GSBS6002 Foundations of Business Analysis

Running head: FOUNDATIONS OF BUSINESS ANALYSIS
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Foundations of business analysis
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Institutional Affiliation
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Foundations of business analysis
Executive summary
The report presents the research conducted that aims to address the causes of poor
performance in departmental stores that have made the organisation to lag in the market due to
the online retailers. The report relied on the online survey questionnaires to collect data from the
organisations database where the participants were sampled randomly. Through the formulation
of the hypothesis, the report sought to address the research questions using the tests for the
hypothesis. The techniques selected for testing the formulated hypotheses are the linear
regression analysis, correlation analysis, and the descriptive statistics. From the results, it is
evident that the current level of customer satisfaction does not reflect the managers’ goals and
targets of the customer satisfaction. The report recommends that the factors, which should be
implemented, are the provision for the shopping assistances, provision of the platforms for the
customers to compare the prices, and allowing the cashback for the customers among other
aspects as drafted in the recommendation section.

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TABLE OF CONTENTS
Introduction....................................................................................................................................5
The design of research...................................................................................................................6
Data sampling and collection....................................................................................................6
Hypothesis development................................................................................................................7
Research questions and the associated hypotheses.................................................................7
The selection of statistical techniques and justification.............................................................8
Results, statistical, and non-statistical interpretation..............................................................10
Descriptive statistics................................................................................................................10
A correlation between the Satisfactions of male customers.................................................12
Cluster plot...........................................................................................................................13
The impact of level of income on the customer satisfaction.................................................14
Examining the impact of age group on the customer satisfaction.......................................15
Examining the impact of the provision of shopping assistance to customer satisfaction..16
Examining the convenient return policy towards customer satisfaction............................17
Examining the impact of facilities pertaining to physical verification to customer
satisfaction................................................................................................................................18
The ability to compare prices in relation to customer satisfaction.....................................19
Impact of the ability of cashback and other facilities to customer satisfaction.................20
The summary of the statistical analyses and results.................................................................20

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Recommendations........................................................................................................................21
References.....................................................................................................................................23

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Introduction
The foundations to success in any business organisation are centred to concepts and
practices that facilitate the customer satisfactions (Kumar and Reinartz, 2016, pp.36-68).
Mangers and the relevant leadership in different organisations have been reported to put in great
efforts to meet the customer demands. However, this has been pausing a great challenge due to
the dynamic consumer needs that have been changing from time to time. The organisations have
therefore reverted to lay in place the strategic practices that would oversee the satisfaction of
their clients as well as enable them to compete sustainably in the market. As a result, stiff
competition has been precipitated among the companies supplying similar products and services
via various means to their customers.
The report presents an analysis of an Australia department store, Myer and David Jones
enterprise that faces stiff competitors from the online retailers who are reported to be more
reliable and convenient to the consumers (Prebensen, Kim, and Uysal, 2016, pp.934-945). For
the departmental stores to level up in the market performance, the organisation conducts a
research study that would help it address the key areas that would enable quality performance
through customer satisfaction. The objective of the analysis is to identify the poor performing
areas in the service to the customers by considering their preferences that build on the
convenience to the service delivery. The analysis will also seek to address the challenges facing
the departmental stores over the online vendors and the ways of solving these problems through
effective decision making.

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The design of research
Data sampling and collection
The use of online questionnaires was deployed for collecting the responses from the
participants who were the customers to the organisation (Stoet, 2017, pp.24-31). The online
survey questionnaires were preferred due to the shorter time taken for collecting data from
different regions/ cities across the country. Additionally, the online survey is cost effective as
well as has lower chances of biases whereby the respondents are given the privilege to express
themselves without the sense of being judged by the interviewer as illustrated by (Brace, 2018).
These respondents were selected from the organisation’s database using the random sampling
techniques that was preferred over other techniques which is believed to cover a large
geographical data samples through the random selections.
However, the questions for the survey were precisely designed to address the research
questions and the hypothesis, which were brief and easy to understand (Rubsamen, Akmatov,
Castell, Karch, and Mikolajczyk, 2017, p.4). Ten questions that had interval answers that were
rated on a scale of 1 to 10 were used to ensure that the respondents use the minimal time to fill
the survey thus encouraging more participating. Through the random sampling techniques, 1000
respondents were selected where 400 completed the survey questions that were considered for
analysis. During the implementation of this method of data collection, the researcher observed
the ethics of research by protecting the privacy of the respondents (Sekaran and Bougie, 2016).
The structure of the questions identified the gender and the type of employment of the
respondents without provision of the names and identities. Additionally, the survey voluntarily
conducted where the respondents could opt out anytime they want without attaching any reasons

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or explanation. The researcher further ensured the participants have understood the purpose of
collecting data, which was attributed to improving the quality of services for the consumers.
Hypothesis development
The establishment of the hypotheses is aimed at aiding the researcher to conduct a
relevant study in line to the research questions. This ensures that there is consistency and
relevance in the data collection and analysis that would lead to reliable results. According to
(O'Sullivan, Berner, Taliaferro, and Rassel, 2016), the hypotheses also ensure that the researcher
does not go out of the scope of the research problems as outlined by. The following are the
research questions and their associated hypotheses, both null and alternative that were developed.
Research questions and the associated hypotheses
I. What are some of the reasons for poor performance reported among the departmental
stores in the recent years in Australia?
The following hypothesis focusses on the above research question
A. The null hypothesis (H0) is that the existing level of satisfaction is similar to the
target of the management that is the score above seven whereas the alternate hypothesis (HA)
states that the existing level of customer satisfcation is not similar to the target of the
management which is the score above seven.
B. The null hypothesis states that the overall customer satisfaction are different
between the female and the male customers while the alternative hypothesis states that the
overall customer satisfaction are not different between the female and the male customers.

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C. The null hypothesis states that the amount of income has an influence on the
overall customer satisfcation while the alternative hypothesis states that the amount of
income has no influence on the overall customer satisfcation while the alternative.
D. The null hypothesis states that there exists differences in satisfcation of customers
basing on the age groups while the alternative hypothesis states that there exists no
differences in satisfcation of customers basing on the age groups.
II. How could the departmental stores compete their online competitors?
The focus on this research question is based on the following hypothesis.
E. The null hypothesis states customer satisfaction is determined by the effective
policies of return, provision of assistance during products purchase, the platform for
comparing the prices, facilitating verification of products through physical provisions, and
allowing cashbacks for the customers. However, the alternative hypothesis states that the
customer satisfaction is not determined by the effective policies of return, provision of
assistance during products purchase, the platform for comparing the prices, facilitating
verification of products through physical provisions, and allowing cashbacks for the
customers
The selection of statistical techniques and justification
The report presents different statistical techniques that were used in the testing of the
hypothesis. The selection of the tools was determined by the nature of the expected/ anticipated
outputs and the relationship to be determined between different variables.
To test the first hypothesis, the use of descriptive analysis will be used to determine the
inferences between the data samples (Zook and Pearce, 2017, pp.43-71). The frequency

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distribution of the two set of the scores will be established whereby the first set will be all the
scores that range between one and six while the second set will be the scores that range between
seven and ten for customer satisfaction. By using this technique, it will be possible to determine
whether the hypothesis is true or false through the frequency distribution. Additionally, using the
descriptive statistics technique will enable the determination of the mean and the standard
deviation of the scores for the two classes identified in the hypothesis. This will provide an easier
way to justify if the organisation has achieved the customer satisfaction goals.
However, the use of correlation technique and scatter plots will be used to determine the
differences in satisfaction of the male and the female customers in the second hypothesis. The
data will be arranged in two sets for the male and female customers whereby a correlation test
will be run to establish the relationship in the satisfaction for the two sets of data. The correlation
coefficient R will illustrate the strength of the correlation in the data sets, which range between -
1 and 1 as illustrated by (Darlington and Hayes, 2016). When the range lies within the
boundaries of one within this range, it will depict that there is no differences in satisfaction of the
female and the male customers since the variables are strongly correlated towards the positives.
When the correlation coefficient does not lie within the one boundaries, it would imply that the
variables have no correlation thus indicating that there is a difference in the satisfaction of the
male and the female customers.
In the third and fourth hypotheses, the use of regression analysis will be deployed in the
efforts to determine the rate of income and the age factor relate to the satisfaction of the
customers. The regression analysis will be carried out independently for determining the
existence of the relationship between the customer satisfaction and these variables. The
regression output will establish the relation between the sets of data that would be vital for

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drawing the conclusions concerning theses hypotheses. The Multiple R coefficient will be used
to determine how the trends in the data sets are related on a scale of zero to one. If the coefficient
is one, it implies the independent and the dependent variables have a relationship while the zero
output will indicate there is no relationship between the variables. Additionally, the evaluation
using the evaluation of the P-value, the probability of existence of a correlation between the sets
of data determined with the aid of the significance value F. During the analysis using these
techniques, when the P-value and the significant value F are high, it implies that the values of the
dependent variable can be altered or predicted with the changes in the independent variable. This
would imply the customer satisfaction is dependent on the age factor and the level of
employment among the consumers.
Additionally, the fifth hypothesis can be verified using the regression analysis to draw the
existence of a relationship between the variables such as the shopping assistance, return policies,
on-site verification of products, the ability to compare prices, and cashbacks in relation to
customer satisfaction. This technique can be justified due to its simplicity in the analysis of bulk
data within a short time. The explanation to the output of this technique is provided in analysis of
third and fourth hypotheses.
Results, statistical, and non-statistical interpretation
Descriptive statistics
To determine whether the organisation has achieved the overall satisfaction goals, the
data was sorted into two categories where the frequencies of each data set was recorded as shown
below. The range of 7-10 was considered as the met goals by the management of customer
satisfaction while the range from 1-6 indicated that the customer satisfaction do not meet the set

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goals. The frequency column in the table indicate the number of customers whose satisfaction is
indicated in the adjacent column by the customer satisfaction intervals.
Customer satisfaction
Intervals
Frequency Relative frequency Percentage relative
frequency
1-6 358 0.895 89.5
7-10 42 0.105 10.5
The information from the table can be presented using a pie chart and a bar graph as shown
below using the percentage cumulative frequency.
Pie chart
90%
11%
Percentage cumulative frequency
Not satisfied
Satisfied

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Bar graph
N o t s a ti s fi e d S a ti s fi e d
0
10
20
30
40
50
60
70
80
90
100
The % cumulati ve frequency
Percentage cumulative frequency
From the data in the table, chart, and the bar graph, it is evident that satisfaction of the
customers as required by the managed is approximately 11 per cent. This indicates that 89% of
the customers from the obtained sample are not satisfied with the provision of the services and
products by the departmental store. This result indicates that the first hypothesis will be rejected
since the current customer satisfaction does not meet the goals of the management of the score of
7 to 10.
A correlation between the Satisfactions of male customers
The correlation coefficient can be run in MS Excel using the formula “=CORREL (Array F,
Array M)” then “enter” where the arrays represents the range of the customer satisfaction for the
female and female consumers respectively. From the results obtained, the correlation coefficient
R was obtained as 0.066748. For the Excel formula to return a result, the number of the variables
in each data set must be equal. However, from the data provided, there were six more female

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customers than male, therefore, the last six female were excluded from the correlation analysis
according to their identification.
Cluster plot
From the correlation, the following graph is a scatter plot drawn from the data samples
0 1 2 3 4 5 6 7 8
0
1
2
3
4
5
6
7
8
Cluster plot for cutomer satisfaction for male and
female consumers
From the results, the correlation coefficient (r = 0.066748) indicates that there exists no
difference in the satisfaction of the male and the female customers as stated in the hypothesis.
There is a big difference of the correlation coefficient from the score of one thus illustrating the
weak relationship among the variables. This is further proven by the scatter plots where the
scatter points does not indicate any patterns where the line of best fit can be established. These
reasons identified does not support the hypothesis thus it will be rejected thus, the alternative
hypothesis will be used.

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The impact of the level of income on the customer satisfaction
To examine this hypothesis, the level of income was taken as the independent variable
while the customer satisfaction as the independent variables in the regression analysis. The
results are shown below.
From the regression output, it is observed that the value of the Pearson’s coefficient R
indicates a weak relationship between the independent and the dependent variable. This indicates
that there is minimal impact on the level of income on the satisfaction among the customers.
Additionally, the coefficient of determination r2 is 0.003134, which implies that only 0.3124% of
points fall along the regression line. This is an indication that nearly zero points are along the
regression line. Therefore, the level of income does not reflect on the degree of customer
satisfaction in this case.

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Examining the impact of age group on the customer satisfaction
The age group was considered as the independent variable while the customer satisfaction
is the independent variable. The regression output is shown below.
From the output, we can depict that there is some linear regression relationship between
the age group and the customer satisfaction indicated by the determination coefficient r2, where
17.91% points fall along the regression line. However, this is considered as a weak correlation
between the variables indicated by the Pearson’s correlation coefficient result of 0.4233.
Therefore, the age group variable could be used in the study although it cannot be fully be relied
upon.

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Examining the impact of the provision of shopping assistance to customer satisfaction
This was run on MS Excel using the regression technique where the scores of customer
satisfaction were considered as the independent variable while the overall customer satisfaction
as the independent variable. The results are displayed below.
The results indicates some relationship between the variables with only 32.7% of the
samples falling along the regression line. The Pearson’s correlation coefficient is above average
thus the correlation between the variables can be relied upon in this hypothesis.

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Examining the convenient return policy towards customer satisfaction
To obtain the output, the scores for the convenient on return policy were identified as the
independent variable while the customer satisfaction as the dependent variable. The results are
displayed as shown below.
The results indicate that the convenient return policy variable does not correlate with the
customer satisfaction from the regression analysis.

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Examining the impact of facilities pertaining to physical verification to customer
satisfaction
He scores from the online survey for the physical verification of the products were
identified as the independent variables in the analysis while the overall customer satisfaction as
the independent variable. The output is displayed below.
The regression results indicate a weak relationship between the variables in the data sets.
This is due to the coefficient of determination, which illustrates that only 21.65% of the sample
point’s fall along the regression line with a progressive low correlation of 0.465293 indicated, by
the Pearson’s coefficient.

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The ability to compare prices in relation to customer satisfaction
The ability to customer satisfaction in the ability to compare the prices was the
independent variable while the overall customer satisfaction was the independent variable. The
results are displayed below.
The output indicates that the customer satisfaction can be altered by the ability to
compare the prices by the customers. This is due to the Pearson’s coefficient that indicates a
correlation between the variables. Additionally, 32.91% of the sample points fall along the
regression line.

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Impact of the ability of cashback and other facilities to customer satisfaction
In this analysis, the independent variables was the customer satisfaction in the cashback
ability while dependent variable was the overall customer satisfaction. The regression output is
displayed below.
The output results indicate that the ability of cashback for the customers has minimal
impacts on the overall satisfaction of the customers. This is indicated by the determination
coefficient, which shows that only 13.49% of the points lie along the regression line from the
analysis.
The summary of the statistical analyses and results
From the interpretation of the results, it is evident that the organisation has not met the
customer satisfaction that is lagging at 11%. Additionally, there is no difference in meeting the

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customer demands of the male and the female consumers. On the other side, the level of income
does not have any impact on the satisfaction of the customers as depicted in the hypothesis.
These factors therefore, does not contribute in the process of solving the causes of poor
performance of the organisation and fostering competition among the online vendors.
However, the factors such as the age group, the provision of the shopping assistance for
the customers, the ability to cashback among other physical facilities, the platform that allows
comparison of prices among the goods and products, and the provision of abilities to verify the
goods on delivery are the factors that influence satisfaction of the departmental stores’
customers. The analyses further indicated the existence of the correlation between the above
aspects with the customer demands. Therefore, these aspects should be clearly examined as
recommended below.
Recommendations
For customer satisfaction to be achieved, it calls for the collaboration of different aspects
that should be addressed to carter for the changing demands in the market. The departmental
stores will continue to lag in the market shares if they do not address the following aspects. The
managers should device platforms that enable the customer to compare prices. This means the
organisation should be in a position to diversify the good and products in the market.
Additionally, the provision of the shopping assistance during the purchase should be
implemented and the physical verification of the products as it boosts the confidence level of the
customers concerning the products. The assistant will be helpful in answering the customer
questions on the products thus leading to their satisfaction. Moreover, the ability to cashback
among other facilities will create a sense of trust between the business and the organisation thus

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establishing a lasting relationship that facilitates customer satisfaction. Should these factors be
achieved, the organisation is likely to register improved performance due to quality products and
services delivery to the customers (Hill and Brierley, 2017). Concisely, this will put the
organisation in a great position to compete with the online retailers.

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References
Brace, I. (2018). Questionnaire design: How to plan, structure and write survey material for
effective market research. London. Kogan Page Publishers.
Darlington, R. B., & Hayes, A. F. (2016). Regression analysis and linear models: Concepts,
applications, and implementation. New York. Guilford Publications.
Hill, N., & Brierley, J. (2017). How to measure customer satisfaction. London. Routledge.
Kumar, V., & Reinartz, W. (2016). Creating enduring customer value. Journal of
Marketing, 80(6), 36-68.
O'Sullivan, E., Berner, M., Taliaferro, J. D., & Rassel, G. R. (2016). Research methods for
public administrators. London. Routledge.
Prebensen, N. K., Kim, H., & Uysal, M. (2016). Cocreation as moderator between the experience
value and satisfaction relationship. Journal of Travel Research, 55(7), 934-945.
Rubsamen, N., Akmatov, M. K., Castell, S., Karch, A., & Mikolajczyk, R. T. (2017).
Comparison of response patterns in different survey designs: a longitudinal panel with
mixed-mode and online-only design. Emerging themes in epidemiology, 14(1), 4.
Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach.
Chicago. John Wiley & Sons.
Stoet, G. (2017). PsyToolkit: A novel web-based method for running online questionnaires and
reaction-time experiments. Teaching of Psychology, 44(1), 24-31.
Zook, K. L., & Pearce, J. H. (2017). Quantitative descriptive analysis. In Applied Sensory Analy
of Foods (pp. 43-71). London. Routledge.