Skip to content

Latest commit

 

History

History
100 lines (85 loc) · 4.04 KB

README.md

File metadata and controls

100 lines (85 loc) · 4.04 KB

Customer Insights and Spending Behavior Analysis

Table of Contents

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.