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| title: Churnflow API | |
| emoji: 📈 | |
| colorFrom: green | |
| colorTo: green | |
| sdk: docker | |
| app_port: 8000 | |
| pinned: false | |
| 🛒 Customer Segmentation & Churn Prediction | |
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| 📌 Executive Summary | |
| Customer churn is one of the biggest revenue leaks in retail. This project analyzes transaction data to identify high-value customer segments and predict churn probability with 73% Recall. | |
| By engineering features from raw purchase logs and utilizing an XGBoost classifier, this solution helps businesses transition from "reactive" retention (trying to save everyone) to "proactive" retention (focusing resources on high-value at-risk users). | |
| Key Business Insight: | |
| "Frequency" was identified as the #1 predictor of churn. | |
| Strategy: Users who fail to make a second purchase within 45 days are 80% likely to churn. Marketing spend should prioritize "Second Purchase Habits" rather than generic discounts. | |
| 📊 Project Architecture | |
| The project follows a modular Data Science lifecycle: | |
| 1. Data Cleaning & Preprocessing (01_Data_Cleaning) | |
| Source: UCI Online Retail II Dataset. | |
| Engineering: Converted raw transaction logs into a customer-level dataset. | |
| Handling: Removed returns (negative quantities) and adjusted for inflation/outliers. | |
| 2. Customer Segmentation (02_RFM_Segmentation) | |
| Methodology: RFM (Recency, Frequency, Monetary) Analysis combined with K-Means Clustering. | |
| Result: 3 Distinct Segments: | |
| 🟢 Champions: High spend, frequent buyers (Upsell targets). | |
| 🔵 Loyalists: Moderate frequency (Maintenance targets). | |
| 🔴 Hibernating: High risk of churn (Win-back targets). | |
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| 3. Predictive Modeling (03_Churn_Prediction) | |
| Target Definition: Sliding window approach (Train on first 9 months, Predict on next 3 months). | |
| Models Tested: Random Forest (Baseline) vs. XGBoost (Final). | |
| Handling Imbalance: Used scale_pos_weight to penalize false negatives. | |
| 📈 Model Performance | |
| We optimized the model for Recall (Sensitivity) because missing a churning customer is more costly than a false alarm. | |
| Metric Random Forest (Baseline) XGBoost (Tuned) | |
| Accuracy 67% 64% | |
| Recall (Churn Catch Rate) 58% 73% 🚀 | |
| Precision 60% 55% | |
| Key Drivers (Feature Importance) | |
| Unlike typical assumptions that "Big Spenders Stay," our model found that Consistency matters more than Value. | |
| Frequency (Count of purchases): The strongest signal for retention. | |
| Recency: Immediate drop-off after 60 days of silence. | |
| Monetary: Surprisingly, a weaker predictor of churn than frequency. | |
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| 🛠️ Tech Stack | |
| Language: Python 3.9+ | |
| Data Manipulation: Pandas, NumPy | |
| Machine Learning: Scikit-Learn (K-Means), XGBoost | |
| Visualization: Plotly (3D Interactive), Matplotlib, Seaborn | |
| Interpretability: Feature Importance Analysis | |
| 🚀 Usage | |
| 1. Clone the repository | |
| code | |
| Bash | |
| git clone https://github.com/aleem1991/customer-segmentation.git | |
| 2. Install dependencies | |
| code | |
| Bash | |
| pip install -r requirements.txt | |
| 3. Run the Notebooks | |
| Open notebooks/01_Data_Cleaning.ipynb to prepare the data. | |
| Run notebooks/03_Churn_Prediction.ipynb to train the model and generate the Churn Report. | |
| 💼 Business Recommendations | |
| Based on the analysis, the following actions are recommended to stakeholders: | |
| Segment Risk Identified Behavior Recommended Action | |
| High Risk (One-Time Buyers) Purchased once, no return in 45 days. Trigger: Send "Welcome Back" bonus for 2nd purchase immediately. | |
| At-Risk Champions High historic spend but Low Recency. Trigger: Personal account manager outreach (No automated spam). | |
| Safe Loyalists Buying regularly. Trigger: Do nothing. Exclude from aggressive discount lists to save margin. | |
| 📬 Contact | |
| Created by Aleem - Feel free to contact me for details on the feature engineering pipeline! |