Super Market Store Branches Sales Analysis
Analyzing Stores Sales and its dependence on Stores Area and Daily customer Count
In this Dataset I will try to analyze why the different stores have varying profitability and whether location or range of product offerings affect profitability. Based on the findings will we able to predict future profits and store viability in the future?
A supermarket is a self-service shop offering a wide variety of food, beverages, and household products, organized into sections. This kind of store is larger and has a wider selection than earlier grocery stores but is smaller and more limited in the range of merchandise than a hypermarket or big-box market. In everyday U.S. usage, however, “grocery store” is synonymous with supermarket, and is not used to refer to other types of stores that sell groceries. (Kaggle )This Dataset was pulled from Kaggle.com, please see this link below.(https://www.kaggle.com/datasets/surajjha101/stores-area-and-sales-data) To carry out the analysis I used SAS on Demand ,after downloading the dataset into my laptop from Kaggle I imported the Dataset as a CSV file into SAS On Demand. I used SAS to work on the dataset in addition to Proc SQL.
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/* Generated Code (IMPORT) */
/* Source File: STORES .csv */
/* Source Path: /home/u61022716/sasuser.v94 */
/* Code generated on: 7/18/22, 2:05 PM */
FILENAME REFFILE ‘/home/u61022716/sasuser.v94/STORES .csv’;
PROC IMPORT DATAFILE=REFFILE
DBMS=CSV
OUT=WORK.IMPORT1;
GETNAMES=YES;
RUN;
PROC CONTENTS DATA=WORK.IMPORT1; RUN;
/* the above code was used to see the contents of the table*/
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/* saved the work.import file into another datafile and renamed it Supermarket_Stores*/
Data Supermarket_stores;
Set work. import;
run;
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/* Did a Proc Print to get a better look at the Table and printed only the first 10 observations*/
PROC PRINT data = supermarket_stores (obs=10);
title ‘Title: SUPERMARKET STORES’;
RUN;
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/* Find out the minimum ,maximum and average sales per store */
PROC SQL;
SELECT MIN (Store_Sales)
FROM Supermarket_stores;
quit;
/* the minimum amount of sales was $1,492*/
PROC SQL;
SELECT MAX (Store_Sales)
FROM Supermarket_stores;
quit;
/* the maximum amount of sales was $116,320 */
PROC SQL;
SELECT AVG (Store_Sales)
FROM Supermarket_stores;
quit;
/* the average amount of sales was $59,532 */
/* Finding the average, minimum and maximum store size */
PROC SQL;
SELECT AVG (Store_Area), MIN (Store_Area), MAX (Store_Area)
FROM Supermarket_stores;
quit;
/* 1485, 775, 2229 average, minimum and maximum store size respectively*/
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/*first counted the total number of store and then counted the number of stores with less than average Store_sales or less than average Store_Area */
proc sql; Select count (*) from supermarket_stores; quit;
/* total number of stores is 896*/
proc sql ;
Select count (Store_Area) From supermarket_stores
Where store_sales < 59532 or Store_Area < 775;
quit;
/* 465 stores have less than the average store sales or average store area none had both less than the average Stores_sales and Store_Area*/
/* find the number of stores that exceeded the average store_sales */
proc sql;
Select count (*) From supermarket_stores
Where store_sales > 59532;
quit;
/* 431 stores exceeded the average sales of $ 59,532*/
/* Find out whether item availability affected store sales
and therefore profitability*/
proc sql;
Select items_available, store_sales from
Supermarket_stores (obs = 15)
order by items_available Desc, store_sales desc;
run;
Looking at the table above we observed that the number of items sold did not affect the total amount of store sales so there is no direct correlation between the number of items sold and the store sales. The store with the highest number of items sold (2111) had a sale of $46,620 which is below the average store sales of $59,532,
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/* Find out whether Daily_Customer_Count affected store sales and therefore profitability*/
proc sql;
Select Daily_Customer_Count, store_sales from
Supermarket_stores (obs = 15)
order by Daily_Customer_Count Desc, store_sales;
run;
In the same way there is no direct correlation between the Daily Customer Count and Store Sales. From one observation the store that had the largest number of Daily Customer Count at 1160 had a sale of $ 59,130 while another store with Daily Customer Count 530 had a sales of $66,490.
Conclusion
From the above dataset we can safely conclude that there is no direct relation between the daily customer count and sales and in the same manner there is no direct relation between number of items available in the store with store sales.
