I embarked on this project to delve into the world of data analysis using Excel and showcase my skills in exploring and extracting valuable insights from complex datasets. The "Global Super Store Dataset" Database, with its rich and comprehensive collection of a superstore data, provided the perfect opportunity to apply Excel techniques and gain practical experience in data manipulation, aggregation, and analysis.
This project aims to understand, analyze and visualize behaviors on data recorded from "Global SuperStore", from 2011 to 2014.
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Relationship between the type of shipping and how long it takes, from the purchase order to its corresponding start of shipping.
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Countries and cities with the highest and lowest purchases. Also see profit and loss.
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Behavior of earnings over time. Do they increase or decrease?
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Market where there are more purchases made and where there are more profits.
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Categories and subcategories with the highest sales, also which ones generate the most profits.
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Product with the most sales per year.
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Customer segment with the highest profitability.
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Clients with greater profitability per year.
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Relationship between the certain properties of the orders with their respective sales.
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Do frequent customers contribute to higher income?
1-
a. The Same Day shipping type is made on the same day that the order is placed.
b. In the First Class shipping type, there is usually a period of two days from the order to its corresponding shipment.
c. In the Second Class shipping type, there is usually a period of three days from the order to its corresponding shipment.
d. In the Standard Class shipping type, it takes the longest. There is approximately a period of five days from the order to its corresponding shipment.
2-
a. United States is the country that generates the greatest profits and also the most purchases.
b. Honduras is the country that caused the most losses.
c. The countries with the lowest purchases are: Bahrain, Burundi, Central African Republic, Eritrea, South Sudan and Swaziland. With only 2 sales to each of these countries.
d. New York is the city that generates the greatest profits and at the same time the one that has the most purchases.
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From 2011 to 2014 there is an increase in both profits and sales volume. The 2011-2012 interval is the one in which there is an increase in both but not as pronounced as the others.
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a. The market with the most sales was APAC (Asia-Pacific) while the one with the least was Canada.
b. The market that generated the greatest profits was LATAM (Latin America) while the one that produced the most losses was EMEA (Europe, the Middle East and Africa).
5-
a. The category that had the most purchases was Office Supplies and the one that generated the greatest profits was Technology by a large difference compared to the others.
b. The subcategory with the most sales was Binders and the one that generated the most profits was Copiers.
c. The Furniture category was the only one of these that generated losses.
d. The subcategories that produced losses were Bookcases, Tables, Fasteners and Supplies.
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From 2011 to 2014, the best-selling product was Staples with an average of 57 units sold per year.
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The customer segment with the highest profitability was Consumer, by far the difference with respect to the other two.
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a. In 2011, the most profitable client was Vivek Grady with 68 million.
b. In 2012, the most profitable client was Cyra Reiten with 87 million.
c. In 2013, the most profitable client was Emily Phan with 67 million.
d. In 2014, the most profitable client was Hunter Lopez with 73 million.
9-
a. The priority order to which the most sales were assigned was Medium.
b. This is then followed by the High priority order.
c. Third, the Critical priority order continues.
d. Finally, the priority order to which the least sales were assigned was Low.
10-
Based on the observation of the scatter graph made, which related the amount of sales and profits/losses. Come to the conclusion that customers who buy more frequently do not necessarily contribute to higher income.
I would like to express my gratitude to the creators of the "Global Super Store Dataset" database for providing this remarkable dataset for analysis. This project wouldn't have been possible without their efforts.
