MARK5828 Advertising Analytics - 2019

MARK5828
Postgraduate
Term 1
6 Units of Credit
On Campus
Marketing
The course outline is not available for current semester. To view outlines from other years and/or semesters, visit the archives .

1. Course Details

Summary of Course

Advertising is one of marketing's key communication activities. In today’s data-rich environment, big data analytics, and information technology have increased the effectiveness of advertising dramatically.

This course will cover new technologies to (1) reach the customers being targeted, (2) measure the effect of ad, and (3) improve the delivery of ad content. It will also illustrate how the advance of data analytics makes it possible to analyse complex types (i.e. picture ad, video ad) of ad content using text, speech, picture, and video analytics.

Students will exercise hands-on data analytics and then tackle real-world advertising problems. No prior knowledge of Python is needed because this course will go through Python step-by-step.

Teaching Times and Locations

Please note that teaching times and locations are subject to change. Students are strongly advised to refer to the Class Timetable website for the most up-to-date teaching times and locations.

View course timetable

Course Policies & Support

Course Aims and Relationship to Other Courses

This course is offered as part of the Marketing stream in the MCom degree. MARK 5828 introduces current advertising technology, innovative approach to measure ad effectiveness, and cutting-edge data analytics tools to analyse a variety of ad content.

The aim is to produce advertising data analytics experts who can work as advertising manager, social media manager, or advertising analytics consultants.

MARK 5810 (Marketing Communication and Promotion) offers theory of marketing communication and its implementation. MARK 5822 (Marketing Analytics) covers overall marketing analytics. The proposed course MARK 5828 focuses on Advertising and ad-related technology and content analytics. MARK 5826 Product Analytics talks about AI-based data product development, and MARK 5827 Customer Analytics introduces data analytics for customer acquisition and retention.

2. Staff Contact Details

Position Title Name Email Location Phone Consultation Times
Lecturer-in-chargeDrJun Bum Kwon3031B, Level 3, Quadrangle Building+61 (2) 9385 9708Wed. 2 ~ 3 pm

​​Communication with staff

Email: email is the preferred contact method.

TAs: TA contact information will be posted on the Moodle course website.

Staff will be available for consultation at the specified consultation times – NO appointment needs to be made if you wish to see your lecturer or tutor at the consultation times.

3. Learning and Teaching Activities

Approach to Learning and Teaching in the Course

This course aims to deliver “how to use data analytics and new advertising technology” to solve business problems about ad content analysis. To achieve this goal, students will learn both soft and hard skills. To give students soft skills, this course will introduce many different types of ad content (i.e. picture and video) and of outcomes (i.e. consumer search, newspaper article) to measure ad effectiveness. Furthermore, this course emphasizes the importance of data communication using visualization tools. This data communication efforts help students to understand big picture, stimulate discussion with team members, come up with plausible hypothesis, and delivery the business value of their data analytics results.

Next is hard skills. This course will introduce how to analyze ad content using computer vision cloud (e.g. Google Vision, Microsoft Indexer) in addition to Python libraries. Students will have much opportunity to exercise those analytics for lab quizzes, assignments, group project, and individual research project. Step-by-step guideline for data analytics is given.


Learning Activities and Teaching Strategies

Teaching in this course will be via lectures, tutorials and computer labs, group discussion and individual research.

• Lectures: The lectures will introduce new AI-based advertising content analysis and emerging advertising technology. Each lecture shows best practices, tools, leading companies and challenges. Then, lectures mainly focus on hands-on data analytics using Python programming languages to tackle marketing problems.

• Tutorials and computer labs: “Those who can’t come up with the solutions of lab quizzes by analyzing data using Python program do not understand course content fully.” In these tutorials and computer labs, lecturer and TA will help students accomplish this mission by offering detailed guideline and continuous advice on the spot. Every student should go home with great confidence.

• Teamwork: Students will need to actively engage with their team members with different background during entire semester. Within team, someone may be good at business insight while others have deep quantitative or programming background. Each one will contribute more at different stage of semester-long project. Students will also realize that continuous discussion with colleagues from different backgrounds will bring several creative approaches, although those ideas do not simply result in solution. In fact, such failures are even encouraged. This is common in today’s data science world. Great teams will discuss again to figure out where things have been wrong by checking programming code again, visualizing data differently, and even changing the definition of the problem. If your teams reach this stage, this would be the most important lesson which you can take out from this course.

• Individual research: While students tackle group projects by collaborating with group members, each student also do individual research. Example topics will be shown. This individual research will give the opportunity for students to test advertising research hypothesis by tacking problems for themselves. For this, students need to digest course materials and lab quizzes and learn from group members so that they can have independent ability to do data analytics.


5. Course Resources

  • Python Software: Detailed guideline will be given during Tutorials
    • https://www.lynda.com/Python-training-tutorials/
    • https://www.kaggle.com/learn/python
    • https://www.datacamp.com/courses/intro-to-python-for-data-science

6. Course Evaluation & Development

Feedback is regularly sought from students and continual improvements are made based on this feedback. At the end of this course, you will be asked to complete the myExperience survey, which provides a key source of student evaluative feedback. Your input into this quality enhancement process is extremely valuable in assisting us to meet the needs of our students and provide an effective and enriching learning experience. The results of all surveys are carefully considered and do lead to action towards enhancing educational quality.

​Each year feedback is sought from students and other stakeholders about the courses offered in the School and continual improvements are made based on this feedback. UNSW's myExperience survey is one of the ways in which student evaluative feedback is gathered. In this course, we will seek your feedback through the end of semester myExperience responses.

If at any time you have any concerns about your progress or any aspects of the course, please feel free to contact me to discuss your concerns.

7. Course Schedule

Note: for more information on the UNSW academic calendar and key dates including study period, exam, supplementary exam and result release, please visit: https://student.unsw.edu.au/new-calendar-dates
Week Activity Topic Assessment/Other
Week 1: 18th of FebruaryLecture

Course Overview

Picture Ad Content Analysis

Picture API: Google, Microsoft

Tutorial / Lab

Instagram Picture Ad Analysis

Python Function and Library

Python code to access Picture API

Lab Quiz 1

Week 2: 25th of FebruaryLecture

Video Ad Content Analysis

 

Important Ad content Selection

Tutorial / Lab

YouTube Video Ad Analysis

- Video Analysis API: Microsoft

- Variable Selection Regression using Python

Lab Quiz 2

Week 3: 4th of MarchLecture

Measure Ad Effect using Consumer Search Data

- Samsung Galaxy's Comparative Ad against Apple iPhone

- Collecting Google Search Data from Google Trend

- Visualizing Brand Positioning Map

- Difference-in-Difference Regression

Tutorial / Lab

Python code

- to Access Google Trend

- for Multi-Dimensional Scaling

Visualization using Microsoft Power BI

Lab Quiz 3

Group Project: Topic Selection (Due: Wed 10 pm on 6 March) & Feedback during Tutorial

Week 4: 11th of MarchLecture

Measure Ad Effect using Mass Media (i.e. Newspaper)

- Keyword level Analysis

- Unsupervised Clustering (i.e. Topic Model)

- Supervised Classification with Human Labels

Tutorial / Lab

Python code for

- Topic Model

- Classification

Lab Quiz 4

Individual Research Project: Topic Selection (Due: Wed 10 pm on 13 March) & Feedback during Tutorial

Week 5: 18th of MarchLecture

Ad Content Analysis using Body Data

- Eye-tracking, Facial Emotion, Brain

- The Effective Location of Ad message in Video Ad using Eye-tracking

Presentation

Progress Presentation for Group Project

Presentation & Feedback during Tutorial

(Video record Due: Wed 10 pm on 20 March & Peer feedback before tutorial)

Week 6: 25th of MarchLecture

Measure Multi-Channel Ad Effect

Ad Attribution

Tutorial / Lab

Data Extraction from Big Database System

Ad Attribution Model using Python

 

Lab Quiz 5

Assignment Due (Wed 10 pm on 27 March)

Week 7: 1st of AprilPresentation

Final Presentation for Group Project

 

Presentation (during lecture and tutorial)

Final Report (Tuesday 5 PM on 9 April)

Week 8: 8th of AprilLecture

Emerging Ad Technology

- AR/VR and Blockchain for Ad

- Automatic Media Buy

- AI-based Ad Content Editing

Tutorial / Lab

Progress Presentation for Individual Research Project

Presentation & Feedback during Tutorial

(Video record Due: Thursday 5 pm on 11 April & Peer feedback before tutorial)

Week 9: 15th of AprilGood Friday Holiday
Week 10: 22nd of AprilPresentation

Final presentation for Individual Research Project

Presentation & Feedback (during lecture and tutorial)

Final Report (Wed 10 PM on 1 May)

8. Policies and Support

Information about UNSW Business School protocols, University policies, student responsibilities and education quality and support.

Program Learning Outcomes

The Business School places knowledge and capabilities at the core of its curriculum via seven Program Learning Outcomes (PLOs). These PLOs are systematically embedded and developed across the duration of all coursework programs in the Business School.

PLOs embody the knowledge, skills and capabilities that are taught, practised and assessed within each Business School program. They articulate what you should know and be able to do upon successful completion of your degree.

Upon graduation, you should have a high level of specialised business knowledge and capacity for responsible business thinking, underpinned by ethical professional practice. You should be able to harness, manage and communicate business information effectively and work collaboratively with others. You should be an experienced problem-solver and critical thinker, with a global perspective, cultural competence and the potential for innovative leadership.

All UNSW programs and courses are designed to assess the attainment of program and/or course level learning outcomes, as required by the UNSW Assessment Design Procedure. It is important that you become familiar with the Business School PLOs, as they constitute the framework which informs and shapes the components and assessments of the courses within your program of study.

PLO 1: Business knowledge

Students will make informed and effective selection and application of knowledge in a discipline or profession, in the contexts of local and global business.

PLO 2: Problem solving

Students will define and address business problems, and propose effective evidence-based solutions, through the application of rigorous analysis and critical thinking.

PLO 3: Business communication

Students will harness, manage and communicate business information effectively using multiple forms of communication across different channels.

PLO 4: Teamwork

Students will interact and collaborate effectively with others to achieve a common business purpose or fulfil a common business project, and reflect critically on the process and the outcomes.

PLO 5: Responsible business practice

Students will develop and be committed to responsible business thinking and approaches, which are underpinned by ethical professional practice and sustainability considerations.

PLO 6: Global and cultural competence

Students will be aware of business systems in the wider world and actively committed to recognise and respect the cultural norms, beliefs and values of others, and will apply this knowledge to interact, communicate and work effectively in diverse environments.

PLO 7: Leadership development

Students will develop the capacity to take initiative, encourage forward thinking and bring about innovation, while effectively influencing others to achieve desired results.

These PLOs relate to undergraduate and postgraduate coursework programs.  Separate PLOs for honours and postgraduate research programs are included under 'Related Documents'.

Business School course outlines provide detailed information for students on how the course learning outcomes, learning activities, and assessment/s contribute to the development of Program Learning Outcomes.

RELATED DOCUMENTS

 

UNSW Graduate Capabilities

The Business School PLOs also incorporate UNSW graduate capabilities, a set of generic abilities and skills that all students are expected to achieve by graduation. These capabilities articulate the University’s institutional values, as well as future employer expectations.

UNSW Graduate CapabilitiesBusiness School PLOs
Scholars capable of independent and collaborative enquiry, rigorous in their analysis, critique and reflection, and able to innovate by applying their knowledge and skills to the solution of novel as well as routine problems.
  • PLO 1: Business knowledge
  • PLO 2: Problem solving
  • PLO 3: Business communication
  • PLO 4: Teamwork
  • PLO 7: Leadership development

Entrepreneurial leaders capable of initiating and embracing innovation and change, as well as engaging and enabling others to contribute to change
  • PLO 1: Business knowledge
  • PLO 2: Problem solving
  • PLO 3: Business communication
  • PLO 4: Teamwork
  • PLO 6: Global and cultural competence
  • PLO 7: Leadership development

Professionals capable of ethical, self-directed practice and independent lifelong learning
  • PLO 1: Business knowledge
  • PLO 2: Problem solving
  • PLO 3: Business communication
  • PLO 5: Responsible business practice

Global citizens who are culturally adept and capable of respecting diversity and acting in a socially just and responsible way.
  • PLO 1: Business knowledge
  • PLO 2: Problem solving
  • PLO 3: Business communication
  • PLO 4: Teamwork
  • PLO 5: Responsible business practice
  • PLO 6: Global and cultural competence

While our programs are designed to provide coverage of all PLOs and graduate capabilities, they also provide you with a great deal of choice and flexibility.  The Business School strongly advises you to choose a range of courses that assist your development against the seven PLOs and four graduate capabilities, and to keep a record of your achievements as part of your portfolio. You can use a portfolio as evidence in employment applications as well as a reference for work or further study. For support with selecting your courses contact the UNSW Business School Student Centre.




Academic Integrity and Plagiarism

Academic Integrity is honest and responsible scholarship. This form of ethical scholarship is highly valued at UNSW. Terms like Academic Integrity, misconduct, referencing, conventions, plagiarism, academic practices, citations and evidence based learning are all considered basic concepts that successful university students understand. Learning how to communicate original ideas, refer sources, work independently, and report results accurately and honestly are skills that you will be able to carry beyond your studies.

The definition of academic misconduct is broad. It covers practices such as cheating, copying and using another person’s work without appropriate acknowledgement. Incidents of academic misconduct may have serious consequences for students.

Plagiarism

UNSW regards plagiarism as a form of academic misconduct. UNSW has very strict rules regarding plagiarism. Plagiarism at UNSW is using the words or ideas of others and passing them off as your own. All Schools in the Business School have a Student Ethics Officer who will investigate incidents of plagiarism and may result in a student’s name being placed on the Plagiarism and Student Misconduct Registers.

Below are examples of plagiarism including self-plagiarism:

Copying: Using the same or very similar words to the original text or idea without acknowledging the source or using quotation marks. This includes copying materials, ideas or concepts from a book, article, report or other written document, presentation, composition, artwork, design, drawing, circuitry, computer program or software, website, internet, other electronic resource, or another person's assignment, without appropriate acknowledgement of authorship.

Inappropriate Paraphrasing: Changing a few words and phrases while mostly retaining the original structure and/or progression of ideas of the original, and information without acknowledgement. This also applies in presentations where someone paraphrases another’s ideas or words without credit and to piecing together quotes and paraphrases into a new whole, without appropriate referencing.

Collusion: Presenting work as independent work when it has been produced in whole or part in collusion with other people. Collusion includes:

  • Students providing their work to another student before the due date, or for the purpose of them plagiarising at any time
  • Paying another person to perform an academic task and passing it off as your own
  • Stealing or acquiring another person’s academic work and copying it
  • Offering to complete another person’s work or seeking payment for completing academic work

Collusion should not be confused with academic collaboration (i.e., shared contribution towards a group task).

Inappropriate Citation: Citing sources which have not been read, without acknowledging the 'secondary' source from which knowledge of them has been obtained.

Self-Plagiarism: ‘Self-plagiarism’ occurs where an author republishes their own previously written work and presents it as new findings without referencing the earlier work, either in its entirety or partially. Self-plagiarism is also referred to as 'recycling', 'duplication', or 'multiple submissions of research findings' without disclosure. In the student context, self-plagiarism includes re-using parts of, or all of, a body of work that has already been submitted for assessment without proper citation.

To see if you understand plagiarism, do this short quiz: https://student.unsw.edu.au/plagiarism-quiz

Cheating

The University also regards cheating as a form of academic misconduct. Cheating is knowingly submitting the work of others as their own and includes contract cheating (work produced by an external agent or third party that is submitted under the pretences of being a student’s original piece of work). Cheating is not acceptable at UNSW.

If you need to revise or clarify any terms associated with academic integrity you should explore the 'Working with Academic Integrity' self-paced lessons available at: https://student.unsw.edu.au/aim.

For UNSW policies, penalties, and information to help you avoid plagiarism see: https://student.unsw.edu.au/plagiarism as well as the guidelines in the online ELISE tutorials for all new UNSW students: http://subjectguides.library.unsw.edu.au/elise. For information on student conduct see: https://student.unsw.edu.au/conduct.

For information on how to acknowledge your sources and reference correctly, see: https://student.unsw.edu.au/referencing. If you are unsure what referencing style to use in this course, you should ask the lecturer in charge.


Student Responsibilities and Conduct

Students are expected to be familiar with and adhere to university policies in relation to class attendance and general conduct and behaviour, including maintaining a safe, respectful environment; and to understand their obligations in relation to workload, assessment and keeping informed.

Information and policies on these topics can be found on the 'Managing your Program' website.

Workload

It is expected that you will spend at least ten to twelve hours per week studying for a course except for Summer Term courses which have a minimum weekly workload of twenty to twenty four hours. This time should be made up of reading, research, working on exercises and problems, online activities and attending classes. In periods where you need to complete assignments or prepare for examinations, the workload may be greater. Over-commitment has been a cause of failure for many students. You should take the required workload into account when planning how to balance study with employment and other activities.

We strongly encourage you to connect with your Moodle course websites in the first week of semester. Local and international research indicates that students who engage early and often with their course website are more likely to pass their course.

View more information on expected workload

Attendance

Your regular and punctual attendance at lectures and seminars or in online learning activities is expected in this course. The Business School reserves the right to refuse final assessment to those students who attend less than 80% of scheduled classes where attendance and participation is required as part of the learning process (e.g., tutorials, flipped classroom sessions, seminars, labs, etc.).

View more information on attendance

General Conduct and Behaviour

You are expected to conduct yourself with consideration and respect for the needs of your fellow students and teaching staff. Conduct which unduly disrupts or interferes with a class, such as ringing or talking on mobile phones, is not acceptable and students may be asked to leave the class.

View more information on student conduct

Health and Safety

UNSW Policy requires each person to work safely and responsibly, in order to avoid personal injury and to protect the safety of others.

View more information on Health and Safety

Keeping Informed

You should take note of all announcements made in lectures, tutorials or on the course web site. From time to time, the University will send important announcements to your university e-mail address without providing you with a paper copy. You will be deemed to have received this information. It is also your responsibility to keep the University informed of all changes to your contact details.



Student Support and Resources

The University and the Business School provide a wide range of support services and resources for students, including:

Business School EQS Consultation Program
The Consultation Program offers academic writing, literacy and numeracy consultations, study skills, exam preparation for Business students. Services include workshops, online resources, individual and group consultations.
Level 1, Room 1035, Quadrangle Building.
BUS.EQS.Consultations@unsw.edu.au
02 9385 4508

Business School Student Centre
The Business School Student Centre provides advice and direction on all aspects of admission, enrolment and graduation.
Level 1, Room 1028 in the Quadrangle Building
02 9385 3189

UNSW Learning Centre
The UNSW Learning Centre provides academic skills support services, including workshops and resources, for all UNSW students. See their website for details.
Lower Ground Floor, North Wing Chancellery Building.
learningcentre@unsw.edu.au
02 9385 2060

Educational Support Service
Educational Support Advisors work with all students to promote the development of skills needed to succeed at university, whilst also providing personal support throughout the process. Check their website to request an appointment or to register in the Academic Success Program.
John Goodsell Building, Ground Floor.
advisors@unsw.edu.au
02 9385 4734

Library services and facilities for students
The UNSW Library offers a range of collections, services and facilities both on-campus and online.
Main Library, F21.
02 9385 2650

Moodle eLearning Support
Moodle is the University’s learning management system. You should ensure that you log into Moodle regularly.
externalteltsupport@unsw.edu.au
02 9385 3331

UNSW IT
UNSW IT provides support and services for students such as password access, email services, wireless services and technical support.
UNSW Library Annexe (Ground floor).
itservicecentre@unsw.edu.au
02 9385 1333

Disability Support Services
UNSW Disability Support Services provides assistance to students who are trying to manage the demands of university as well as a health condition, learning disability or who have personal circumstances that are having an impact on their studies. Disability Advisers can arrange to put in place services and educational adjustments to make things more manageable so that students are able to complete their course requirements. To receive educational adjustments for disability support, students must first register with Disability Services.
Ground Floor, John Goodsell Building.
disabilities@unsw.edu.au
02 9385 4734

UNSW Counselling and Psychological Services
Provides support and services if you need help with your personal life, getting your academic life back on track or just want to know how to stay safe, including free, confidential counselling.
Level 2, East Wing, Quadrangle Building.
counselling@unsw.edu.au
02 9385 5418


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