Customer Insights and Spending Behavior Analysis

Table of Contents

• Overview
• Features
• Dataset
• Visualizations and Insights
• Technologies Used
• Usage
• Insights
• Contributing
• License

Overview

This project focuses on analyzing customer behavior and spending patterns using a comprehensive dataset. Through advanced data visualization and analysis techniques, we aim to uncover actionable insights to improve marketing strategies, optimize product targeting, and enhance customer engagement.

Features

• Detailed Data Analysis: Analyze customer demographics, behavior, and spending patterns.
• Interactive Visualizations: Present insights through visually appealing and meaningful plots.
• Segmentation Analysis: Explore how attributes like age, education, marital status, and household size affect spending.
• Campaign Effectiveness: Examine campaign response rates and their correlation with spending.

Dataset

The dataset contains the following features:

People

• ID: Unique customer identifier
• Year_Birth: Year of birth
• Education: Education level
• Marital_Status: Marital status
• Income: Yearly household income
• Kidhome: Number of children
• Teenhome: Number of teenagers
• Dt_Customer: Enrollment date
• Recency: Days since last purchase
• Complain: Complaints in the last 2 years

Products

• MntWines: Spending on wine
• MntFruits: Spending on fruits
• MntMeatProducts: Spending on meat
• MntFishProducts: Spending on fish
• MntSweetProducts: Spending on sweets
• MntGoldProds: Spending on gold

Promotion and Campaigns

• NumDealsPurchases: Number of purchases with discounts
• AcceptedCmp1 to AcceptedCmp5: Campaign acceptance indicators
• Response: Acceptance of the most recent campaign

Place

• NumWebPurchases: Purchases via the website
• NumCatalogPurchases: Purchases through catalogs
• NumStorePurchases: Purchases in-store
• NumWebVisitsMonth: Website visits in the last month

Visualizations and Insights

Key Visualizations

1. Age Distribution: Analyzed age groups of customers to identify the dominant age range.
2. Spending by Product: Highlighted spending trends across product categories.
3. Campaign Effectiveness: Assessed campaign response rates and correlations.
4. Website Visits vs Online Purchases: Explored the relationship between website visits and purchases.
5. Income vs Total Spending: Examined how income correlates with overall spending.
6. Spending by Household Size: Showed spending variations by household composition.
7. Education and Spending: Analyzed spending behavior by education levels.

Insights

• Older customers spend significantly more, especially on wine and meat products.
• Single-person households have the highest spending across most categories.
• Recent campaigns have better response rates, indicating improved targeting.
• Spending on luxury items like wine and gold is correlated with higher income.
• Customers with complaints show lower spending, emphasizing the importance of customer satisfaction.

Technologies Used

• Python: For data processing and visualization.
• Pandas: Data manipulation and analysis.
• Matplotlib: Plotting and visualization.
• Seaborn: Advanced statistical visualizations.
• NumPy: Numerical data handling.

Usage

• Use the Consumer Personality Analysis.py script to generate all visualizations.
• The results and visualizations are saved in the output/ directory for further use.

Contributing

We welcome contributions to improve the analysis and add more features. To contribute:

1. Fork the repository.
2. Create a feature branch:

   git checkout -b feature-name

3. Commit your changes and push them:

   git push origin feature-name

4. Create a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.