Coffee Sales Analysis

Introduction

This project analyzes a coffee sales dataset to derive valuable insights and trends. The dataset includes multiple sheets, each providing different dimensions of sales data, such as order details, customer details, and product details. The main problem being solved is understanding which coffee types perform best in different months and years, helping the business make data-driven decisions about inventory, marketing, and production.

### Here are my takeaways:

1. Sales Trends:

- Distinct sales trends were observed for each coffee type. Arabica showed consistent sales growth, while Robusta sales fluctuated significantly month-to-month. This data allows for trend analysis over time, identification of peak sales months, and comparison of coffee type popularity.

2. Geographical Distribution:

- The United States emerged as the largest market, with sales exceeding 35.5 million, significantly higher than in Ireland and the United Kingdom. This suggests a strong market presence and potential for further expansion in the U.S., aiding in identifying key markets and planning targeted marketing strategies.

3. Customer Segmentation:

- High-value customers, such as Allis Wilore and Brenn Dundredge, were identified. Focusing on these customers can enhance loyalty and drive further sales.

Conclusion

This project demonstrates a comprehensive analysis process using Excel, from initial data exploration and cleaning to advanced data visualization and dashboard creation. The insights gained provide a deeper understanding of sales trends, geographical distribution, and customer behavior, which can be leveraged to optimize marketing strategies and improve overall business performance.