This project analyzes customer retention behavior using Cohort Analysis. By grouping users based on their first transaction month, we track retention trends and visualize customer loyalty over time. This analysis helps businesses understand user engagement and churn patterns.
- Data Loading: Loads and cleans transaction datasets.
- Cohort Generation: Groups users based on their first purchase month (
first_cohort). - Retention Calculation: Calculates monthly retention rates for each cohort.
- Visualization: Generates an intuitive Heatmap using Seaborn to spot trends instantly.
- Pandas (Data Manipulation)
- NumPy (Numerical Operations)
- Seaborn & Matplotlib (Data Visualization)
- Clone this repository
git clone [https://github.com/](https://github.com/)[username]/Customer-Cohort-Retention-Analysis.git cd Customer-Cohort-Retention-Analysis - Install requirements
pip install pandas numpy seaborn matplotlib
- Run the Notebook
- Open
Cohort.ipynbin Jupyter Notebook/Lab. - Run the cells sequentially to process
dummy_data.csvand generate the cohort chart.
- Open
| File | Description |
|---|---|
| Cohort.ipynb | Main Jupyter Notebook containing the analysis code |
| dummy_data.csv | Transaction dataset (Anonymized/Dummy Data) |
| LICENSE | MIT License details |
| README.md | Project documentation |
The analysis outputs a heatmap showing the percentage of active customers over time.
Key Insights:
- Identifies how long customers typically stay active.
- Highlights periods with the highest churn rates.
- Shows the effectiveness of acquisition campaigns in specific months.
Distributed under the MIT License. See LICENSE file for more information.