Coffee Shop Sales Report

Hover over the dashboard to explore interactive features.

About this project

This project aimed at simulating real-world challenges faced by coffee shop managers. In this scenario, managers need a tool that helps them monitor store performance, track sales trends, and understand customer behaviors. While the data used in this project is sample data, the problem it addresses is one that many businesses encounter: how to centralize and analyze sales data to drive informed decisions.

Solution Approach

I developed an interactive dashboard that provides a clear, real-time view of key performance metrics. The dashboard consolidates data across stores, products, and time periods, enabling managers to monitor sales trends and assess store performance efficiently. The user-friendly design allows for a deep dive into critical sales metrics, helping managers drive business improvements through data-driven decisions.

Data Sources and Development Process

The dashboard leverages three key datasets:

  • Store Data: Details of coffee shop locations, which allowed me to compare and analyze performance across different stores.
  • Product Data: Information about the products sold, including categories and prices, which provided insights into which products are driving sales.
  • Monthly Purchase Data: Data on transactions, total sales, and units sold over several months, allowing me to identify trends and customer behavior patterns.

I cleaned and transformed these datasets to ensure smooth integration and meaningful analysis. Using Power BI, I built a dashboard with three main sections: Sales Overview, Monthly Sales Trends, and Shopper Behaviors. This structure provides users with easy access to the most critical insights, while also enabling deeper analysis through filters and drill-down options.

Key Insights from the Dashboard

Analyzing the Coffee Shop Sales Dashboard reveals several key insights that can help shop managers optimize performance:

  • Store Performance Variations: Sales distribution across stores shows that Astoria, Lower Manhattan, and Hell’s Kitchen contribute almost equally to total sales. However, slight variations in sales and units sold suggest that Hell’s Kitchen and Lower Manhattan have opportunities for further growth compared to Astoria.
  • Product Category Breakdown: Coffee stands out as the dominant product category, contributing 34.87% of total sales. Bakery items are also performing well, with 16.07% of total sales. However, smaller categories such as Loose Tea and Flavours show significantly lower sales and may require targeted promotions or adjustments in product offerings.
  • Monthly Sales Trends: There’s a clear upward trend in sales, particularly from February to June, with May seeing the largest jump (+77.73%) in total sales. However, April experienced a dip in sales (-24.50%). This could be related to seasonal fluctuations or promotional changes, indicating the need for closer monitoring during similar periods in the future.
  • Shopper Behavior Patterns: Customer purchasing behaviors reveal peak shopping hours in the late morning, especially between 10 AM and 12 PM. This suggests that these hours are critical for sales and may require additional staff or special offers to maximize revenue during these times.
  • Impact of Pricing on Transaction Quantity: The dashboard shows that higher-priced products, such as specialty coffee and certain bakery items, tend to have lower transaction quantities, while more affordable items sell in larger volumes. Managers can use this insight to optimize pricing strategies, potentially offering discounts or bundled deals to increase sales for higher-priced items.

Recommendations for Shop Managers

Based on the key insights defined, I recommend the following actions:

  • Leverage Top-Performing Products: Coffee and Bakery items are consistently the highest contributors to sales. Focus promotions and marketing efforts on these categories to drive even more sales. Additionally, consider introducing variations or premium options within these categories to capitalize on their popularity.
  • Target Underperforming Product Categories: Products like Loose Tea and Flavours are lagging behind in sales. To increase revenue, managers should consider running targeted promotions, discounts, or bundling these items with more popular products. Alternatively, reassessing their place in the product lineup may be necessary if sales continue to underperform.
  • Monitor Store-Specific Performance: With Astoria leading in sales, managers should use it as a benchmark for other stores, like Hell’s Kitchen and Lower Manhattan, to identify strategies that can be replicated. Look into factors such as product placement, store layout, or customer demographics that might be influencing higher sales in Astoria.
  • Capitalize on Peak Hours: With data showing peak sales times between 10 AM and 12 PM, managers should focus resources during these hours. Consider scheduling additional staff, offering time-specific promotions, or ensuring high stock levels during these peak periods to fully capture customer demand.
  • Adjust Pricing Strategies: The relationship between unit price and transaction volume suggests that customers may be price-sensitive to certain items. Managers can experiment with pricing strategies, such as offering discounts on high-priced items or bundling them with lower-priced products to increase transaction quantities.

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