This repository contains a Power BI project that analyzes and visualizes sales performance data for a fictional bike retail company. The dashboard provides insights into sales trends, top-performing products, and regional performance, helping stakeholders and executives to make data-driven decisions.
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Topline Performance: A high-level view of total revenue, number of transactions, and return trends with additional product category detail.
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Regional Sales: Geographical analysis showing order performance across different regions and countries.
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Product Performance: Insights into product revenue, order and profit margins. Adjustable product metric charts over time.
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Customer Insights: Analysis of customer demographics, buying patterns, and revenue value.
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Year-over-year and month-over-month sales comparison to track growth.
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Maven Market Dashboard_2024_09.pbix - The Power BI file containing the data model and dashboard.
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Maven+Market+CSV+Files - Raw data used for the analysis, including customer details, regoonal, store and product information with a subfolder for sales transactions.
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Maven_Market.png - Image used for grocery logo.
The data used in this project comes from the attached csv files:
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Transaction Data: Contains details of sales transactions, including product/customer keys and quantity.
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Return Data: Includes date of return and different foreign keys related to products.
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Lookup Data: Information about customers, products, calendar and regional details.
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Power BI Desktop (latest version)
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Microsoft Excel (for reviewing raw data)
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Basic understanding of DAX and Power BI visuals
To run this Power BI project locally:
- Clone the repository:
git clone https://github.com/Polyee99/MavenMarket_grocery_store_dashboard.git
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Open the Maven Market Dashboard_2024_09.pbix file in Power BI Desktop.
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Ensure that the data source paths are correct. If needed, modify the data connections in the Transform Data tab to point to the correct file locations.
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Refresh the data to load the latest data from sales_data.xlsx.
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Explore the dashboard and interact with the visualizations.
- Total revenue, profit, sales, and return rate.
- Monthly KPIs for Transaction, Profit and Returns.
- Product detail table with additional metrics like brand, transaction, profit, margin and return rate.
- Map visualization showing sales in stores by state and city.
- Treemap to show proportion of total visible regions.
- Weekly Revenue on a timeline comparison.
- Breakdown of product revenue vs target on a gauge chart.
- Data Cleaning: The raw data was cleaned using Power BI's Power Query Editor, ensuring that all records are complete and formatted properly.
- Data Modeling: Relationships were created between sales, customers, and product tables to allow for effective slicing and dicing of the data.
- DAX Calculations: Custom measures were created using DAX (Data Analysis Expressions) to compute KPIs like total revenue, profit margin, and average customer value.
- Visualization: Multiple Power BI visualizations were used, including bar charts, line graphs, maps, and tables to represent different facets of the data.