This repository contains two Jupyter notebooks that demonstrate how to implement customer segmentation and retention strategies using RFM analysis and KMeans clustering, and cohort analysis. The notebooks are based on a publicly available e-commerce dataset and include detailed explanations of the data preprocessing, analysis, and visualization steps.

• Introduction
• Requirements
• Notebook Descriptions
• Instructions
• Contributing

Customer segmentation and retention analysis are critical components of any business strategy. In this project, we explore two popular techniques for customer analysis: RFM analysis with KMeans clustering and cohort analysis. The RFM analysis helps to segment customers based on their transaction history, while the KMeans algorithm is used to group similar customers together. Cohort analysis is used to study customer behavior over time, allowing businesses to identify trends and patterns that may be used to improve retention rates. The first notebook, Customer_Segmentation_with_RFM_and_KMeans.ipynb, demonstrates how to use RFM analysis and KMeans to segment customers into groups based on their purchase history. We also explore the characteristics of each segment and develop targeted retention strategies to improve customer engagement and loyalty. The second notebook, Customer_Retention_with_Cohort_Analysis.ipynb, shows how to use cohort analysis to track customer behavior over time and identify trends and patterns that inform retention strategies. We also compare the effectiveness of different retention strategies across different cohorts.

To run these notebooks, you will need the following libraries:

• pandas
• numpy
• scipy
• seaborn
• matplotlib
• sklearn
• plotly

You can install these libraries using pip: pip install pandas numpy scipy seaborn matplotlib scikit-learn plotly

This notebook demonstrates how to use RFM analysis and KMeans clustering to segment customers based on their purchase history. The notebook includes a detailed explanation of the data preprocessing steps, the RFM analysis, the KMeans algorithm, and the visualization of the resulting customer segments. We first preprocess the data and calculate the RFM scores for each customer. We then segment the customers into different groups based on their RFM scores and analyze the characteristics of each segment. Finally, we develop targeted retention strategies for each segment to improve customer engagement and loyalty.

This notebook shows how to use cohort analysis to track customer behavior over time and identify trends and patterns that inform retention strategies. We first preprocess the data and group customers into cohorts based on the time they made their first purchase. We then analyze the behavior of each cohort over time and compare the effectiveness of different retention strategies across different cohorts.

To use these notebooks, you can clone the repository and open the desired notebook in Jupyter Notebook or JupyterLab. You will need to have the required libraries installed (see Requirements section). Each notebook includes detailed explanations of the code and the steps taken to perform the analysis. You can run the cells in the notebook to reproduce the results.

Contributions to this repository are welcome. If you find a bug or have a suggestion for improvement, please open an issue or submit a pull request.