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Sales and inventory analysis

Overview

This project focuses on analyzing sales and inventory data to identify trends, customer behavior, and product performance. Using datasets related to product sales and inventory, I developed a comprehensive interactive dashboard in Power BI that enables users to explore critical metrics like top-selling products, least-performing items, and customer purchasing patterns. This project highlights my ability to create actionable insights and visually appealing dashboards.

Datasets Used

  1. Fruit Sales Data - Inventory and Sales.xlsx:

    • Contains detailed sales data, including product names, quantities sold, unit prices, discounts, and total sales amounts.

    • Includes customer purchase data for tracking purchasing habits and frequency.

What I Did?

  • Data Cleaning and Transformation:

    • Handled missing and inconsistent values in sales data.

    • Standardized product names and customer details for consistency.

    • Calculated derived metrics, such as total discount and revenue, using DAX (Data Analysis Expressions).

  • Data Modeling in Power BI:

    • Created relationships between sales and customer datasets for integrated analysis.

    • Optimized data models to improve dashboard performance.

  • Visualization Design:

    • Built an interactive Power BI dashboard with the following key features:

      • Top/Least Performing Products:

        • Identified "Asparagus" as the most sold product and "Lemon" as the least sold.

        • Visualized product performance using pie and bar charts.

      • Customer Analysis:

        • Highlighted customers purchasing the highest quantities (e.g., Amina Loo at 51 items).

      • Trends Over Time:

        • Plotted sales trends across decades, showing the highest total sales amount in 1960.

      • Unit Price and Discount Analysis:

        • Correlated product unit prices and discounts with quantities sold.

  • Dynamic Interactivity:

    • Added slicers for product names, customer names, and time periods for flexible filtering.

    • Used tooltips and drill-through pages to provide in-depth details.

What I Learned

  1. Power BI Proficiency:

    • Mastered data modeling and DAX calculations for creating key performance indicators (KPIs).

    • Designed engaging and user-friendly dashboards.

  2. Data Analysis Insights:

    • Learned to interpret sales data for business insights.

    • Understood the importance of customer purchasing behavior in driving sales strategy.

  3. Visualization Best Practices:

    • Applied storytelling techniques for effective data communication.

    • Balanced visual design elements with analytical content for clarity.​

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Key Highlights

  1. Top-Selling and Least-Selling Products:

    • Most Sold: Asparagus with 60 units.

    • Least Sold: Lemon with 20 units.

  2. Customer Insights:

    • Amina Loo topped the list of customers, purchasing 51 items.

  3. Revenue and Discount Analysis:

    • Total discounts of 266.60 were offered, with significant revenue spikes in 1960.

  4. Product Performance Trends:

    • Clear trends in unit price vs. sales quantity provided actionable insights for pricing strategy.

  5. Visual and Analytical Appeal:

    • Engaging visuals showcasing product and customer trends, paired with dynamic interactivity for exploration.

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Next Steps

Building on this project, I plan to integrate more complex datasets, such as inventory management and supply chain data, to provide a holistic view of business operations. Additionally, I aim to incorporate advanced analytics for predictive modeling of sales performance.

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