Project Overview

In this project, I analyzed an Online Retails dataset to make senior management (CEO and CMO) to understand how their business is performing and what areas are the key strengths of the company.

They are also focused on identifying opportunities that would lead to growth and generate more revenue in the future.

My goal was to transform raw online retail data into actionable insights that could help CEO and CMO to make business decision on how to generate more revenue.

All data cleaning, transformation, and analysis were done using PowerBI.

Data Overview

The dataset contains online retail information across various dimensions including:

Invoice No, Description of the products, Quantity sold, Invoice Date (Date & Time), Unit Price, Customer ID & Country

OBJECTIVES

1. The CEO of the retail store is interested to view the time series of the revenue data for the year 2011 only.
2. The CMO is interested in viewing the top 10 countries (United State excluded) which are generating the highest revenue. Additionally, the CMO is also interested in viewing the quantity sold along with the revenue generated.
3. The CMO of the online retail store wants to view the information on the top 10 customers by revenue. He is interested in a visual that shows the greatest revenue generating customer at the start and gradually declines to the lower revenue generating customers. The CMO wants to target the higher revenue generating customers and ensure that they remain satisfied with their products.
4. The CEO is looking to gain insights on the demand for their products. He wants to look at all countries (United Kingdom excluded) and see which regions have the greatest demand for their products.

POWERBI ANALYSIS

I imported the CSV files into Power BI and performed data cleaning using Power Query. During the process, I noticed that the Unit Price and Quantity columns contained negative values.

To address this, I created conditional columns to ensure Quantity is at least 1 unit and Unit Price is not below $0, effectively filtering out invalid entries.

I also checked for duplicate records and missing values to ensure data quality.

Additionally, I separated the date and time from the Invoice Date column using a custom column, as it initially contained both. After completing these cleaning steps, I loaded the data into Power BI for further analysis.

I used DAX measures to calculate Revenue and to extract both the month name (e.g., Jan, Feb) and month number (e.g., Jan = 1, Feb = 2) to facilitate my analysis.