This project showcases a comprehensive Coffee Shop Sales Analytics Dashboard, designed to transform raw transaction data into insightful visualizations that drive business decisions. Using MySQL for robust data modeling and Power BI for interactive dashboard design, it enables stakeholders to explore sales performance, product trends, and operational efficiency across multiple store locations.

Key Features:

• Interactive Insights: Drill-down capability on sales, orders, and quantity sold for meaningful trend analysis.
• Dynamic KPIs: Automated month-on-month performance metrics for revenue, orders, and item quantity.
• Time-Based Patterns: Identification of peak sales hours and days using heatmaps and calendar charts.
• Product-Level Analysis: Deep dive into product category performance and top-selling items to support inventory optimization.
• Location Comparison: Visual comparisons of branch performance to enable targeted business strategies.

Technical Highlights:

• Data Pipeline: Raw CSV data imported and modeled in MySQL; cleaned and standardized for analytics.
• Advanced SQL & DAX: Executed month-over-month calculations (using LAG), created dynamic DAX measures for Power BI cards and visuals.
• Power BI Visualization: Custom dashboard theme, interactive slicers, tooltip pop-ups, and drill-through functionality.
• Scalable Design: Built to scale for additional months, stores, and future product categories.

Business Impact:

• Informed management decisions on staffing and inventory by surfacing busiest hours.
• Revealed growth trends and bottlenecks rapidly to guide marketing and resource planning.
• Delivered actionable insights on product and store performance to optimize operations.

EXPLORE THE DASHBOARD STORY ⬇️