Diwali Sales Analysis 📈💰📊

Overview

This project analyzes Diwali Sales data to provide insights into customer behavior, sales trends, and marketing performance. The analysis explores factors such as customer demographics, purchase patterns, and product categories to identify key sales drivers during the Diwali festival season.

Key Insights

Customer Segmentation:

• The data was segmented based on gender, age, and marital status to explore purchasing behavior among different customer groups.

Sales Performance:

• Identified top-performing product categories, brands, and locations based on total sales and profit.

Purchase Trends:

• Analyzed seasonal trends in customer purchases during Diwali, including product categories with high demand.

Marketing Analysis:

• Explored the effectiveness of various marketing campaigns and promotions in driving sales.

Technologies Used

Python: For data cleaning, wrangling, and analysis.

• Pandas: For data manipulation and aggregation.
• Matplotlib & Seaborn: For creating visualizations and exploring trends.
• Jupyter Notebook: For organizing and presenting the analysis in an interactive format.

Analysis Process

Data Cleaning:

• Handled missing values and formatted the dataset for analysis.

Exploratory Data Analysis (EDA):

• Visualized customer demographics (age, gender, marital status) and their relationship with sales.
• Identified high-performing products and regions during the Diwali season.

Segmentation:

• Analyzed customer segments and their purchasing behavior based on demographic factors.

Visualizations:

• Created informative plots to represent insights into Diwali sales trends.

Data Source

The dataset used for this analysis includes sales records for various products and customer information during the Diwali season.

Result

Married women between the age group of 26-35 years from the states of UP, Maharashtra and Karnataka working in the IT Sector, Healthcare and Aviation are more likely to buy products from the Food, Clothing & Appreal and Electronics & Gadgets category.

Conclusion

This project demonstrates how data-driven insights can optimize marketing strategies and boost sales during high-demand periods like Diwali. The findings can be applied to improve product offerings, target customer segments, and enhance promotional campaigns.

Project Learnings

• Performed data cleaning and manipulation.
• Performed exploratory data analysis (EDA) using pandas, matplotlib, and seaborn libraries.
• Improved customer experience by identifying potential customers across different states, occupation, gender, and age groups.
• Improved sales by identifying most selling product categories and products, which can help to plan inventory and hence meet the demands.