Analysis of the Store Data - Business Analytics

This report provides an analysis of the store data for a supermart chain, including gross profit, sales on Sundays, online sales, wastage, and factors affecting sales variability. The report concludes that advertising expenses, number of staff, and number of car spaces are the most effective factors in explaining sales variability.

Running Head: BUSINESS ANALYTICS
MIS171
Business Analytics
Name of the Student
Name of the University
Author Note

1BUSINESS ANALYTICS
To: Grace Wong
From: Stephen Hennigsson
Subject: Analysis of the Store Data
Dear Sir,
With reference to your mail and the interest of the board to evaluate the performance of the store
in the previous financial year and to understand whether the stores can keep pace with the
competitors, the analysis have been conducted as asked by the board.
An overall summary of the gross profit has been evaluated and from the data on the 150 stores of
the store chain. From the analysis, it can be said that the average profit earned from all the stores
is $0.93m. Fifty percent of the stores have earned a profit above $0.88. It can also be seen from
the analysis that for most of the stores, the profit is less than the average profit of $0.93m.
There are some stores which are open on Sundays and some other stores that are not open on
Sundays. It is important to understand whether there is any significant difference in the sales of
the stores when they are open or closed on Sundays. From the analysis, it has been observed that
there is no significant difference in the sales of the stores whether they are open on Sundays or
closed on Sundays. Thus, it is not going to be a good idea to keep the stores open on Sundays in
order to compete with the competitors.
It has been observed that 105 stores out of the 150 stores of supermart have introduced an online
channel. It has been seen that there is significant difference in the online sales between the stores
located in the malls and not located in the malls. Moreover, it has been observed that the online

2BUSINESS ANALYTICS
sales of the stores located in the malls have shown a less amount of online sale than the ones not
located in the malls.
It is known that any unsold stock or wastage is always costly to the stores. All the stores of this
particular store chain experiences wastage. Some stores experience high wastage, some others
experience low and medium wastage. From the analysis, it has been observed that most of the
stores have experienced medium wastage followed by high and low wastage. 54 percent of the
stores have experienced medium wastage, 31 percent of the stores have experienced high
wastage and 15 percent of the stores have experienced low wastage. Thus, among the 150 stores,
46 stores have shown high wastage.
Among the 46 stores showing high wastage, 11 stores are in Queensland and 11 are in South
Australia. 9 stores in Victoria have shown high wastage and 8 stores in New South Wales have
shown high wastage. Thus, it can be said that high wastage has been observed mostly in stores of
Queensland and South Australia. High wastage have been observed the least in stores located in
the Australian Capital Territory and in the Northern Territory.
It is important to find out which factors explain the variation in the sales between the stores most
effectively. The most important factors have been identified as the advertising expenses, number
of staff, number of car spaces and number of trading hours. From the analysis, it has been seen
that the advertising expenses can explain 71 percent of the variability in the sales between the
stores. It has also been observed that this variability is explained significantly. Again, the number
of staffs that work for the stores can explain 54 percent of the variability in the sales value
between the stores. This is also quite a large percentage and thus, the number of staffs is also
significantly effective in explaining the variation of the sales of the stores. The third factor,
number of car spaces can explain 33 percent of the variability in the sales of the stores. This is

3BUSINESS ANALYTICS
not quite an effective factor though the number of car spaces present in the stores explains the
variability in the sales significantly, the percentage of explanation of the variability is very less.
The fourth factor, the number of trading hours of the store can explain only 1 percent of the
variability in the sales between the stores. It has also been found that the number of trading hours
is completely insignificant in explaining the variabilities in the sales between different stores.
Thus, the advertisement expenditures, the number of staffs in the stores and the number of car
spaces have shown effectiveness in explaining the variability in the sales between the different
stores. Among these three factors, the advertisement expenses have been most effective in
explaining the variability of the sales between the stores.
I hope all the analysis conducted and the report shared will be able to answer all the questions the
board had in mind.
Sincerely,
Stephen Hennigsson