BAN221 - Digital Marketing and Social Media Analytics

Credit Points: 15 credit points

Workload: 36 hours

Prerequisite: BAN211 Business Analytics; BDA112 Data Science Fundamentals

Co-requisite: N/A

Aims & Objectives

This is a second-year Core Unit in the Bachelor of Business, Major in Digital Marketing and Social Media Analytics, and offered as a recommended Elective Unit from another specialisation stream in the Bachelor of Business, subject to meeting pre- and co-requisites. For Course Learning Outcomes and further information relating to Bachelor of Business programs, please visit our website: http://www.mit.edu.au/study-with-us/programs/bachelor-business.  

Digital and social media marketing are at the heart of how many entrepreneurs and businesses connect with their customers. This unit provides students with the analytical tools (e.g., DALL-E 2) needed to measure digital activity and implement best practices for marketing strategy. Students will learn how to integrate large amounts of data from web, digital, social, and search platforms and extract valuable information to make effective marketing decisions. Students also will have an opportunity to learn to communicate the business value of social media data clearly and effectively and to develop the skills to convert these data into marketing insights. 

Unit topics include: 

  • The Evolution of Digital Marketing, Analytics and The Internet  
  • Search Engines & The Internet  
  • Basic and Advanced Web Analytics and Web Intelligence  
  • Understanding and Working with Third-Party Data 
  • Ethics of Digital Marketing: Navigating Privacy and Data Security 
  • Social Media and Marketing Analytics  
  • Leveraging Social Media Content and Analytics 
  • Advanced Text Analytics & Algorithms 
  • Social Media Actions and Hyperlink Analytics 
  • Aligning Digital Media with Business Strategy   

Learning Outcomes

  1. Course learning outcomes 

  • The Course learning outcomes applicable to this unit are listed on the Melbourne Institute of Technology's website: www.mit.edu.au   

  1. Unit learning outcomes  

  • Understand and apply the core principles of and approaches to data analytics in the context of social media and digital marketing.  
  • Demonstrate ethical considerations of integrating large amounts of customer data from web, digital, social, and search platforms. 
  • Apply quantitative methods and techniques to collect, analyse and extract insights and take effective actions. 
  • Explain the role of data analytics for decision-making in social media and digital marketing. 
  • Develop skills to ensure privacy and security of Big data. 

Assessment

Assessment Task Due Date A B Learning Outcomes Assessed 
1. Assessment 1: (Individual)   Ethical/unethical ads Week 3 - 10%  a
2. Assessment 2: Digital Data Analysis Report (individual)   Week 8  30%  - a-d 
3. Assessment 3: Contribution and Participation  Week 12  - 10% a-e 
4. Assessment 4: (Group of 2-3 students) Project Report (3,000 words) (30%) and Presentation (20%) Week 12  30%  20% a-e 
TOTALS   60%  40%   

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

Contribution and Participation (10%)

This unit has class participation as an assessment. The assessment task and marking rubric will follow the Guidelines on Assessing Class Participation (https://www.mit.edu.au/about-us/governance/institute-rules- policies-and-plans/policies-procedures-and-guidelines/Guidelines_on_Assessing_Class_Participation). 

Further details on the type of assessment tasks and the marking rubrics will be provided in the assessment specification. 

Textbook and Reference Materials

Note: Students are required to purchase the prescribed textbook and have it available each week in class. 

Prescribed Textbooks

  • Feroz, A. K, Khan, G.  and Sponder, M. (2024), Digital Analytics for Marketing (Mastering Business Analytics) 2nd Edition, Routledge, January 25, 2024. 

Recommended Texts

  • Hanlon, A (2022), Digital Marketing: Strategic Planning & Integration,  2nd edition, Sage Publications Ltd, Feb. 2022. 
  • Hemann, C and Burbary, K (2018), Digital marketing analytics: making sense of consumer data in a digital world, Pearson Education 2018. 
  • Miller, W (2015), Marketing data science: modelling techniques in predictive analytics with R and Python, Pearson Education, 2015. 

The Referencing style for this unit is APA:  

See the MIT Library Referencing webpage: https://library.mit.edu.au/referencing/APA and the Unit Moodle page for additional referencing support material and web links. 

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

Legend

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.