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Supermarket_Dashboard

Dashboard link : https://app.powerbi.com/view?r=eyJrIjoiNDY1N2VkYmYtMmVjYi00NzBmLWIzMjMtZTYxZmIxMzFhNjc0IiwidCI6IjY0ZGU2ZGRmLTA4ZTAtNGJjNy1iYTdkLWZmNTM1MmU1MGFjYyJ9

Product Sales Analysis using Power BI

In this project, I utilized Power BI to perform an in-depth Product Sales Analysis across various dimensions such as revenue, customer demographics, sales channels, and product performance. By creating interactive dashboards and visualizations, I transformed raw sales data into actionable insights, which supported strategic decision-making and enhanced business growth.

Key Responsibilities:

  • Data Collection and Integration: Aggregated sales data from multiple sources, including CRM systems, e-commerce platforms, and ERP systems. Cleaned, transformed, and structured the data using Power Query to ensure accuracy and consistency for analysis.

  • Revenue and Sales Performance Tracking:

    • Developed dynamic dashboards that tracked overall sales revenue, sales volume, and product profitability by category, region, and time period (daily, monthly, quarterly, and yearly).
    • Implemented KPIs to monitor revenue growth, profit margins, and sales trends, providing clear visibility into the top-performing products and identifying underperforming items.

  • Customer Demographic Analysis:

    • Segmented customer data by demographics (age, gender, region) to understand purchasing behavior. Visualized the correlation between customer preferences and product categories, helping to identify key target markets for future marketing campaigns.

  • Sales Channel Performance:

    • Analyzed sales across various channels (online, in-store, distributors) to determine which channels yielded the highest revenue. Implemented visuals to highlight the most profitable sales channels, which supported resource allocation and marketing investments.

  • Product-Specific Insights:

    • Used DAX to calculate custom metrics such as product churn rate, repeat purchase rate, and average order value per product. This allowed for better decision-making regarding product lifecycles, inventory management, and promotions.

  • Time-Based Trends and Forecasting:

    • Applied time intelligence in Power BI to track seasonal sales patterns, monthly performance comparisons, and year-over-year growth.
    • Leveraged forecasting models to predict future sales trends, enabling proactive decision-making for product launches, stock replenishment, and sales campaigns.

  • Geographical Sales Distribution:

    • Mapped out product sales by region, allowing for geo-spatial analysis of market penetration. Visuals showed regional sales hotspots, highlighting areas with untapped market potential and regions requiring targeted promotions.

  • Customer Feedback and Ratings:

    • Integrated customer feedback and product ratings into the analysis to correlate product performance with customer satisfaction. This helped identify products with high ratings but low sales, providing insights into potential improvements in marketing efforts or distribution.

  • Business Recommendations:

    • Based on the data-driven insights, I provided strategic recommendations such as optimizing product portfolios, focusing on high-margin items, and enhancing marketing strategies for underperforming products. These recommendations were instrumental in improving sales efficiency and overall profitability.

Tools and Technologies:

  • Power BI for dashboard creation and data visualization.
  • DAX (Data Analysis Expressions) for complex calculations and custom KPIs.
  • Power Query for data cleansing and transformation.
  • SQL for database querying and data extraction.
  • Excel for supplemental analysis and data preparation.

This project demonstrated the power of data-driven insights in optimizing product sales strategies, improving customer targeting, and driving overall business performance. By leveraging Power BI, I provided the organization with a clear, visual understanding of sales trends and actionable recommendations to fuel growth.