Beginner Excel Project: Vrinda Store Data Analysis Part I
In this article, I’ll walk you through the process of how I completed my first data analysis project inspired by a fantastic tutorial I found on YouTube. This project has been an opportunity for me to delve deeper into Excel’s capabilities and explore new ways to streamline data analysis.
Disclaimer: This is my first time publishing on Medium so please don’t expect perfection hehe
Now, let’s dive into the project, shall we?
Objective: This project analyses the sales data of Vrinda Store for the year 2022 to understand customer behavior and ultimately increase sales in the coming year.
Analytical Approach
The analysis and extraction of insights from Vrinda store dataset comprises the following key steps:
- Data Cleaning
- Data Analysis
- Data Visualization
- Interpretation and Insights
Before analysis starts, there must be a problem(s) we need to solve to get our analysis and these include:
- Which month had the highest sales and orders?
- Who purchases more, men or women?
- List the top 10 states contributing to sales.
- Which channel is contributing to maximum sales?
- What is the relationship between age and gender based on the number of sales?
- What are the different order statuses in 2022?
Data Cleaning
The first step is to import the data into Excel and clean it. This includes adjusting column width, removing duplicates and missing values, checking spelling errors, and using appropriate data formats.
Data Analysis
To answer the first question, we need to create a pivot table by using the columns values, month, sum of amount, and Count of order ID. Then, a column chart is used to display the result and we can see here that March is the month that had the highest sales and orders with a total of 1928066.
For the second question, we use the same process. A pivot table is created but this time, the columns gender and sum of amount are used instead. A pie chart is used to display this data and we can see that women purchased more than men.
Here, we have ordered the top 10 states contributing to sales in descending order using the ship-state and sum of amount columns. A bar chart is used to display the results above in ascending order.
A pivot table is created but this time, the columns Channel and sum of amount are used instead. A pie chart is used to display this data and it is evident that the Amazon channel is the primary contributor to overall sales.
I’ll wrap it up for now part II will be uploaded tomorrow where we’ll answer the remaining questions, visualize our answers with a dashboard, and state our insights.
Want to find out more about this project? It’s all waiting for you on my GitHub page. Follow me there for updates on my upcoming projects, or stay tuned here as I’ll be sharing everything I work on.
Thank you for reading! I welcome your questions, suggestions, and insights. Feel free to share your thoughts in the comment section — I look forward to engaging with you!
