketing Association, 2016. 
  • Mike Grigsby. Marketing Analytics: A Practical Guide to Improving consumer insights using data techniques, KoganPage, 2018
  • Peter Krensky. Marketing Analytic: The ROI of Insight and Advantage of the Cloud, Research Brief, Aberdeen Group, 2015
  • Sean Kelley and Carey Chou. Marketing Analytics in Practice: How to Realise Business Value of Analytics, Kodak Infosys, 2011
  • Stephen L. Francea and Sanjoy Ghoseb. Marketing Analytics: Methods, Practice, Implementation, and Links to Other Fields, Expert Systems with Applications, 2018
  • T. Evgeniou and J. Niessing. Data Analytics: A Marketing Segmentation Case Study, Insead Business School, 2014
  • Trip Kucera and David White. Predictive Analytics for Sales and Marketing, Research Brief, Aberdeen Group, 2012

Graduate Attributes

MIT is committed to ensure the course is current, practical and relevant so that graduates are “work ready” and equipped for life-long learning. In order to accomplish this, the MIT Graduate Attributes identify the required knowledge, skills and attributes that prepare students for the industry.
The level to which Graduate Attributes covered in this unit are as follows:

Ability to communicate Independent and Lifelong Learning Ethics Analytical and Problem Solving Cultural and Global Awareness Team work Specialist knowledge of a field of study


Levels of attainment Extent covered
The attribute is covered by theory and practice, and addressed by assessed activities in which the students always play an active role, e.g. workshops, lab submissions, assignments, demonstrations, tests, examinations.
The attribute is covered by theory or practice, and addressed by assessed activities in which the students mostly play an active role, e.g. discussions, reading, intepreting documents, tests, examinations.
The attribute is discussed in theory or practice; it is addressed by assessed activities in which the students may play an active role, e.g. lectures and discussions, reading, interpretation, workshops, presentations.
The attribute is presented as a side issue in theory or practice; it is not specifically assessed, but it is addressed by activities such as lectures or tutorials.
The attribute is not considered, there is no theory or practice or activities associated with this attribute.