/RFM_Segmentation_CLV_Calculation_Ecommerce

Primary LanguageJupyter Notebook

RFM Segmentation and CLV Calculation for E-commerce Business

Overview

This repository contains a comprehensive Python data analysis exercise, showcasing skills in data manipulation, visualization, and interpretation. The analysis is performed on a customer dataset, addressing specific questions related to customer behavior, segmentation, and lifetime value.

Instructions

Follow the steps below to review the analysis:

1. Clone the Repository

git clone git@github.com:ghaskari/RFM_Segmentation_CLV_Calculation_Ecommerce.git

2. Open the Jupyter Notebook

Open data_analyst_task_rfm_clv.ipynb in a Jupyter environment. Alternatively, use online platforms like Google Colab or Jupyter Notebook Viewer.

3.Install requirements file

Ensure you have the required Python libraries installed:

pip install -r requirements.txt

4. Run the Code

Execute code cells in the notebook to perform data analysis tasks.

5. Review Results

Check the notebook for calculated metrics, visualizations, and answers to provided questions.

Note

  • Assumptions and specific details for each question are included in the code.
  • Persian calendar months are used for analysis.
  • K-means is used for customer segmentation based on RFM model.
  • CLV calculation considers churned customers and the number churned rate.