Retail Chain Sales And Returns Dashboard

With DAX measures, I created calculations and aggregations to gain insights into sales performance, such as total revenue, % weekend Transactions, Last month's Revenue, and Profit Margin. Additionally, I calculated return rates and analyzed the impact of returns on overall sales metrics. These measures provided a comprehensive understanding of the financial performance of the retail store chain.

Using M codes, I performed data transformations and cleansing operations to ensure data accuracy and consistency. I integrated sales and return data, enabling a comprehensive analysis of the entire customer journey and the associated financial implications.

To enhance the visualization and analysis of different scenarios, I leveraged bookmarks in Power BI. By saving and organizing different views and filters as bookmarks, I could easily switch between various perspectives, enabling meaningful comparisons and insights. This allowed me to explore different scenarios, identify patterns, and uncover actionable insights to optimize sales and returns processes.

Throughout the project, the combination of DAX measures, M codes, and bookmarks in Power BI empowered me to visualize and analyze the complex sales and returns data of the retail store chain. By effectively utilizing these tools, I gained valuable insights into financial performance and customer behavior, enabling data-driven decision-making to drive revenue growth, reduce returns, and improve overall customer satisfaction.