Business Computing Case Study Report Order Instructions: please read Business Report Assignment Specification – Sem 2 2016 doc
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Business Computing Case Study Report Sample Answer

BUSINESS COMPUTING CASE STUDY REPORT

• …………………………………………………………………………………………………………….2
• Data Analysis Results……………………………………………………………………………………………….3
• Discussion of the Data Analysis Results……………………………………………………………………..6

1.3.1.         Discussion of Factors Affecting the Results…………………………………………………7

• Concluding Remarks………………………………………………………………………………………………..7
• ……………………………………………………………………………………………………7
• Reference List…………………………………………………………………………………………………………..8
• Introduction

Data visualization 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 visualization 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 flavor, 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.

• 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 analyzed with regards to three broader aspects of the drinks concerning the marketing potential for each flavor, the different retailers and the different regions.

Table 1: A Table Showing the Number of Drinks Sold per Flavour (number of 300 ml cans)

Figure 1: A Bar Chart Showing the Number of Drinks Sold per Flavour (number of 300 ml cans)

Table 2: A Table Showing the Number of Drinks Sold in Different Regions (number of 300 ml cans)

Figure 2: A Pie Chart Showing the Number of Drinks Sold in Different Regions (number of 300 ml cans)

Table 3: A Table Showing the Number of Drinks Sold by Different Retailers (number of 300 ml cans)

Figure 3: A Column Chart Showing the Number of Drinks Sold by Different Retailers (number of 300 ml cans)

Table 4: A Table Showing Sales Trends over the Trial Period (number of 300 ml cans)

• Discussion of the Data Analysis Results

The marketing potential of these drinks for each flavor 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).

• 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.

• Concluding Remarks

In conclusion, it is important to emphasize that the 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:

1. Increase the production of Almond Passion, Mango Delight and Triple Shot Expresso flavours because sales seem to be higher compared to the rest.
2. 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.
3. Expand distribution in the Victoria and Tasmania regions where low sales are recorded and distribution cost is lower and compared to other regions due to their proximity to the central distribution warehouse.

Business Computing Case Study Report 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