Superstore-Analysis

A Sample Superstore Dashboard created using Power BI analyzing Sales. Sample Superstore Dashboard

INTRODUCTION

In today's data-driven society, having an intelligent strategy is essential for businesses to thrive. With the availability of numerous data sources, organizations can utilize data to make informed decisions, forecast future events, and achieve their strategic objectives. Continuous visibility into business performance and the ability to measure key performance indicators are crucial for success. #OBJECTIVE

This project aims to use business intelligence tool to analyse the performance of a global superstore. The outcome of this analysis will provide insights that inform strategic decisions for the continued success and refinement of the Superstore.

DATA SOURCE

This project’s data is open source, it is the Global Superstore dataset obtained from Kaggle. The data can be accessed via this link. The dataset is in a CSV format with 51,290 observations and 24 features.

BI QUESTIONS

This project focuses on descriptive and predictive analytics using the available historical data. The following questions will be addressed through the analysis:

• What is the monthly trend of sales?

• What day of the week do we receive most orders?

• Which products do our customers order most?

• Where are we making profits/loss in terms of product categories?

• Do we have the most sales in region with highest number of customers?

• Who are our best customers?

• Which type of customer segment brings the most sales?

• How many customers do we have per country, and what is their sales contribution percentage? What’s the most preferred shipping mode?

  DATA ANALYSIS AND DASHBOARD

The data analysis was divided into sales performance analysis, product analysis, customer analysis, and region analysis. Using DAX and calculated columns, these analyses provided insights into various aspects of the superstore's operations. Each segment has a dedicated dashboard page that addresses the business intelligence questions raised. The interactive dashboard allows users to navigate and personalize the displayed information according to their specific needs.

Summary of findings

• Standard Class had the highest Total Orders at 5968, followed by Second Class, First Class, and Same Day.
• Office Supplies in Ship Mode Standard Class made up 36.40% of Total Orders.
• Standard Class had the highest average Total Orders at 1,989.33, followed by Second Class, First Class, and Same Day.
• November had the highest Total Sales (352,461.07) and Total Orders (1471).
• At 352,461.07, November had the highest Total Sales and was 489.88% higher than February, which had the lowest Total Sales at 59,751.25.
• November accounted for 15.34% of Total Sales.
• Across all 12 Month, Total Sales ranged from 59,751.25 to 352,461.07.
• At 256,368.16, New York City had the highest Total Sales and was 495.45% higher than Springfield, which had the lowest Total Sales at 43,054.34.
• New York City accounted for 25.09% of Total Sales.
• Across all 10 City, Total Sales ranged from 43,054.34 to 256,368.16.

CONCLUSION

In conclusion, this project utilized data analytics techniques to analyze the performance of a global superstore. Through the interactive dashboard and data insights, stakeholders can make informed decisions and take necessary actions to achieve their strategic objectives. By leveraging the power of data, businesses can drive growth, improve profitability, and stay competitive in today's dynamic market.

Thanks for reading.