This project focuses on analyzing coffee sales data to provide insights into sales performance across various dimensions, including time, geography, product types, and customer behavior. The goal is to identify trends and top performers in order to aid decision-making for sales strategies and customer engagement.
The project uses three datasets:
- Orders: This dataset contains coffee order details such as order date, customer ID, product ID, quantity, and sales amount.
- Customers: This dataset includes customer information like name, contact details, country, and loyalty card membership.
- Products: This dataset lists coffee products with details like coffee type, roast type, size, unit price, and profit.
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Sales Over Time:
- The analysis tracks sales over the months from 2019 to 2022 to show trends in coffee orders. Sales data is segmented by different coffee types, such as Arabica, Excelsa, Liberica, and Robusta.
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Top-Selling Products:
- The data highlights which coffee roast types and sizes performed best in terms of sales volume and revenue.
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Geographical Sales Distribution:
- Sales are analyzed across different countries, with the United States being the top contributor, followed by Ireland and the United Kingdom.
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Customer Insights:
- An analysis of customer behavior focuses on high-value customers and the impact of loyalty cards. The dashboard identifies the top 5 customers and their respective contributions to overall sales.
The dashboard visualizes the following key metrics:
- Sales Over Time: A line chart showing total sales by coffee type for each month.
- Sales by Country: A bar chart comparing the total sales in each country.
- Top 5 Customers: A bar chart listing the top customers by sales value.
- Product Filters: Interactive filters allow users to filter sales by roast type, coffee size, and loyalty card usage.
- Excel: For initial data exploration and formatting, Pivot Tables, Functions, Visualizations
