Project Overview

This project analyzes chocolate sales data from 2023–2024 to understand sales performance, identify trends, and monitor key business metrics through an interactive dashboard.

The goal of this project is to transform raw sales data into clear insights that help management understand business performance and identify growth opportunities.

Dataset

The dataset consists tables: Calendar, Customer, Products, Sales, and Stores.

Data Preparation

The data was prepared using Power Query in Power BI.

Steps performed:

1. Import all CSV files into Power BI.
2. Clean and format the data using Power Query.
3. Ensure consistent data types across all tables.
4. Remove duplicates and handle missing values.
5. Create relationships between tables to build a data model.

Data Model

Relationships were created between tables to allow proper filtering and aggregation: Calendar → Sales Customers → Sales Products → Sales Stores → Sales This structure enables accurate time-based and category-based analysis.

Calculated Columns & Measures

Several measures were created using DAX: Member Duration = DATEDIFF(customers[join_date],TODAY(),YEAR) Member Status = if(customers[loyalty_member]=1,"Member","Non Member") Profit Margin = divide(sum(sales[profit]),sum(sales[revenue])) Profit per transaction = divide (sum(sales[profit]),DISTINCTCOUNT(sales[order_id])) Revenue per person = divide (sum(sales[revenue]),DISTINCTCOUNT(sales[customer_id])) Revenue per transaction = divide (sum(sales[revenue]),DISTINCTCOUNT(sales[order_id])) Total Transaction = DISTINCTCOUNT(sales[order_id]) Transaction per person = divide(DISTINCTCOUNT(sales[order_id]),DISTINCTCOUNT(sales[customer_id]))

Key findings

• Revenue remained stable across the analysis period.
• The business maintains a strong profit margin of around 40%.
• Ferrero is the top profit-generating brand.
• White chocolate is the most profitable category.