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This report provides performance analysis of merchandise
made by Newcastle United Football Club (NUFC) with different products. As NUFC
needs to have sustainable business and growth prospect, it is important to
analyze its performance in terms of revenue generation. Dolles and Soderman
(2012) explored the need for having a framework to analyze and gain competitive
advantage in terms of promoting football club. Ularu et al. (2012) stated that
data analytics can help in improving understanding of the business and improve
it further. Data analytics provides necessary intelligence to make good
decisions. Vamplew et al. (n.d) explored the usage of football grounds and
promoting the game besides revenues. Merchandise is an important activity of
NUFC with respect to different sport products. Sapana, Padro and Turmo (2007)
studied football organizations and promoting business by performing data
mining. Data mining can help in extracting business trends or patterns that can
lead to leveraging of sustainable business. This report provides data analytics
and the insights related to business intelligence (BI) that can help in making
expert decisions by NUFC. Excel is the spreadsheet software used to analyze the
data of merchandise of different products promoted by NUFC. The data analytics
pertaining to merchandise data is used to have strategic decision making.For
preparing the Dashboard,I had to collect the data of the merchandise for the
last three years(ie.,2014-2016).I collected data and shortlisted it,which is
relevent to the products,channels and years.After the collection of data,I had
choosen different charts which could be best to represent my data
effectively.For representing the data on Dashboard,I have used different charts
like Area charts,Bar charts,Line charts and column charts.The following
sections provide more information on the business intelligence and critical
evaluation of the BI extracted using data analytics in Excel ans SAS.

 

 

                                                                 Theoretical
framework is important to have systematic approach in completing a project.

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Framework provides building blocks that can be used to provide better possible
solution. Youssef (2014) explored a framework that can be used to have
framework that can help in data analytics in making good decisions. Useful
insights can be obtained using data analytics. Augustine (2014) studied and
provided the advantages of data analytics. Data analytics are thus used to
analyze merchandise data of NUFC inorder to gain business intelligence.

 it is evident that the data theory and
conceptual model are explored to make a formal model. The tools and prototypes
that can help in making empirical data are used and then data analytics are
explored in order to have research findings. Koop (2005) studied the analysis of
economic data. Economic data needs to be analyzed in order to have correlations
hidden data.

In this report, data analytics
with merchandise data of NUFC is made and the business intelligence is studied
for making recommendations. The data is analyzed in terms of revenues, the
importance of location, gender, product and loyalty points. The data is
subjected to data analytics in Excel. Excel is one of the spreadsheet software
that can be used to store data in tabular format and analyze it in order to have
trends in the data. The rationale behind the usage of Excel is its simplicity
in the data storage and analysis. The columns of input data are type, product,
price, quantity, discount, sales and loyalty points. Different types of
products are used for data analysis. The quantity of sales, discounts and
location of sales besides the year of sales are considered for analysis.

Loyalty points mean the points are given to regular customers. For example, the
person purchased products multiple times the points are given to customers.

Functions are used in excel to solve the problems relating data analytics.

The concept of pivot table and
sliceres are used in order to have better data analysis. Pivot table provides
the summary of data that can help in making useful information in the form of
graphs. The graphical representation of data can help in understanding the
dynamics of NUFC performance in terms of merchandising football products. The
products are selling by the NUFC in different sources like Amazon,
Sportsdirect, Sportsdirect, Website and Store. To getting the most revenues and
least revenues through the products first we are finding the total revenues by
the each product. For that actually we are using the functions to calculate the
total revenues of each product.

Critical Analysis and
Justification of Data Analytics and BI:

Data collection and data analytics
are very useful with Excel software. As explored by Barga et al. (n.d) many
problems can be solved using spreadsheet. There are different ways of getting
data analytics from Excel file. Berhe et al. (2007) opined that regression
analysis is widely used in data analytics. Standard software programs like
Excel are used in order to have data analysis. Multivariate and singlevariate
analysis can help in making data analysis and obtain business intelligence.

Phillips-Wren et al. (2015) specified the data and its importance as it can be
used to derive business intelligence. That is the reason, the merchandise data
of NUFC is analyzed and BI is achieved in this report.

Revenue in different locations:

From the figure 1, It is evident
that the revenues related to football products are more in England. The results
reveal the trends in the sales of different products in different locations.

The least performance and highest performance are observed.As shown in the
figure revenue gradually decreases from 2014 to 2016 in all locations except in
England.The most of the revenue is generated only through the shoes and
football products in all locations.Finally,from the SAS and Excel reports
revenue(sales) comes from the products footballs and shoes.In the same fashion,
year wise quantity of sales of different products is shown in Figure 2.

Year wise sales through different
channels:

As shown in Figure 2, it is
evident that different channels are used to sell products. The EBay channel is
able to perform well in selling products. The least performance is recorded
with web site. With respect to the price of the products, the price of the
products sold in stores is more. Similarly,from the SAS and Excel the sales are
more in the Ebay compare to the other channels in all the three
years(2014-2016).In the same way the price of the products in different
channels is shown in Figure 3.

Price of products through
different means:

From the Figure 3,it is clear that
different channels have different prices for the products.Prices are high in
store and are low on website for all the products.The Amazon and Ebay shows
similar trends in pricing the products.Consequently,SAS and Excel reports also
shows the similar pricing for the products in different channels.Also the
discounts given by various channels for diffrerent years are shwon in the
Figure 4.

Discount of products in different
channels of revenue generation:

It is evident that there are more
discounts given to the channel web site. The products that are offered at least
and highest discounts are presented. This can help in understanding the BI and
the correlation among the least performing products in different locations and
making well informed decisions.

Loyality points through different
channels:

Figure 5,shows the loyality points
given to the both male and female by different channels from 2014 to 2016.The
highest loyality points was received by the male than the female from all the
channels.Overall,Website stays top by providing the highest loyality points for
both gendre in all the years.Where as,Amazon stays at the bottom for the Female
and sportsdirect for the Male.As well as, the Excel and SAS reports also
depicts the same trend for the different channels.                                                                    
                                                                                           Data
analysis is made on merchandise data of NUFC. The data analytics are made for
having comprehensive business intelligence. A dashboard is made in order to
have the information of business performance. The dashboard can help in
understanding the performance just by a glance. The information thus obtained
from business analysis can help in making correct business decisions. There are
many aspects of business data that is used to have analysis. The data
considered include revenues, products, promotions, locations, revenue
generation channels, price and discounts.

Revenue in different locations is
analyzed and understood that the revenue at different locations is different
for different products. Loyalty points are also considered at different
locations and years for different channels of making revenues. There was year
wise quantity of sales through different channels of making sales. There is
analysis of price of different channels as well. The price of stores is more
for different products. The sales of products are more with e-Commerce
applications. The results of analysis reveal that more sales are made and
revenues are generated through e-Commerce applications. The rationale behind
this might be the time and geographical convenience and the reduced pricing.

SAS Report Analysis:

With the use of relevant raw-data
available from the dashboard,SAS reports represent the mean,median and the key
performance Indicators of the sales,prices,channels and quantity of the
products.It also depicts the mininmum value,maximum value,and the standard mean
of each of the performance indicators.

From the  Figure 1,it clearly shows the sales are high
in the Northern Island compared to the other locations.In the Figure 2,the
sales of the shoes are higher compared to the other products in all
channels.The Figure 3,represents the Loyalty points given by channels are high
in website during the three year period.

In the line of issues identified
above,SAS report provides the graphical representation of the sales of the NUFC
Merchandise in different channels for the last three years(2014-2016).

This brings us to the
understanding of the most of the revenue is generated from England from all the
channels and also depicts shoes and football products were the highest sold
products in all the three years.

 

NUFC has getting the revenues
through different sports products related to football. They used different
resources to sell the products. The resources are Amazon,Ebay, Sportsdirect,
Website and Store. The products names are Bags/Hold alls, Football, Gloves,
Headwear and Shoes. From this conceptual framework we analyzed and provided in
the report. The data is synthesized in order to solve business analytics on the
data. Excel is used to store data in tabular format. The data set helped to
find the objectives. The objectives are finding most revenue through the
products and find the least revenue through the products. The results of
analysis reveal the performance of NUFC in terms of getting revenues through
the selling of products. From the dataset we are getting the values of each
product selling in the different sources like Amazon,Ebay, Sportsdirect,Website
and Store. From these values we are getting more revenue selling shoes and less
revenue getting by selling of headwear. The most revenue i.e. 485850 is getting
by NUFC to selling the product shoes. And the least revenue i.e. 2500 is
getting by NUFC to selling the product Headwear.

 From that most and least revenues in the data
analytics problem the following are taken as recommendations.

1. Focus on Bags/Hold alls which
are getting most revenue to selling to compare the others. From this product
NUFC getting more revenue in future.

2. At the same time to follow the
same standards to continue the revenues through the selling of the product
shoes as performing best as of now.

3. Take measures to improve the
other products as well. The product of Headwear getting less revenue to compare
the others as of now. So that put the most efforts and concentration to improve
the revenues.

4. From this way we find the
solutions for data analytics problems.

5. It is better to promote sales
by identifying the locations at which sales are low and the customer loyalty
needs to be considered for the same.

 

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