Superstore Sales Analysis

Introduction

Superstore, a fictional retail company which has been in operation for 4 years is looking to improve their sales and overall performance. The store's data experts have been tasked with analyzing the company's data which has been generated over the years of operation to present actionable insights to the stakeholders.

I, the junior data analyst was tasked by the data team to present to them a general report of the company's performance and recommendations that will be of importance to the stakeholders or that can be further investigated before the presentation to the stakeholders.

This report is a documentation of the highlight of my analysis and presentation to be delivered to the data team.

Business Task

The team is interested in an analysis of the store's historical data so they can better understand how the store and their products have performed over the years and what operations they need to improve. They also expect insightful recommendations from the data analyst. All of this will serve as an introduction to the presentation to the stakeholders.

Data Sourcing

The dataset this personal project was carried out on is a CSV file which was gotten from Kaggle and can be accessed here: Superstore dataset

Skills Demonstrated

1. Exploratory Data Analysis
2. Data Transformation
3. Basic Statistics
4. Data Validation
5. Intermediate Data Analysis

Analysis Process

The documentation of the data preparation process, data analysis and results gotten from the data can be found here:

• Data Importation and Validation
• Answers to Business Questions

Visualization

A visualization that allows stakeholders and involved departments assess the company's performance and track orders shipment was created with Tableau and can be found here: Superstore Sales and Profit Comparison Dashboard.

A picture of it can be seen below:

Dashboard

Table

Visualization Process

The documentation of the visualization project can be found here: Superstore Comparison Dashboard Project

Observations

1. Consumers make the most orders and generate the most sales for the store.
2. Phones are our most profitable sub category.
3. We make the most sales in the 4th quarter of the year.
4. We make the most sales in the months of November and December and the least sales at the beginning of the year; February and January.
5. Wednesday is the only day we record markedly low sales.
6. 60 percent of our orders were sent for delivery later than the stipulated date.
7. We received the most orders and made the most sales from the West and the least orders and sales from the South of the United States.
8. We recorded losses from some transactions instead of profits.
9. We recorded losses from 10 states after deducting the sum of individual sale losses from profits.
10. There is a 35.49% difference between the profit percentages of the Furniture and Office Supplies category even though their sales percent is only different by 0.92%.
11. Each year, we lose an average of 35.63 percent of the company's sales profit to losses incurred from sales. We have lost 36 percent of our total profit generated since we began operation to losses incurred from sales.
12. Sales growth dropped after the first year of operation but rose sharply in the third year and eventually dropped by a percentage in the fourth year while profit growth has improved by a percentage each year.

Recommendations

1. The logistics department should look into the reason why 60 percent of the orders made were sent late for shipping and proffer a solution which will aim to reduce the average time before shipping from 4 days to 2 days.
2. The data team and other involved departments should look into the reason for the low orders that come from the Southern and Central region states e.g. pre-existing competitor with more pocket friendly offers.
3. Data should be collected on the reason for the loss of 35.63 percent of sales profit each year and the case should be investigated by the data team. This should also spotlight the cause of losses incurred from individual orders. The data team's intervention will help to increase our final profits after loss deduction.
4. The marketing team should work on driving sales by attracting new customers through advertisements and other creative means, and the rolled out initiatives should place an extra focus on Consumers and consumer friendly products. They should also work on improving sales on days, months and yearly quarters where sales are low. These time periods have been listed in the 'Observation' section.

Conclusion

In the four years Superstore has been in operation, we have received 8000 orders, generated sales of $1,838,588 and made a profit of $225,074, which is only 12.24 percent of our sales earning. Looking into the recommedations will aid the increase of the store's earnings and also help improve customer satisfaction.