MIS771 – Descriptive Analytics and Visualisation

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Penalties for late submission: The following marking penalties will apply if you
submit an assessment task after the due date without an approved extension: 5%
will be deducted from available marks for each day up to five days, and work that is
submitted more than five days after the due date will not be marked. You will
receive 0% for the task. ‘Day’ means calendar days or part thereof. The Unit Chair
may refuse to accept a late submission where it is unreasonable or impracticable to
assess the task after the due date.
For more information about academic misconduct, special consideration, extensions,
and assessment feedback, please refer to the document Your rights and
responsibilities as a student in this Unit in the first folder next to the Unit Guide of
the Resources area in the CloudDeakin unit site.

The assignment uses the file mdcb.xlsx, which can be downloaded from CloudDeakin. Analysis of the data
requires the use of techniques studied in Module 2.
Assurance of Learning
This assignment assesses following Graduate Learning Outcomes and related Unit Learning Outcomes:
Graduate Learning Outcome (GLO) Unit Learning Outcome (ULO)
GLO1: Discipline-specific knowledge and
capabilities – appropriate to the level of
study related to a discipline or profession.
GLO3: Digital Literacy – Using technologies to find,
use and disseminate information
GLO5: Problem Solving – creating solutions to
authentic (real-world and ill-defined)
problems.

ULO 1: Apply quantitative reasoning skills to solve
complex problems.
ULO 2: Use contemporary data analysis and
visualisation tools and recognise the
limitation of such tools.

Feedback before submission
You can seek assistance from the teaching staff to ascertain whether the assignment conforms to
submission guidelines.
Feedback after submission
An overall mark together with suggested solutions will be released via CloudDeakin, usually within 15
working days. You are expected to refer and compare your answers to the suggested solutions to
understand any areas of improvement.

MIS771 – Descriptive Analytics and Visualisation Trimester 1, 2019

Page 3 of 10
Case Study (Background to Mad Dog Craft Beer)
Mad Dog Craft Beer, is an Australian micro-brewery company with less than
fifteen years of experience in brewing ale. Despite its limited operations in
Melbourne and regional Victoria, the company has experienced fast growth in
its production and sales in the past couple of years. In 2018, the company
reported increasing its brewing capacity to 3 million litres per year to meet the
increasing demand of its pale ale beer.
Mad Dog Craft Beer sells pale ale to two market segments: a) pubs, bars and
restaurants and b) bottleshops. Their beer is sold to these market segments
either directly to the buyer or indirectly through a sales representative.
Despite their successful operations and solid financial turnovers in the last two years, Mad Dog Craft Beer
is forecasting a shift in business climate within the next five years. This is a result of the ever-increasing
popularity of craft beer among Victorians and emergence of micro-brewery culture in this region. Now
more than ever, Mad Dog Craft Beer management feels the need to ensure a strong relationship with its
customer base. In addition, they are planning to put in place a formal procedure to forecast their beer
production. This would help Mad Dog Craft Beer accurately project future supply and demand and adjust
production needs accordingly.
Subsequently, Mad Dog Craft Beer has approached BEAUTIFUL-DATA (a market research company) and
asked them to conduct a large-scale survey of their clientsto better understand the characteristics of Mad
Dog Craft Beer’s customers, and their repurchase intention.
Data Collection Process (Conducted by BEAUTIFUL-DATA)
To address Mad Dog Craft Beer’s concerns, BEAUTIFUL-DATA has contacted Mad Dog Craft Beer’s clients
and encouraged them to participate in an online survey. The collected data are then supplemented by
other information compiled and stored in Mad Dog Craft Beer’s datamarts and accessible through its
decision support system (DSS).
Primary Database (accessible via mdcb.xlsx file)
The primary database consists of 200 observations. There are three types of information in this database.
The first group of information comes from Mad Dog Craft Beer’s data warehouse and includes information
about the customer such loyalty in years, type, region, and distribution channel. The second group of
information relates to the customers’ perceptions of Mad Dog Craft Beer on nine attributes. Mad Dog
Craft Beer’s customers were asked to rate the company on nine attributes using a 1 – 10 scale. The third
group of information relates to quantity ordered and business relationships (e.g., number of bottles
bought from Mad Dog Craft Beer; and the likelihood of recommending Mad Dog Craft Beer to others).
A complete listing of variables, their definitions, and an explanation of their coding are provided in
mdcb.xlsx file.

MIS771 – Descriptive Analytics and Visualisation Trimester 1, 2019

Page 4 of 10
Your Role as a BEAUTIFUL-DATA Data Analyst Intern
You are a graduate student doing an internship at BEAUTIFUL-DATA. The research team manager (Todd
Nash, with a PhD in Data Science and a Master Degree in Digital Marketing) has asked you to lead the data
analysis process for the Mad Dog Craft Beer project and directly report the results to him. You and Todd
just finished a meeting wherein he briefed you on the primary purpose of the project.
Todd explained that a model should be built to estimate Order Quantity. Therefore, the first goal is to
identify critical factors that influence the quantity ordered. Todd is also interested in gaining more
profound insights into factors that predict the likelihood of current clients to recommend Mad Dog Craft
Beer’s products to others. The final goal is to construct a model which forecast Mad Dog Craft Beer’s Pale
Ale production in the upcoming four quarters. From these insights, Mad Dog Craft Beer will be in an
excellent position to develop plans for the next financial year.
Todd also allocated relevant research tasks and explained his expectations from your analysis in the
meeting. Minutes of this meeting are available on the next page.
Now, your job is to review and complete the allocated tasks as per this document.

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