CRICOS Provider Code: 00586B

School of Management and Marketing

College of Business and Economics

BMA708 MARKETING INSIGHTS INTO BIG DATA

Semester 2, 2020

Unit Outline

Steven D'Alessandro

© The University of Tasmania 2018

CONTACT DETAILS

Unit coordinator

Unit coordinator: Steven D’Alessandro

Campus: Sandy Bay

Email: steve.dalessandro@utas.edu.au

Phone: +613 6226 2836

Room location and number: Room 212, Centenary Building, Sandy Bay TAS 7005

Consultation hours: Tuesday 11 a.m -2.30 p.m Wednesday 12.30-2.30 p.m

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CONTENTS

WHAT IS THE UNIT ABOUT? 2

UNIT DESCRIPTION 2

INTENDED LEARNING OUTCOMES 2

GRADUATE STATEMENT 2

ALTERATIONS TO THE UNIT AS A RESULT OF STUDENT FEEDBACK 3

PRIOR KNOWLEDGE &/OR SKILLS 3

HOW WILL I BE ASSESSED? 4

ASSESSMENT SCHEDULE 4

ASSESSMENT DETAILS 5

HOW YOUR FINAL RESULT IS DETERMINED 9

SUBMISSION OF ASSIGNMENTS 10

ACADEMIC INTEGRITY 12

ACADEMIC MISCONDUCT 14

STUDENT BEHAVIOUR 14

WHAT LEARNING OPPORTUNITIES ARE THERE? 15

MYLO 15

RESOURCES 15

ACTIVITIES 19

COMMUNICATION 20

CONCERNS AND COMPLAINTS 21

LEARNING SUPPORT 21

FURTHER INFORMATION AND ASSISTANCE 22

UNIT SCHEDULE 23

ACCREDITATION 25

AACSB ACCREDITATION 25

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WHAT IS THE UNIT ABOUT?

Unit description

The advent of big data accelerated by the internet, ecommerce and social media

provides opportunities for better business/organisational management and a better

society through evidence-based decision-making and the provision of new services

and products. This subject introduces the conceptual and practical issues in

developing models to aid in decision making in marketing. Students will be

introduced to the discovery and analysis of social networks, social trends, and

relationships amongst industry factors using spreadsheets and data visualisation

software. Students will also translate these analytic models into competitive strategy

models by making policy for strategic and other decision recommendations.

Intended Learning Outcomes

On completion of this unit, you will be able to:

1. critically examine different methods of data analysis and presentation for social networks, complex systems and relational links.

2. apply intermediate skills in spreadsheets and data visualisation software to identify trends and relationships among factors in industry and society.

3. analyse government, industry and social media data to identify relationships and trends

4. critically evaluate conclusions drawn from different data and analytic tools. 5. create interactive models using appropriate software and effectively

communicate results and findings to aid decision-makers in understanding interrelationships and trends.

Graduate Statement

Successful completion of this unit supports your development of course learning

outcomes, which describe what a graduate of a course knows, understands and is able

to do. Course learning outcomes are available from the Course Coordinator. Course

learning outcomes are developed with reference to national discipline standards,

Australian Qualifications Framework (AQF), any professional accreditation

requirements and the University of Tasmania’s Graduate Statement.

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The University of Tasmania experience unlocks the potential of

individuals. Our graduates are equipped and inspired to shape and

respond to the opportunities and challenges of the future as

accomplished communicators, highly regarded professionals and

culturally competent citizens in local, national, and global society.

University of Tasmania graduates acquire subject and multidisciplinary

knowledge and skills and develop critical and creative literacies and

numeracies and skills of inquiry. They demonstrate the ability to apply

this knowledge in changing circumstances. Our graduates recognise and

critically evaluate issues of social responsibility, ethical conduct and

sustainability, are entrepreneurial and creative, and are mindful of their

own wellbeing and that of the community. Through respect for diversity

and by working in collaborative ways, our graduates reflect the values of

the University of Tasmania.

Alterations to the unit as a result of student feedback

This subject has not been taught for two years and is being relaunched with new

assessments under a different lecturer.

Prior knowledge &/or skills

Basic Excel Skills

The beginner’s guide to excel https://www.youtube.com/watch?v=rwbho0CgEAE

An Understanding of Data Visualisation Using Tableau

Introduction to Tableau https://www.youtube.com/watch?v=VUVqj7YsWmU

Familiarisation with Insight Maker to Develop Simulations

Introduction to Insight Maker v2 https://www.youtube.com/watch?v=9XX_zoKHPXg

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HOW WILL I BE ASSESSED?

Assessment schedule

Assessment task Date due Percent weighting

Links to Intended Learning Outcomes

Assessment Task 1: Spreadsheet analysis

End of Week 4, August 7th, 4.00 p.m

35%

Entrepreneurship and creativity (ILOs 2,5), Literacy (Method, ILOs2,4,5), Numeracy (Method, ILOs 2,4,5), Experiential learning (Method, ILOs 2,4,5)

Assessment Task 2: Data visualisation

End of Week 9, September 25th, 4.00 p.m

35%

Entrepreneurship and creativity (Method, ILO 5), Literacy (Method, ILO 5), Numeracy (Method, ILO 5), Experiential learning (Method, ILO 5)

Assessment Task 3: Complex systems

End of Week 13, October 25th, 4.00 p.m

30%

Ethics (Method), Entrepreneurship and creativity (Method, ILO 5),

Literacy (Method, ILO 5), Numeracy (Method, ILO 5), Experiential learning (Method, ILO 5)

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Assessment details

Assessment task 1: Spreadsheet analysis

Task descrip

tion

You are working for a real estate development firm which is considering various options in and around the Greater Sydney region versus investing in Tasmania. The company buys residential property and then builds or renovates as needed and then sells to owner occupiers or investors. Typically, the company buys one of more adjacent stand-alone houses and then builds small-to-medium-sized apartment blocks. The combination of interest payments, opportunity costs, building costs, and taxes require the company to build and sell as quickly as possible, while maintaining a high-quality product. There is regular debate among senior managers about where is the best place to invest. As an analyst for several managers occasionally you are asked to gather evidence to suit different agendas. Your Task You will be allocated one Local Government Area (LGA) to compare it to the Tasmanian market. Your task is to prepare two separate short reports within an Excel document: 1. Show that your LGA is a good place to build property for resale compared to Tasmania. Maximum 100 words of explanatory notes (not counting cover sheet, tables or graphs). 2. Note: your graph should tell the story, not your verbal explanation. 3. Show that your LGA is a bad place to build property for resale compared to Tasmania. Maximum 100 words of explanatory notes (not counting cover sheet, tables or graphs). 4. Note: your graph should tell the story, not your verbal explanation.

Resources

Excel Spreadsheets Sales, Trend March 1991 – December 2015, Metropolitan LGAs (http://www.housing.nsw.gov.au/data/assets/file/0009/373851/ Sales GMR 15q4.xls) may be downloaded from the NSW Department of Housing

(http://www.housing.nsw.gov.au/aboutus/

reports-plans-and-papers/rentand-

sales-reports/latest-issue).

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The data show median home prices over time in a number of LGA's. Dollar values in these resources do not take account of inflation. You may decide that these prices should be adjusted for inflation using the Housing Inflation data in Table 5 or Table 9 of the Consumer Price Index data

http://www.abs.gov.au/ausstats/abs%40.nsf/mf/6401.0

Further information about Tasmanian property prices can also be accessed from the ABS at https://www.abs.gov.au/AUSSTATS/abs@.nsf/Lookup/6416.0Main+Features1Dec%202019?OpenDocument

Criterion Measures Intended Learning Outcome:

Criterion 1

Organisation of spreadsheet ILO1, ILO2

Criterion 2

Formulae used in spreadsheet ILO1, ILO2

Criterion 3

Positive report: Graph ILO2, ILO3, ILO5

Criterion 4

Positive report: Organisation ILO2, ILO3, ILO5

Criterion 5

Positive report Evidence ILO4, ILO5

Criterion 6

Negative report: Graph ILO2, ILO3, ILO5

Criterion 7

Negative report: Organisation ILO2, ILO3, ILO5

Criterion 8

Negative report Evidence ILO4, ILO5

Task

length

Your task is to prepare two separate short reports within an Excel document. 200 words maximum.

Due by date

End of Week 4, August 7th, 4.00 p.m

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Assessment task 2: Data visualisation

Task description Access Data (Excel spreadsheets) from a report on Estate planning in Australia. This will be provided on MyLo.

1. Use Tableau® to create an interactive dashboard allowing the user to interrogate data from the spreadsheet, to address basic questions about how estate planning in Tasmania differs from that in the rest of Australia. (15 marks).

2. Construct a Story Board to explain some aspect of the data you find interesting or that could be useful for insights into this industry. (20 Marks).

Criterion Measures Intended

Learning Outcome:

Criterion 1 Dashboard- Comparisons ILO5

Criterion 2 Dashboard-Ease of Use ILO5

Criterion 3 Dashboard-Formatting ILO5

Criterion 4 Storyboard- Interesting, important or novel issue

ILO5

Criterion 5 Storyboard- Short well-presented story. ILO5

Criterion 6 Storyboard-formatting ILO5

Task length Online dashboard and Storyboard. Around 300 words.

Due by date End of Week 9, September 25th, 4.00 p.m

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Assessment task 3: Complex systems

Task description Students will create a model of complex interactions among industry or social factors Using Insight Maker. (https://insightmaker.com) to create a complex systems model of the relationships among different players in the town of Burnie, Tasmania. Create a simulation model which demonstrates by simulation how the town can deal with a new outbreak of COVID-19, balancing health and economic outcomes. The model does not have to directly reflect reality (that can come later), so you don't need to consider a record of actual levels of disease outbreaks, economic impacts in Burnie in the past. Some hints for getting started in modelling are provided in MyLo.

Submit your assignment simply by sharing your finished model with the Insight Maker Group called BMA708_Marketing insights into Big Data (Details about how to do this are provided on MyLo.

Tips for getting

started

1. Draw a very simple model on a piece of paper first. 2. Explore some of the many models and examples in the

InsightMaker website 1. Note or make copies of interesting models

and use them as a guide to how to make your variables interact with each other.

3. Create a model in InsightMaker that is even more simple than your paper model, involving just two or three variables.

1. Link them with appropriate formulae and check that a simulation moves the variables in the expected directions.

2. Check the range of parameter values for which the simulation works or makes sense.

4. Add one more variable to the model, 1. Link it with appropriate formulae. 2. Check the simulation and sensitivity of the

model to parameter settings. 5. Add additional variables as needed.

1. Check the simulation and sensitivity to extreme values.

6. Keep notes as you go 1. Use these to help your documentation.

7. Remember that a good simple model is more useful than a bad complicated model. A "model" is a simplified representation of a real thing, designed to help us understand some aspect of that

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thing. No model can include all the many aspects of the real thing at the same time (without being the real thing) . So, a model usually includes only those aspects that a relevant to the issue we are studying.

Example: Our understanding of flight for different purposes are represented with models of:

1. a glue-together plastic model airplane 2. a paper plane 3. a venturi 4. a bird's wing.

Criterion Measures Intended

Learning Outcome:

Criterion 1 Adjustments that can be made to the model by a user. (sliders, settings)

ILO5

Criterion 2 Inclusion of appropriate interrelationships, feedback loops, etc.

ILO5

Criterion 3 Documentation (explaining what is going

on).

ILO5

Task length 500 words should do it.

Due date End of Week 13, October 25th, 4.00 p.m

How your final result is determined

To pass this unit, you need to demonstrate your attainment of each of the Intended

Learning Outcomes.

Your grade will be determined in the following way:

Your overall mark in this unit will be determined by combining your results from each

assessment task. These marks are combined to reflect the percentage weighting of

each task. You need to achieve an overall score of at least 50% to successfully complete

this unit. It is expected that you will seek help (from the unit coordinator in the first

instance), well before the due date, if you are unclear about the requirements for an

assessment task.

- PP (pass) at least 50% of the overall mark but less than 60%

- CR (credit) at least 60% of the overall mark but less than 70%

- DN (distinction) at least 70% of the overall mark but less than 80%

- HD (high distinction) at least 80% of the overall mark

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All grades are provisional, until confirmation by the Assessment Board at the end of

semester.

Submission of assignments

The act of submitting your assignment will be taken as certification that it is your own

work.

Assignments, unless other specified, must be submitted electronically through the

relevant assignment tab in MyLO. You must ensure that your name, student ID, unit

code, tutorial time and tutor’s name (if applicable) are clearly marked on the first

page. If this information is missing, the assignment will not be accepted and,

therefore, will not be marked.

Where relevant, Unit Coordinators may also request you to submit a paper version of

your assignment. You will be advised by the Unit Coordinator of the appropriate

process relevant to your campus.

Please remember that you are responsible for lodging your assessment items on or

before the due date and time. We suggest you keep a copy. Even in a perfect system,

items sometimes go astray.

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Requests for extensions

In this Policy:

1. (a) ‘day’ or ‘days’ includes all calendar days, including weekends and public holidays;

(b) ‘late’ means after the due date and time; and

(c) ‘assessment items’ includes all internal non-examination-based forms of

assessment

2. This Policy applies to all students enrolled in TSBE Units at whatever Campus or

geographical location.

3. Students are expected to submit assessment items on or before the due date and

time specified in the relevant Unit Outline. The onus is on the student to prove the

date and time of submission.

4. Students who have a medical condition or special circumstances may apply for an

extension. Requests for extensions should, where possible, be made in writing to the

Unit Coordinator on or before the due date. Students will need to provide

independent supporting documentation to substantiate their claims.

Penalties

Late submission of assessment items will incur a penalty of 10% of the total marks

possible for that piece of assessment for each day the assessment item is late unless an

extension had been granted on or before the relevant due date.

Assessment items submitted more than five (5) days late will not be accepted.

Academic staff do NOT have the discretion to waive a late penalty, subject to clause 4

above.

Review of results and appeals Review of Assessment is available to all students once the University has released the

final result for a unit. If you are dissatisfied with your final result, you may apply to

have it reviewed. Applications for a review of assessment are due within 10 working

days of the release of the final result in the unit. When applying for a review, you must

pay a $50 fee.

If you wish to have a piece of internal assessment reviewed as part of the review

process, please state this clearly on the application form referred to above and include

that assessment item with your application.

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Please read and follow the directions provided by the University at:

https://www.utas.edu.au/students/admin/forms/student-forms/exams/application-

for-review-of-assessment

Academic integrity

What is academic integrity?

The University community is committed to upholding the Statement on Academic

Integrity.

A breach of academic integrity is defined as being when a student:

a) fails to meet the expectations of academic integrity; or b) seeks to gain, for themselves or for any other person, any academic

advantage or advancement to which they or that other person is not entitled; or

c) improperly disadvantages any other member of the University community.

Breaches of academic integrity such as plagiarism, contract cheating, collusion and so

on are counter to the fundamental values of the University and can result in a range of

penalties. These penalties are outlined in Ordinance 9: Student Academic Integrity.

More information is available from the Academic Integrity for Students webpage.

The University and any persons authorised by the University may submit your

assessable works to a text matching service, to obtain a report on possible instances of

plagiarism or contract cheating.

Academic Integrity Training Module

As part of the University’s educative approach to academic integrity, there is a short

Academic Integrity Training Module that all students are required to complete.

Completion of the module allows you to demonstrate your understanding of what

constitutes academic misconduct.

The Academic Integrity Training Module is available for all students through MyLO.

All commencing students (pre-degree through to higher degree by research) are required to complete the Academic Integrity module. If you do not complete this module your final unit results will be withheld. You should aim to complete the module within the first few weeks of commencing study at the University.

Page 13 BMA708 Marketing Insights into Big Data

Academic referencing

In your written work you will need to support your ideas by referring to scholarly

literature, works of art and/or inventions. It is important that you understand how to

correctly refer to the work of others and maintain academic integrity.

Failure to appropriately acknowledge the ideas of others constitutes a breach of

academic integrity, a matter considered by the University of Tasmania as a serious

offence.

The appropriate referencing style for this unit is American Psychological Association

6th (APA 6th). See http://utas.libguides.com/referencing/APA

The University library provides information on presentation of assignments, including

referencing styles and should be referred to when completing tasks in this unit.

Please read the following statement on plagiarism. Should you require clarification

please see your unit coordinator or lecturer.

For further information, see the Academic Integrity for Students webpage.

Plagiarism

Plagiarism is a form of cheating. It is taking and using someone else's

thoughts, writings or inventions and representing them as your own; for

example, using an author's words without putting them in quotation

marks and citing the source, using an author's ideas without proper

acknowledgment and citation, copying another student's work.

If you have any doubts about how to refer to the work of others in your

assignments, please consult your lecturer or tutor for relevant referencing

guidelines. You may also find the Academic Honesty site on MyLO of

assistance.

The intentional copying of someone else’s work as one’s own is a serious

offence punishable by penalties that may range from a fine or

deduction/cancellation of marks and, in the most serious of cases, to

exclusion from a unit, a course or the University.

The University and any persons authorised by the University may

submit your assessable works to a plagiarism checking service, to

obtain a report on possible instances of plagiarism. Assessable

works may also be included in a reference database. It is a

condition of this arrangement that the original author’s

permission is required before a work within the database can be

viewed.

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Academic misconduct

Academic misconduct includes cheating, plagiarism, allowing another student to

copy work for an assignment or an examination, and any other conduct by which a

student:

a. seeks to gain, for themselves or for any other person, any academic

advantage or advancement to which they or that other person are not

entitled; or

b. improperly disadvantages any other student.

Students engaging in any form of academic misconduct may be dealt with under

the Ordinance of Student Discipline, and this can include imposition of

penalties that range from a deduction/cancellation of marks to exclusion from a

unit or the University. Details of penalties that can be imposed are available in

Ordinance 9: Student Discipline – Part 3 Academic Misconduct.

Student Behaviour

The University Behaviour Policy sets out behaviour expectations for all members of

our University community including students and staff.

The aim in doing so is to ensure that our community members are safe, feel valued

and can actively contribute to our University mission.

It is expected that community members behave in a manner that is consistent with

our University values – respect, fairness and justice, integrity, trust, responsibility

and honesty. There are also certain behaviours that are considered inappropriate,

such as unlawful discrimination, bullying and sexual misconduct.

The accompanying University Behaviour Procedure sets out the process and

avenues that University community members can access to resolve concerns and

complaints regarding inappropriate behaviour by a University community member.

Wherever possible, the focus will be on early intervention and a ‘restorative’

approach that creates awareness of inappropriate behaviour and its impact on others.

However, in some cases, students who engage in inappropriate behaviour may be

subject to disciplinary proceedings, which may impact upon continuation of their

studies.

Students can seek support and assistance from the Safe and Fair Community Unit

SaFCU@utas.edu.au or ph: 6226 2560.

Matters are dealt with in confidence and with sensitivity.

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WHAT LEARNING OPPORTUNITIES ARE THERE?

MyLO

MyLO is the online learning environment at the University of Tasmania. This is the

system that will host the online learning materials and activities for this unit. As this

subject will likely be taught online this will be the main method of instruction. MyLo

provides additional information about assessments, readings and access to lectures

and demonstrations. Links to recordings of online meetings will also be available

there. It is the main portal for all learning in this subject.

Getting help with MyLO

It is important that you are able to access and use MyLO as part of your study in this

unit. To find out more about the features and functions of MyLO, and to practice

using them, visit the Getting Started in MyLO unit.

For access to information about MyLO and a range of step-by-step guides in pdf, word

and video format, visit the MyLO Student Support page on the University website.

If something is not working as it should, contact the Service Desk (phone 6226 2600

or request ITS help online through the Service Portal).

Resources

Required readings

You will need the following text:

Winston, Wayne (2014) Marketing analytics: Data-driven techniques with Microsoft

Excel, John Wiley and Sons, IN, USA. ISBN 978-1-11837349-9

Recommended readings

Body of knowledge

• Ahlemeyer-Stubbe, A., & Coleman, S. (2014). A practical guide to data mining

for business and industry. John Wiley & Sons.

• Franks, B. (2012). Taming the big data tidal wave: Finding opportunities in huge

data streams with advanced analytics (Vol. 49). John Wiley & Sons.

• Hu, W. C., & Kaabouch, N. (2014). Big Data Management, Technologies, and

Applications. Information Science Reference.

• Linoff, G. S., & Berry, M. J. (2011). Data mining techniques: for marketing, sales,

and customer relationship management. John Wiley & Sons.

Page 16 BMA708 Marketing Insights into Big Data

• Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to

know about data mining and data-analytic thinking. " O'Reilly Media, Inc.".

• Published articles

• Alemany Oliver, M., & Vayre, J.-S. (2015). Big data and the future of knowledge

production in marketing research: Ethics, digital traces, and abductive

reasoning. Journal of Marketing Analytics, 3(1), 5-13.

• Andonova, Y. (2013). Loyalty 3.0: How big data and gamification are

revolutionizing customer and employee engagement. Journal of Marketing

Analytics, 1(4), 234-236.

• Breur, T. (2015). Big data and the internet of things. Journal of Marketing

Analytics, 3(1), 1-4.

• Calder, B. J., Malthouse, E. C., & Maslowska, E. (2016). Brand marketing, big

data and social innovation as future research directions for engagement.

Journal of Marketing Management, 32(5-6), 579-585.

• Danaher, B., Yan, H., Smith, M. D., & Telang, R. (2014). An empirical analysis of

digital music bundling Strategies. Management Science, 60(6), 1413-1433.

• Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big data consumer analytics and

the transformation of marketing. Journal of Business Research, 69(2), 897-904.

• Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015).

How ‘big data’ can make big impact: Findings from a systematic review and a

longitudinal case study. International Journal of Production Economics, 165, 234-

246.

• Gloor, P. A., & Giacomelli, G. (2014). Reading global clients' signals. MIT Sloan

Management Review, 55(3), 23-29.

• Gopaldas, A. (2014). Marketplace Sentiments. Journal of Consumer Research,

41(4), 995-1014.

• Green, P. E. (1992). Paradigms, paradiddles, and parafoils. Journal of the

Academy of Marketing Science, 20(4), 377.

• Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality

for data science, predictive analytics, and big data in supply chain management:

An introduction to the problem and suggestions for research and applications.

International Journal of Production Economics, 154, 72-80.

Page 17 BMA708 Marketing Insights into Big Data

• He, W., Wu, H., Yan, G., Akula, V., & Shen, J. (2015). A novel social media

competitive analytics framework with sentiment benchmarks. Information &

Management, 52(7), 801-812.

• Hofacker, C. F., Malthouse, E. C., & Sultan, F. (2016). Big data and consumer

behavior: imminent opportunities. Journal of Consumer Marketing, 33(2), 89-97.

• Jobs, C. G., Aukers, S. M., & Gilfoil, D. M. (2015). The impact of Big data on your

firm's marketing communications: A framework for understanding the

emerging marketing analytics industry. Academy of Marketing Studies Journal,

19(2), 81-92.

• Kim, H. S. (2015). Attracting views and going viral: How message features and

news-sharing channels affect health news diffusion. Journal of Communication,

65(3), 512-534.

• Leeflang, P. S. H., Verhoef, P. C., Dahlström, P., & Freundt, T. (2014).

Challenges and solutions for marketing in a digital era. European Management

Journal, 32(1), 1-12.

• Montgomery, K. C. (2015). Youth and surveillance in the Facebook era: Policy

interventions and social implications. Telecommunications Policy, 39(9), 771-

786.

• Post, R., & Edmiston, D. (2014). Challenging big data preconceptions: New ways

of thinking about data and integrated marketing communication. International

Journal of Integrated Marketing Communications, 6(1), 18-24.

• Pousttchi, K., & Hufenbach, Y. (2014). Engineering the value network of the

customer interface and marketing in the data-rich retail environment.

International Journal of Electronic Commerce, 18(4), 17-42.

• Rex Yuxing, D., Ye, H., & Damangir, S. (2015). Leveraging trends in online

searches for product features in market response modeling. Journal of

Marketing, 79(1), 29-43.

• Rust, R. T., & Ming-Hui, H. (2014). The Service revolution and the

transformation of marketing science. Marketing Science, 33(2), 206-221.

• Scarlet, S., & Tarraf, S. (2015). Mining for insights. Marketing Insights, 27(4), 18-

19.

• Sudhir, K. (2016). The Exploration-exploitation tradeoff and efficiency in

knowledge production. Marketing Science, 35(1), 1-9.

Page 18 BMA708 Marketing Insights into Big Data

• Tirunillai, S., & Tellis, G. J. (2014). Mining marketing meaning from online

chatter: Strategic brand analysis of big data using latent dirichlet allocation.

Journal of Marketing Research,, 51(4), 463-479.

• Van Auken, S. (2015). From consumer panels to big data: An overview on

marketing data development. Journal of Marketing Analytics, 3(1), 38-45.

• Xu, Z., Frankwick, G. L., & Ramirez, E. (2016). Effects of big data analytics and

traditional marketing analytics on new product success: A knowledge fusion

perspective. Journal of Business Research, 69(5), 1562-1566

Useful online resources • Introduction to Insight Maker v5

https://www.youtube.com/watch?v=xlYHEoer2SY

• Gene Bellinger Insight Maker Interactive simulation

https://www.youtube.com/watch?v=bW1qrhdWKj8

• Gene Bellinger Casual loop diagram

https://www.youtube.com/watch?v=EZrtwGXpHXA

• Gene Bellinger Agent Based Models, Part 1

https://www.youtube.com/watch?v=WRlnhk-bfIg

• Gene Bellinger Insight Maker Equations

https://www.youtube.com/watch?v=DZyncQvjd_0

• CVEN1701 Environmental Principles and Systems - World3 Model

Demonstration on Insight Maker

• https://www.youtube.com/watch?v=-ogavZaHRZc

Reading Lists

Reading Lists provide direct access to all material on unit reading lists in one place.

This includes eReadings and items in Reserve. You can access the Reading List for this

unit from the link in MyLO, or by going to the Reading Lists page on the University

Library website.

Equipment, materials, software, accounts

Access to Excel, and software program Tableau (to be arranged with lecturer). Access

to Insight Maker (free web-based software).

Page 19 BMA708 Marketing Insights into Big Data

Activities

Learning expectations

The University is committed to high standards of professional conduct in

all activities, and holds its commitment and responsibilities to its

students as being of paramount importance. Likewise, it holds

expectations about the responsibilities students have as they pursue their

studies within the special environment the University offers.

Students are expected to participate actively and positively in the

teaching/learning environment. They must attend classes when

and as required, strive to maintain steady progress within the

subject or unit framework, comply with workload expectations,

and submit required work on time.

Details of teaching arrangements

On-campus students

There will be 13 x 3 hour workshops for on-campus students held each week

during the semester. The workshops will comprise a lecture and practical

activities. The lecture component will be recorded and made available on MyLO.

The program of content for each workshop and any materials and pre-workshop

preparation will be made available on MyLO each week. All on-campus students

are required to attend the weekly workshop.

Distance/ online students

Distance students have access to the recorded lectures and are expected to listen

to the weekly lecture which will be posted on MyLO shortly after the live

workshop finishes. Students enrolled in distance mode are expected to undertake

the workshop activities as self-directed learning. Students will have access to a

weekly workshop activity podcasts presented by the Unit Coordinator each week

via MyLO. The podcast will outline the key points for working through the

activities. Note that the set times for this subject if taught online or web-based

sessions for this unit, will mirror that of on-campus availability however the Unit

Coordinator may elect to occasionally run other live sessions (if required), or

change the time in which live sessions run for reasons of convenience or

technological demands (avoiding high demands of the Internet during the day.

Distance students are welcome to contact the Unit Coordinator for individual

assistance.

Specific attendance/performance requirements

In this unit, your active engagement will be monitored in the following way:

1. Your attendance at the weekly workshops (on-campus students)

OR Your online interaction with the Unit Coordinator

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2. Submitting Assessment 1 in week 4.

Teaching and learning strategies

The University is committed to a high standard of professional conduct in all

activities, and holds its commitment and responsibilities to its students as being

of paramount importance. Likewise, it holds expectations about the

responsibilities students have as they pursue their studies within the special

environment the University offers. The University’s Code of Conduct for

Teaching and Learning states:

Students are expected to participate actively and positively in the

teaching/learning environment. They must attend classes when and as

required, strive to maintain steady progress within the subject or unit

framework, comply with workload expectations, and submit required work on

time.

During the first four weeks of this semester, your participation and engagement in

this unit will be monitored. If you do not demonstrate evidence of having engaged

actively with this unit by Week 4 of semester, your enrolment may be cancelled or

you may be withdrawn from the unit.

Work Health and Safety (WHS)

The University is committed to providing a safe and secure teaching and learning

environment. In addition to specific requirements of this unit you should refer to the

University’s Safety and Wellbeing webpage and policy.

Communication

TO KEEP UP WITH ANNOUNCEMENTS REGARDING THIS UNIT

Check the MyLO Announcement tool at least once every two days. The unit

Announcement will appear when you first enter our unit’s MyLO site. Alternatively,

click on the Announcement button (towards the top of the MyLO screen) at any time.

WHEN YOU HAVE A QUESTION

Other students may have the same question that you have. Please go to the Ask the

Class Discussion forum on the unit’s MyLO site. Check the posts that are already there

– someone may have answered your question already. Otherwise, add your question as

a new topic. Students are encouraged to support each other using this forum – if you

can answer someone’s question, please do. We will attempt to respond to questions

within 48 business hours. If your question is related to a personal issue or your

performance in the unit, please contact the appropriate teaching staff member by

email instead.

WHEN YOU HAVE AN ISSUE THAT WILL IMPACT ON YOUR STUDIES OR THE

SUBMISSION OF AN ASSESSMENT TASK

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If you have a personal question related to your studies or your grades, please contact

teaching staff by email.

For general questions about the unit, please add them to the Ask the Class Discussion

forum on the unit’s MyLO site. This way, other students can also benefit from the

answers.

A NOTE ABOUT EMAIL CORRESPONDENCE

You are expected to check your UTAS email (WebMail) on a regular basis – at least

three times per week. To access your WebMail account, login using your UTAS

username and password at https://webmail.utas.edu.au/.

You are strongly advised not to forward your UTAS emails to an external email service

(such as gmail or Hotmail). In the past, there have been significant issues where this

has occurred, resulting in UTAS being blacklisted by these email providers for a period

of up to one month. To keep informed, please use your UTAS email as often as

possible.

We receive a lot of emails. Be realistic about how long it might take for us to respond.

All emails are answered during the five-day working week.

Concerns and complaints

The University is committed to providing an environment in which any concerns and

complaints will be treated seriously, impartially and resolved as quickly as possible.

We are also committed to ensuring that a student may lodge a complaint without fear

of disadvantage. If you have a concern, information about who to contact for

assistance is available on the ‘How to resolve a student complaint’ page.

Learning support

The University provides a range of face-to-face and online services to help equip

students with the academic and literacy skills that they need to undertake their study.

These services are in addition to the support you receive in each unit from unit

coordinators, lecturers and tutors. For details of these additional services such as

workshops, individual consultation for learning advice, and peer assisted learning

opportunities, please visit https://www.utas.edu.au/students/learning.

The University also provides free access to Studiosity, 24/7 online study help for all

UTAS students, enabling them to get feedback on written work within 24 hours or

chat live with a subject specialist anywhere and anytime.

All direct assessment-based feedback is provided only from the staff teaching you the

unit.

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Further information and assistance

If you are experiencing difficulties with your studies or assignments, have personal or

life-planning issues, disability or illness which may affect your course of study, you are

advised to raise these with the unit coordinator in the first instance.

In addition to Learning Support, there is a range of University-wide support services

available to you including Student Advisers, Disability Services, and more which can

be found on the Study Support and Resources and Safety, Health and Wellbeing pages

from the Current Students portal of the University website.

Should you require assistance in accessing the Library, visit their website for more

information.

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Unit schedule

WEEK DATE

BEGINNING TOPIC/ MODULE/

FOCUS AREA ACTIVITIES RESOURCES/

READINGS/

FURTHER

INFORMATION

1 13th July

Course Introduction: Measurement and modelling theory.

View introductory lecture and readings.

2 20th July

Presenting data with PivotTables

Refer to MyLO for this week’s workshop activities

Winston

(2014)

chapter 1

3 27th July MS Excel data

handling and graphs Refer to MyLO for this week’s workshop activities

Winston (2014)

chapter 2

4 3rd August

Using Excel

functions to

summarise marketing

data

Refer to MyLO for this week’s workshop activities

Winston (2014)

chapter 3

Assignment 1 due 7th August, 4.00 p.,m.

5 10th August

Data Visualisation theory

Introducing Tableau

Software

Refer to MyLO for this week’s workshop activities

Tableau online video training

6 17th August

Combining data sources, Metadata

Visualizing events

over time

Refer to MyLO for this week’s workshop activities

Tableau online video training

Mid-semester break (move to appropriate time)

7 7th September

Social Media and Web traffic tools

Google Analytics in Tableau

Refer to MyLO for this week’s workshop activities

Tableau online video training

8 14th September

Data calculation and

advanced charts in

Tableau

Refer to MyLO for this week’s workshop activities

Tableau online video training

Page 24 BMA708 Marketing Insights into Big Data

WEEK DATE

BEGINNING TOPIC/ MODULE/

FOCUS AREA ACTIVITIES RESOURCES/

READINGS/

FURTHER

INFORMATION

9 21st September

Summarising Sales over time Forecasting Sales in Tableau

Forecasting in Excel

Refer to MyLO for this week’s workshop activities

Tableau online video training

Assignment 2, due 25 September, 4.00 p.m

10 28th September

Complex Systems: Agent-based models and Dynamic Systems models

Refer to MyLO for this week’s workshop activities

Insight Maker software for building simulations

11 5th October

Dynamic Systems models: Stocks, Flows & Feedback Loops

Refer to MyLO for this week’s workshop activities

Insight Maker software for building simulations

12 12th October Social Network

Mapping Refer to MyLO for this week’s workshop activities

Overview of

techniques and

approaches in this

area

13 19th October Revision: The future

of Big data and

Marketing analytics

Refer to MyLO for this week’s workshop activities

What the

future has in

store for us,

maybe.

Assignment 3 due 25th of October, 4.00 p.m.

Page 25 BMA708 Marketing Insights into Big Data

ACCREDITATION

AACSB Accreditation

The Tasmanian School of Business and Economics (TSBE) is currently in the process of

applying for business accreditation with the Association to Advance Collegiate Schools

of Business (AACSB) – the lead program for accrediting business schools globally.

AACSB seeks to connect educators, students, and business to achieve a common goal –

to create the next generation of business leaders.

By joining AACSB and going through the accreditation process, TSBE is joining a

global alliance committed to improve the quality of business education around the

world, and to share the latest innovations in business education. Gaining Business

Accreditation with AACSB is a multi-year process involving TSBE demonstrating our

performance against the 15 accreditation standards.

Once complete, TSBE will join a select community of accredited business schools, with

only 7% of all business schools globally having completed the AACSB process. This

will further enhance the reputation of TSBE, and further enhance the global

recognition of your qualifications. To find out more about AACSB click here.