Descriptive Analytics and Visualisation

DESCRIPTIVE ANALYTICS AND VISUALISATION

DESCRIPTIVE ANALYTICS AND VISUALISATION
Contents
INTRODUCTION........................................................................................................... 3
1. DEPENDENT VARIABLES DESCRIPTION...................................................................4
1. MODELLING QUANTITY ORDERED.........................................................................6
2.1 FACTOR IDENTIFICATION..................................................................................... 6
2.2 MODEL BUILDING................................................................................................ 6
2.3 INTERACTION EFFECT.......................................................................................... 8
2. MODELLING LIKELIHOOD OF RECOMMENDATION..................................................9
3.1 MODEL................................................................................................................ 9
3.2 PREDICTED PROBABILITIES................................................................................ 11
3.3 PREDICTED PROBABILITIES VISUALIZATIONS.....................................................11
3. FORECASTING PRODUCTION................................................................................ 11
CONCLUSION............................................................................................................. 13
REFERENCES............................................................................................................. 14
APPENDICES.............................................................................................................. 16
APPENDIX 1............................................................................................................ 16
APPENDIX 2............................................................................................................ 17
APPENDIX 3............................................................................................................ 22
APPENDIX 4............................................................................................................ 24
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DESCRIPTIVE ANALYTICS AND VISUALISATION
INTRODUCTION
The dynamic nature of the modern business environment has made it necessary for businesses to
become dynamic as well (Kiechel, 2010; Sirajuddin, et al., 2017). Research, innovations
and technology have played a major role in making the modern business environment dynamic
(Aalst & Wil, 2011). Thus, for businesses to become dynamic, it is necessary to for them to
invest in research, innovations and technology. Among these three factors, research acts as a
foundation for the other two factors.
Through research, innovators get access to previously done work in the same field thereby
enabling them to develop better innovations (Albright & Winston, 2014). Also through
research, the optimal technology can be identified for a business process (Laudon & Guercio,
2014). Optimal in this case implying a technology that is efficient will still being up to date.
Hence research can be considered as vital for acquiring and maintaining competitive advantage
in the modern dynamic business environment.
Mad Dog Craft Beer is an Australian based brewery company. This micro-brewery has managed
to establish a competitive advantage with fast growth (both in terms of sales and production) in
its customer bases in Regional Victoria and Melbourne. The company is interested in
maintaining its competitive advantage especially with the increase in competition from new
micro-brewery within its customer bases.
This research paper aims at conducting a research on the Order Quantity and Recommendations
of Mad Dog Craft Beer’s pale ale beer. Inferences can be drawn and actionable
recommendations determined from the analysis on the two variables. By using the inferences and
recommendations, Mad Dog Craft Beer can establish policies to enable it maintain its
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DESCRIPTIVE ANALYTICS AND VISUALISATION
competitive advantage despite the rise in competition from new market entries. This paper will
also aim at developing a model for the forecasting of the production of Mad Dog Craft Beer’s
pale ale beer line. This forecasting model will enable the company to better plan their finances as
well as estimate the expected future profits.
The study paper will look at the descriptive statistics of the Order Quantity and Recommendation
variables, develop models for estimation of these two variables and develop a forecasting model
for the production of pale ale beer line.
1. DEPENDENT VARIABLES DESCRIPTION
The table, Table 1: Dependent Variable Summary Description, below gives the summary
description of the two dependent variables in this research study:
Table 1: Dependent Variable Summary Description
Variable Variable
Description
Nature of
Variable
Variable Type Measurement
Scale
Order_Qty Informs on the
number of pale
ale beers
bottles ordered
by a customer.
Numerical
Variable.
Dependent
Variable.
Ratio Scale.
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DESCRIPTIVE ANALYTICS AND VISUALISATION
Recommendation Informs on
whether a
customer
would
recommend the
pale ale beer to
others.
Categorical
Variable with
two levels; 0
represents No
and 1
represents Yes.
Dependent
Variable.
Nominal Scale.
From Table 3: Descriptive Statistics of Order_Qty and Recommend, we observe that
customers that purchase the pale ale beer are slightly more likely to recommend it to other
people than they are likely not to. 101 customers replied they would recommend the pale ale
beer to other people while 99 said they would not recommend it. The average order quantity
of the pale ale beer was 1.665 with a standard deviation of 0.8932 and mode of 7.2. The
lower quartile (Q1), median, Upper Quartile and Interquartile Range values for Order_Qty
were 7.1, 7.6, 8.2 and 1.1 respectively. The range for the Order_Qty data is 5.6 units. From
the histogram of the Order_Qty, Figure 1: Histogram of Order_Qty, we observe that the data
distribution can be described as skewed to the right (negatively skewed). This is also
confirmed from the value of Skewness = -0.2064 with a kurtosis of 0.5840.
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DESCRIPTIVE ANALYTICS AND VISUALISATION
1. MODELLING QUANTITY ORDERED
2.1 FACTOR IDENTIFICATION
In order to identify the possible factors that affect the quantity ordered variable, Order_Qty,
the Factor Analysis Method was applied. Factor Analysis is a multivariate analysis method
that is used in dimension reduction to obtain a reasonably acceptable number of variables for
analysis (Freedman, 2009; Vicenc, 2017). The Principal Analysis method reduces the
size of a set of independent variables to a small manageable size for the analysis of the
impact on the dependent variable (Han & Jaiwei, 2011; O'Neil & Schutt, 2013).
From Table 4: Eigen Values, we observe that the following factors had an Eigen value
greater than 1; Loyalty (3.810), Cust_Type (2.764), Region (1.722) and Dist_Channel
(1.081). Therefore, we consider the Loyalty, Cust_Type, Region and Dist_Channel as the
possible factors affecting the quantity ordered of the pale ale beer line of the Mad Dog Craft
company. This can also be seen from Figure 2: Scree Plot, where there is a point of inflection
at Eigen value 5, thus we consider factor 1 to 4.
2.2 MODEL BUILDING
The purpose of this model is to estimate the quantity ordered for the pale ale beer line of the
Mad Dog Craft Beer company. The model will consider the possible factors that affect the
Order_Qty variable in modelling the estimation of the quantity ordered for the pale ale beer
line.
The factors that are considered are the Loyalty, Cust_Type, Region and Dist_Channel. These
factors will form the independent variables while the Order_Qty forms the dependent
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