MARKETNG 2002 - Marketing Analytics

North Terrace Campus - Semester 1 - 2022

This course develops students? capabilities to use analytical tools and techniques to address marketing problems, with a focus on providing data to assist marketing decision-making. Marketing analytics enables marketers to measure, manage and analyse customer preferences and trends, as well as evaluate marketing performance to maximize its effectiveness. Students will develop an understanding how to use marketing analytics to predict outcomes. The course also examines the ethical and technical issues related to data privacy.

  • General Course Information
    Course Details
    Course Code MARKETNG 2002
    Course Marketing Analytics
    Coordinating Unit Adelaide Business School
    Term Semester 1
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 3 hours per week
    Available for Study Abroad and Exchange Y
    Prerequisites ECON 1008
    Course Description This course develops students? capabilities to use analytical tools and techniques to address marketing problems, with a focus on providing data to assist marketing decision-making. Marketing analytics enables marketers to measure, manage and analyse customer preferences and trends, as well as evaluate marketing performance to maximize its effectiveness. Students will develop an understanding how to use marketing analytics to predict outcomes. The course also examines the ethical and technical issues related to data privacy.
    Course Staff

    Course Coordinator: Dr Alex Belli

    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    On successful completion of this course, students will be able to:
    1. Demonstrate the use of analytical tools in marketing.
    2. Choose 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.
    5. Explain and illustrate how marketing analytics are used in an integrated manner to solve strategic marketing problems.
    University Graduate Attributes

    No information currently available.

  • Learning Resources
    Required Resources
    Due to the practical nature of the subject, no required textbook is needed for MARKETNG2002, so please rely on the lecture/tutorial slides and any additional material provided on MyUni by the subject coordinator. Additional readings will be available to you via the Course Readings link in the left-hand navigation panel. As a student in this class, you have full access to all University of Adelaide library resources.
    Recommended Resources
    1. Hair, J., Harrison, D.E., and Ajjan, H. (2022). Essentials of Marketing Analytics (1st Edition). MCGraw Hill. ISBN: 978-1-260-59774-5.
    2. Lillien, G.L., Rangaswamy, A., and De Bruyn (2017). Principles of Marketing Engineering and Analytics (3rd Edition). DecisionPro, Inc. ISBN: 978-0985764821.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    The subject is based on dynamic and interactive weekly sessions, comprising of a two-hour lecture and a one-hour tutorial.
    Both lecture and tutorial 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

    Week #          
    Lecture Content Tutorial and Assessments
    Week 1

    Overview of the Course
    Introduction to Marketing Analytics
    No tutorials
    Week 2

    Customer Value: Satisfaction, Net Promoter Score and Customer Lifetime Value

    Tutorial 1
    Tutorials start this week. Group project allocations will be made during the tutorial. Contact your tutor if you are unable to attend to be placed in a group.
    Week 3

    Metrics to Measure Brand Assets
    Guest lecture by Dr Tony Aitchison from Paradelta Strategy: “ Calculating Brand Equity Index and Using it to Leverage Business Growth”
    Tutorial 2

    Note: Due to the public holiday in 2022, Monday tutorials have been rescheduled to Tuesday 15.03.22 as follows:
    • TUT02 – Tuesday 10 AM in Nexus10 computer suite 3
    • TUT05 – Tuesday 11 AM in Nexus10 computer suite 3
    • TUT08 – Tuesday 4 PM online class
    Week 4

    Experimental Design I: Market tests, experiments and A/B testing Tutorial 3

    Assessment(s): Proposal presentations will be held during tutorial time.
    Week 5

    Experimental Design II: Conjoint Analysis for New Product Development Tutorial 4
    Week 6

    Data Management and Wrangling
    Types of Data
    Data Visualisation and Summary Measures
    Tutorial 5

    Assessment(s): Mid-term in-class quiz will be held during tutorial time.
    Mid-semester Break
    Week 7

    Inferential statistics
    Revision of tests of differences (T-tests, ANOVA, ANCOVA etc.)
    Tutorial 6

    Note: Due to the public holiday in 2022, Monday tutorials have been rescheduled to Tuesday 26.04.22 as follows:
    • TUT02 – Tuesday 10 AM in Nexus10 computer suite 3
    • TUT05 – Tuesday 11 AM in Nexus10 computer suite 3
    • TUT08 – Tuesday 4 PM online class
    Week 8

    Revision of tests of association (e.g., cross-tab, correlation
    Regression Analysis and Logistic Regression Analysis
    Tutorial 7
    Week 9

    STP Model I: Segmentation and Targeting
    Cluster Analysis
    Tutorial 8
    Week 10

    STP Model II: Positioning
    Perceptual Maps
    Factor Analysis and Multidimensional Scaling
    Tutorial 9

    Assessment(s):
    Video Presentations due 
    Week 11

    Introduction to Data Mining and Basket Analysis Tutorial 10
    Feedback on the video presentations will be provided to students.
    Week 12

    Review of the Course and Information about the final take-home assessment Tutorial 11

    Assessment(s): End-of-term Quiz will take place during tutorial time. Final Reports are due 
    Week 13

    No lecture Tutorial 12
    A final take-home assessment drop-in session will be held during tutorial time.

    Assessment(s):
    The final take-home assessment will be available online this week 
  • Assessment

    The University'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
    Mid-term Quiz Individual 10% 1, 4, 5
    Proposal Presentation Group/Individual 5% 2, 5
    Project Report Video Presentation Group 10% 1, 2, 3, 4
    Project Report Group 15% 1, 2, 3, 4
    End-of-term Quiz Individual 10% 1, 3, 4, 5
    Take-home Assessment Individual 40% 1, 3, 4
    In-class participation Individual 10% 1, 5
    Assessment Detail
    The assessment for this subject is made up of the following components:
    • Two in-class quizzes (both 10%), one in Week 6 (mid-term) and one in Week 12 (end-of-term). These quizzes will test students' knowledge and understanding of the topics covered in the lectures and the tutorials. A cheat sheet for formulas and SPSS commands/output interpretation can be used;
    • A group project (30%) which will require students to identify a marketing issue, collect data, interpret it, and provide recommendations based on their analysis to a company. This is a formative assignment and is made up of three parts:
      • Part A: Proposal Presentation (5%) due either in Week 3 or 4. Students will be required to put together and present a research proposal during the tutorial, which should outline: (1) their main research problem/question, (2) their proposed methodology and (3) their result expectations (hypotheses);
      • Part B: Video Presentation (10%) due in Week 10. Students will be required to submit an online video (e.g. Youtube) where they summarise their main research problem(s), their research approach, methodology, and their preliminary findings;
      • Part C: Project Report (15%) due in Week 12. 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;
    • A take-home assessment (40%) due during the official final exam period (to be discussed with students). 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;
    • In-class participation (10%) assessed throughout the term. Participation will be evaluated on students' engagement in in-class discussions, participation in demonstrations (e.g., showing how to do an exercise, etc.), willingness and proactiveness to help others, and general attitude. Participation will be assessed both in lectures and tutorials. Marks will be given and released at the end of the term.


    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 Access Adelaide.

  • Student Feedback

    The University 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 University 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.

  • Student Support
  • Policies & Guidelines
  • Fraud Awareness

    Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.

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