https://github.com/tanishq-ctrl/consumer-personality-analysis
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.
https://github.com/tanishq-ctrl/consumer-personality-analysis
dataanalysis dataanalytics matplotlib numpy pandas python seaborn
Last synced: 2 months ago
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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.
- Host: GitHub
- URL: https://github.com/tanishq-ctrl/consumer-personality-analysis
- Owner: tanishq-ctrl
- License: mit
- Created: 2024-12-13T15:22:22.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-12-13T15:34:41.000Z (5 months ago)
- Last Synced: 2025-01-27T06:35:03.402Z (4 months ago)
- Topics: dataanalysis, dataanalytics, matplotlib, numpy, pandas, python, seaborn
- Language: Jupyter Notebook
- Homepage: https://github.com/tanishq-ctrl/Consumer-Personality-Analysis
- Size: 808 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Customer Insights and Spending Behavior Analysis
## Table of Contents
- [Overview](#overview)
- [Features](#features)
- [Dataset](#dataset)
- [Visualizations and Insights](#visualizations-and-insights)
- [Technologies Used](#technologies-used)
- [Usage](#usage)
- [Insights](#insights)
- [Contributing](#contributing)
- [License](#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:
```bash
git checkout -b feature-name
```
3. Commit your changes and push them:
```bash
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.