Sales Analysis Dashboard

Data-driven decision-making is crucial for any company aiming for sustainable growth. Understanding sales trends, identifying top performers, and pinpointing areas for improvement are key to optimizing revenue and achieving business objectives. This is where a robust sales analysis dashboard becomes invaluable.

I recently developed a comprehensive sales analysis dashboard in Excel, providing a dynamic and insightful view of key sales performance indicators. Before creating the visualizations, I accurately transformed and cleaned the raw sales data using Power Query, ensuring data accuracy and consistency. This involved handling missing values, standardizing data formats, and removing duplicates. The cleaned data comprised the following columns: Order ID, Order Date, Shipped Date, Shipping Duration, Customer ID, Customer Name, City, State, Salesperson, Region, Month, Quarter, Shipper Name, Payment Type, Product Name, Category, Unit Price, Quantity, Revenue, and Shipping Fee.

Leveraging the power of Power Pivot, I then calculated a range of important measures and KPIs. This allowed for a deeper dive into the data, revealing valuable insights. The result is a three-dashboard system offering a holistic view of sales performance:

Revenue Analysis: This dashboard focuses on revenue trends, broken down by category, region, quarter, and top-performing products and cities. It provides a clear picture of revenue generation and identifies key contributors to overall sales.

Quantity Analysis: This dashboard provides insights into sales volume, focusing on the quantity of products sold, top-performing sales representatives, and leading customer segments. This helps identify potential areas for increased sales volume.

Shipping Analysis: This dashboard analyzes shipping costs, performance of different shippers, and payment type trends. This allows for optimization of shipping costs and identification of potential areas for improvement in logistics.

To enhance interactivity and allow for granular analysis, I incorporated several powerful slicers. These slicers enable users to easily filter the data based on various criteria, such as: Product Name, Category, Month, Salesperson, Region, Quarter, Shipper Name, and Payment Type. This dynamic filtering allows for a deeper exploration of the data and facilitates the identification of specific trends and patterns.