Sales Dashboard Project
Overview:
This project is a Sales Dashboard designed using Power BI. The dashboard provides a comprehensive analysis of sales data, offering insights into various key metrics such as profit, quantity, and amount across multiple dimensions like state, category, customer, and payment mode.
Key Features:
Summary Cards:
Total Quantity: Displays the sum of quantities sold.
Total Profit: Shows the overall profit generated.
Average Order Value: Calculates and displays the sum of average order values.
Total Sales Amount: Represents the total revenue from sales.
State-Wise Performance:
A bar chart comparing the sum of amount and profit by different states like Maharashtra, Madhya Pradesh, Uttar Pradesh, and Delhi.
Category Analysis:
A donut chart breaking down the quantity sold by category, highlighting major segments such as Clothing (63%), Electronics (21%), and Furniture (17%).
Monthly Profit Trends:
A bar chart showing profit trends by month, helping to identify peak sales periods and downturns.
Customer Insights:
A bar chart illustrating the amount spent by key customers such as Harivansh, Madhav, and Madan Mohan.
Payment Mode Breakdown:
A donut chart presenting the distribution of payment methods used, with COD (44%) being the most common, followed by UPI (21%), Debit Card (13%), and Credit Card (12%).
Sub-Category Profit Analysis:
A horizontal bar chart comparing the profit generated by different sub-categories like Printers, Bookcases, Sarees, and Accessories.
Interactivity:
The dashboard allows users to:
Filter by state and quarter using slicers.
Drill down into detailed views for further analysis.
Design:
The dashboard uses a dark-themed color scheme with vivid charts to enhance visibility and user experience. The use of different chart types ensures clarity while summarizing key metrics for decision-making.
Tools and Techniques:
Power BI: Used for creating visualizations, managing relationships, and filtering data.
DAX: Applied for calculations and deriving metrics.
Power Query: Employed for data transformation and cleaning.