Course Title & Number: Business Computing 1 & ISYS2056
Workshop Time:
Workshop Tutor’s Name:
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Business Computing Case Study
Name: …………………… ID Number: ………………………………Page 2
Table of Contents
1.1.
Introduction……………………………………………………………………………………………………………..2
1.2. Data Analysis
Results……………………………………………………………………………………………….3
1.3. Discussion of the Data Analysis
Results……………………………………………………………………..6
1.3.1. Discussion of Factors Affecting the Results…………………………………………………7
1.4. Concluding
Remarks………………………………………………………………………………………………..7
1.5.
Recommendations…………………………………………………………………………………………………….7
1.6. Reference
List…………………………………………………………………………………………………………..8
Business Computing Case Study
Name: …………………… ID Number: ………………………………Page 3
1.1 Introduction
Data visualisation is without any doubt one of the most powerful business analytics tools that
play a fundamental role in enabling the process of effective decision making in business
(Cleveland, 2014). Thus, according to Tufte (2013) and Stephen (2015), data visualization in
business allows abstract information to be graphically displayed for two purposes: data analysis
or sense-making and communication of meaningful insights obtained to enable informed
decision making (Liff & Posey, 2013; Robbins, 2015). Effective visualization makes complex
data more understandable, accessible and usable; while helping users to carry out analysis and
reasoning about data and the evidence it provides mostly through comparative representation of
the data using tables and charts to show patterns, trends or relationships between variables of the
data (Carr & Pickle, 2012; Post, Nielson & Bonneau, 2012; Friendly, 2014).
The purpose of this business report is to use varied data visualisation techniques to represent the
sales data of the number of cans of drinks sold over a trial period of 4 months. In particular, the
report is directed to Colin who is interested in expanding to non-alcoholic beverages and analysis
of the trial period results done and, advice provided to him based this analysis concerning the
marketing potential for each flavour, the different retailers and the different regions. The report
also provides the sales trends over the trial period, as well as other factors worthy consideration
when the results are interpreted, and finally providing advice on other factors that may need to be
considered prior to making final decisions concerning the ongoing trial period sales of the
‘mixer’ drinks.
1.2 Data Analysis Results
Analysis of the sales data contained in the spreadsheet is carried out in tables and charts to
visually represent the data in a more understandable and meaningful manner. The sales data is
analysed with regards to three broader aspects of the drinks concerning the marketing potential
for each flavour, the different retailers and the different regions.
Business Computing Case Study
Name: …………………… ID Number: ………………………………Page 4
Table 1: A Table Showing the Number of Drinks Sold per Flavour (number of 300 ml cans)
FLAVOUR TOTALS
ALMOND PASSION 1975834
CRANBERRY WIZZ 525519
LIME SODA 428008
MANGO DELIGHT 1027283.333
RASPBERRY SODA 247802
TRIPLE SHOT EXPRESSO 1244085
OVERALL TOTAL 5448531
Figure 1: A Bar Chart Showing the Number of Drinks Sold per Flavour (number of 300 ml cans)
05000001000000150000020000002500000
Number of Drinks Sold per Flavour
ALMOND PASSION
CRANBERRY WIZZ
LIME SODA
MANGO DELIGHT
RASPBERRY SODA
TRIPLE SHOT EXPRESSO
A Bar Chart Showing the Number of Drinks Sold per
Flavour
Business Computing Case Study
Name: …………………… ID Number: ………………………………Page 5
Table 2: A Table Showing the Number of Drinks Sold in Different Regions (number of 300 ml
cans)
REGION TOTALS
NSW 1395684
QLD & NT 1758854
VIC &TAS 934091
WA & SA 1359902
OVERALL TOTALS 5448531
Figure 2: A Pie Chart Showing the Number of Drinks Sold in Different Regions (number of 300
ml cans)
NSW,26%
QLD & NT,32%
VIC &TAS,17%
WA & SA,25%
A Pie Chart Showing the Number of Drinks Sold in
Different Regions
Business Computing Case Study
Name: …………………… ID Number: ………………………………Page 6
Table 3: A Table Showing the Number of Drinks Sold by Different Retailers (number of 300 ml
cans)
RETAILER TOTALS
THIRSTY CAMEL 1702809
CELLARBRATIONS 1150649
DAN MURPHY’S 2418035
DUNCAN’S 177038
OVERALL TOTALS 5448531
Figure 3: A Column Chart Showing the Number of Drinks Sold by Different Retailers (number
of 300 ml cans)
THIRSTY CAMELCELLARBRATIONSDAN MURPHY’SDUNCAN’S
Retailers
0
500000
1000000
1500000
2000000
2500000
3000000
A Column Chart Showing the Analysis of Sales by
Different Retailers
Business Computing Case Study
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Table 4: A Table Showing Sales Trends over the Trial Period (number of 300 ml cans)
Number of Drinks sold (number of 300 ml cans)
September October November December Totals
Mixer Flavour
ALMOND PASSION 439615 553068 455825 527326 1975834
CRANBERRY WIZZ 168453 150640 206371 55 525519
LIME SODA 300772 76109 37696 13431 428008
MANGO DELIGHT 224747 250997 261243 290296 1027283
RASPBERRY SODA 66372 71074 55374 54982 247802
TRIPLE SHOT
EXPRESSO
213894 310386 317038 402767 1244085
Region
NSW 366040 387425 336472 305747 1395684
QLD & NT 429141 467946 428128 433639 1758854
VIC &TAS 221655 230457 241504 240475 934091
WA & SA 397017 326446 327443 308996 1359902
Retailer
THIRSTY CAMEL 462963 455320 374882 409644 1702809
CELLARBRATIONS 252914 261046 271751 364938 1150649
DAN MURPHY’S 640078 655990 645582 476385 2418035
DUNCAN’S 57898 39918 41332 37890 177038
1.3 Discussion of the Data Analysis Results
The marketing potential of these drinks for each flavour, the different retailers and the different
regions is varied. For instance, the sales of each flavour are as follows: ALMOND PASSION
(1975834 Cans); TRIPLE SHOT EXPRESSO (1244085 Cans); MANGO DELIGHT (1027283
Cans); CRANBERRY WIZZ (525519 Cans); LIME SODA (428008 Cans); and RASPBERRY
SODA (247802 Cans) respectively. The first three flavours seem to have a significant potential
due to their high sales compared to the rest. In addition, the four different regions considered also
show varied results as follows: QLD & NT (1758854 Cans or 32%); NSW (1395684 Cans or
26%); WA & SA (1359902 Cans or 25%); and VIC & TAS (934091 Cans or 17%) respectively.
Furthermore, the four different retailers considered also show varied results as follows: DAN
MURPHY’S (2418035 Cans); THIRSTY CAMEL (1702809 Cans); CELLARBRATIONS
(1150649 Cans); and DUNCAN’S (177038 Cans).
Business Computing Case Study
Name: …………………… ID Number: ………………………………Page 8
1.3.1 Discussion of Factors Affecting the Results
It is evidently clear that, the results obtained concerning the drinks over the 4 month trial period
with regards to the marketing potential for each flavour, the different retailers and the different
regions may have been affected by a number of factors. First, the packaging of the ‘mixer’ drinks
only in 300 ml aluminium cans seems monotonous and may hinder purchasing convenience
among some buyers subsequently affecting the sales. Second, considering that these drinks are
advertised as ‘adult’ mixers and their sales is accompanied with cocktail recipes directly printed
onto the can may affect the sales results, since the sales of these drinks are envisaged to
correspond to the sales of alcoholic beverages irrespective of being non-alcoholic beverages.
1.4 Concluding Remarks
In conclusion, it is important to emphasize that marketing potential of the drinks is affected by
various factors including the flavour, chosen retailers, distribution regions and cost as well as the
packaging and advertising strategy. The sales trends also vary, and this means if all these factors
are appropriately balanced sales of these drinks can improve significantly.
1.5 Recommendations
From the analysis carried out in the report, there are three recommendations that are appropriate
for Colin to adopt:
- Increase the production of Almond Passion, Mango Delight and Triple Shot Expresso
flavours because sales seem to be higher compared to the rest. - Increase the advertising budget of the three drinks with low sales including Cranberry
Wizz, Lime Soda and Raspberry Soda to ensure that their sales improve. - Expand distribution in the Victoria and Tasmania regions where low sales are recorded
and distribution cost is lower and compared to other regions due their proximity to the
central distribution warehouse.
Business Computing Case Study
Name: …………………… ID Number: ………………………………Page 9
Reference List
Carr, DB & Pickle, LW 2012, Visualizing Data Patterns with Micromaps, Chapman and
Hall/CRC, Melbourne, VT.
Cleveland, WS 2014, The Elements of Graphing Data, Hobart Press, Melbourne.
Friendly, M 2014, A Brief History of Data Visualization, Springer-Verlag, Melbourne.
Liff, S & Posey, PA 2013, Seeing is Believing: How the New Art of Visual Management Can
Boost Performance Throughout Your Organization, AMACOM, Sydney. ISBN 978-0-
8144-0035-7
Post, FH, Nielson, GM & Bonneau, G-P 2012, Data Visualization: The State of the Art,
Springer-Verlag, Melbourne.
Robbins, NB 2015, Creating More Effective Graphs, Prentice-Hall, Sydney.
Stephen, F 2015, Fundamental Differences in Analytical Tools: Exploratory, Custom, or
Customizable, Prentice-Hall, Sydney.
Tufte, E 2013, The Visual Display of Quantitative Information, Graphics Press, Sydney. ISBN 0-
9613921-4-2.
