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MARKETNG 7001 - Analytics for Marketers

North Terrace Campus - Trimester 2 - 2023

This subject responds to businesses' increasing emphasis and reliance on big data and considers the use of analytics and advanced statistical methods in order to support marketing decision-making. In this subject, students will be able to develop practical and analytical skills relevant to address marketing problems, which involve measurement, management and assessment of customer preferences and firm performance to maximise marketing effectiveness, both offline and online. Students will be able to develop an understanding of descriptive analytics (e.g. Customer Lifetime Value, Net Promoter Score etc.) and predictive analytics (e.g. A/B testing and conjoint analysis), and their application. In addition, students will be exposed to a variety of methodological techniques essential to analyse big datasets, both quantitative (e.g. multiple and logistic regression, cluster analysis) and qualitative (e.g. text and sentiment analysis). The subject will also introduce students to digital marketing analytics and examines the ethical and technical issues related to data privacy and big data.

  • General Course Information
    Course Details
    Course Code MARKETNG 7001
    Course Analytics for Marketers
    Coordinating Unit Marketing
    Term Trimester 2
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Contact Up to 3 hours per week
    Available for Study Abroad and Exchange N
    Prerequisites MARKETNG 7104, COMMERCE 7039
    Course Staff

    Course Coordinator: Dr Kim Huynh

    Dr Kim Huynh (subject coordinator and lecturer)
    Course Timetable

    The full timetable of all activities for this course can be accessed from .

  • Learning Outcomes
    Course Learning Outcomes
    1. Demonstrate the understanding and use of metrics and analytical tools in marketing.
    2. Choose and critique appropriate data sources and analytical tools to assess marketing performance.
    3. Apply analytics tools to a variety of data collected by marketers.
    4. Translate the results of quantitative analyses into managerial insights for marketing decision-making to solve strategic and tactical problems.
    最新糖心Vlog Graduate Attributes

    No information currently available.

  • Learning Resources
    Required Resources

    Textbook


    Hair, J., Harrison, D.E., and Ajjan, H. (2022). Essentials of Marketing Analytics (1st Edition). MCGraw Hill. ISBN: 978-1-260-59774-5.

    Due to the practical nature of the subject, students should also rely on the lecture/tutorial slides and any additional material provided on MyUni by the subject coordinator. Additional readings will be available via the Course Readings link in the left-hand navigation panel. As a student in this class, you have full access to all 最新糖心Vlog of Adelaide library resources.



    Software

    As part of this subject, students will learn to use the following software:

    SPSS
    Tableau*
    Python and Anaconda*
    Leximancer*

    Students are encouraged to download the above-mentioned software (indicated by “*”) on their laptop in preparation for tutorials and their assignments. The subject coordinator will provide instructions on how to download and use these platforms on MyUni and during the tutorials.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    The subject is based on dynamic and interactive weekly workshops held in a computer lab. The workshop will involve critical debates, in-depth case discussions, in-class exercises, practical demonstrations, and student presentations. Students are expected to access materials provided online (lecture slides, textbooks, and other readings, videos and/or case studies) prior to class and to complete any set activities recommended by the lecturer.

    Students are expected to review the weekly readings as well as online materials and to be able to discuss the material with other students during the course of the tutorials. Tutorials will include time where students will work together in student-led discussions of the exercise and/or case with the provision of tutor and peer feedback. The class will receive weekly feedback from both peers and instructors.
    Workload

    No information currently available.

    Learning Activities Summary
    1. Overview of the Course and Introduction to Marketing Analytics
    2. All About Data: Data Types, Data Management, and Wrangling
    3. Exploring and Visualising Data Patterns
    4. Metrics to Assess Customer Value
    5. Metrics to Assess Customer Engagement on Digital Platforms
    6. Natural Language Processing
    7. Experimental Research I: Experimental Designs and Analysis
    8. Experimental Research II: Conjoint Analysis
    9. Predictive Analytics and Regression
    10. Discriminant Analysis and Logistic Regression
    11. Analysis Methods for the STP Model: Segmentation, Targeting, and Positioning
    Specific Course Requirements
    In order to enroll in MARKETNG 7001, students are required to have successfully completed the following courses:
    • MARKETNG 7104 – Marketing Management
    • COMMERCE7039 – Business Research Methods
    These subjects' contents will be considered assumed knowledge and briefly reviewed during the lectures and tutorials.
  • Assessment

    The 最新糖心Vlog's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    Assessment Task Task Type Weighting Learning Outcome
    Two In-Class Quizzes Individual 30% 1, 2, 4
    Research Proposal Presentation Group 5% 1, 2
    Group Report Presentation Group 10% 3, 4
    Group Report Group 15% 2, 3, 4
    In-Class Participation Individual 10% 1, 2, 3, 4
    Take-Home Final Assessment Individual 30% 2, 3, 4
    Assessment Detail
    The assessment for this subject is made up of the following components:
    1. Two in-class quizzes (30%), one in Week 7 (mid-term) and one in Week 12 (end-of-term). These quizzes are closed-book and will test students' knowledge and understanding of the topics covered in the lectures and the tutorials;
    2. A group project (30%), which will require students to work in collaboration with a local company. Students will carry out research for the company based on a brief and a series of issues that the company is facing. This is a formative assignment and is made up of three parts:
      • Part A: Group Project Research Proposal (5%) due in Week 6. Students will be required to deliver a research proposal, which should outline: (1) their main research problem/question, (2) their proposed methodology (including measures, sampling methods, and questionnaire draft), and (3) their result expectations (hypotheses);
      • Part B: Group Project Report Presentation (10%) due in Week 11. Students will be required to present their research approach, methodology, and their preliminary findings in a 10-minute (max) presentation;
      • Part C: Group Project Report (15%) due in Week 11. Students will be required to submit a report that outlines the main research problem(s), research questions, methodology, and the results of their research. The appendices should include the instruments utilised to collect data and the SPSS outcome/analysis. Students should integrate the feedback received throughout the semester into the report;
    3. In-Class Participation (10%), assessed throughout the term. This will be assessed based on the students' in-class engagement, participation in discussions and demonstrations, and proactiveness to help others.;
    4. A Take-home Final Exam (30%), due during the official final exam period. Students will be provided with a file, and they will have to conduct analyses on it using the methods learnt in class. They should finally draw conclusions and provide recommendations based on them. The turnaround for this assignment is one week.
    Submission

    No information currently available.

    Course Grading

    Grades for your performance in this course will be awarded in accordance with the following scheme:

    M10 (Coursework Mark Scheme)
    Grade Mark Description
    FNS   Fail No Submission
    F 1-49 Fail
    P 50-64 Pass
    C 65-74 Credit
    D 75-84 Distinction
    HD 85-100 High Distinction
    CN   Continuing
    NFE   No Formal Examination
    RP   Result Pending

    Further details of the grades/results can be obtained from Examinations.

    Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

    Final results for this course will be made available through .

  • Student Feedback

    The 最新糖心Vlog places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the 最新糖心Vlog to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

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  • Policies & Guidelines
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