Churn & Retention Analytics System

Executive Summary

This project delivers an end-to-end churn and retention analytics system that transforms raw customer data into actionable retention strategies. The system processes 100,000+ customer records and millions of usage events through a production-grade SQL pipeline, identifies at-risk segments via lifecycle analysis, and enables data-driven retention decisions through ML-powered risk scoring and a business simulator. The outcome is a complete workflow from data generation to ROI-optimized retention interventions.

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Project Objective

Customer churn is a critical business problem across SaaS, fintech, telecom, and subscription industries. This system enables decision-makers to:

• Identify at-risk customers before they churn (31–45 day intervention window)
• Prioritize retention efforts using ML-powered risk scores
• Simulate retention strategies with cost vs. impact analysis
• Optimize ROI by targeting high-value, high-risk segments

The system demonstrates production-ready analytics workflows suitable for portfolio demonstration and technical interviews.

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High-Level Architecture

Raw Data → SQL Feature Engineering → EDA & Lifecycle Analysis → ML Models → Retention Simulator

📌 [Insert architecture diagram here]

Tech Stack: Python 3.11+, DuckDB, Pandas, Scikit-learn, Streamlit, Plotly, Seaborn

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Project Stages (Wiki Navigation)

For detailed technical documentation, see stage-specific guides:

• Stage 1 — Data Generation & Modeling
  Synthetic data generation with retention probability curves

• Stage 2 — SQL Pipelines & Data Quality
  Staging, data quality checks, and feature engineering

• Stage 3 — EDA & Lifecycle Analysis
  Exploratory analysis with lifecycle segmentation

• Stage 4 — ML-lite Modeling & Explainability
  Logistic Regression and Random Forest with SHAP

• Stage 5 — Retention Strategy Simulator
  Streamlit app for retention strategy simulation

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Documentation Index

• Stages overview: docs/README.md
• Streamlit app: app/README.md
• Pipeline scripts: pipeline/README.md
• Core modules: src/README.md
• SQL layer: sql/README.md
• Data layout: data/README.md
• Notebooks: notebooks/README.md
• Model artifacts: models/README.md
• EDA outputs: eda_outputs/README.md

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Key Results (Summary)

Dataset Statistics

• Total Customers: 92,610
• Active Customers: 85,590 (92.4%)
• Churned Customers: 7,020 (7.58%)
• At-Risk Segment: 6,347 customers (6.3%) in 31–45 day intervention window

Model Performance

• Logistic Regression (Baseline):

  • ROC AUC: 0.83
  • Precision@Top 10%: 26%
  • Good interpretability, moderate performance

• Random Forest (Production):

  • ROC AUC: 0.96 (16% improvement)
  • Precision@Top 10%: 51% (96% improvement)
  • Excellent non-linear pattern capture

Business Impact Metrics

• Targeting Efficiency: Top 10% risk customers show 51% churn probability (vs 7.58% baseline)
• Precision Improvement: 2x better targeting with ML vs. random selection
• ROI Potential: Optimal retention strategies yield 2.3x ROI for targeted segments
• Intervention Window: 31-45 days before churn is optimal timing (6,347 customers addressable)

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Key Insights (Executive)

Data-Driven Findings

• Usage decline is the strongest churn signal (negative usage trends correlate with churn)

  • Customers with declining 30-day usage show 3.2x higher churn probability
  • Early intervention (31-45 day window) can prevent 60%+ of at-risk churn

• Free / lower-tier plans show significantly higher churn risk (13.2% vs 0.9% for Enterprise)

  • Free tier: 13.2% churn rate
  • Basic tier: 8.5% churn rate
  • Pro tier: 4.2% churn rate
  • Enterprise tier: 0.9% churn rate
  • Recommendation: Focus retention efforts on Free/Basic tiers with high usage

• Payment friction and support load materially increase churn probability

  • Customers with 1+ payment issues: 2.8x higher churn risk
  • Customers with 3+ support tickets: 2.1x higher churn risk
  • Combined effect: 4.5x higher churn risk

• Targeted retention can yield positive ROI when focused on high-risk, high-value segments

  • Top 10% risk customers: 51% precision (vs 7.58% baseline)
  • Optimal strategy: 15% discount + Priority Support on Pro/Enterprise customers
  • Estimated ROI: 2.3x for targeted interventions

Model Performance Insights

• Random Forest outperforms Logistic Regression significantly:

  • ROC AUC: 0.96 vs 0.83 (16% improvement)
  • Precision@Top 10%: 51% vs 26% (96% improvement)
  • Better capture of non-linear feature interactions

• Feature Importance (SHAP Analysis):

  1. Usage trend (30-day): Highest impact on churn prediction
  2. Total payment issues: Strong negative signal
  3. Active days: Engagement indicator
  4. Plan tier: Segmentation driver
  5. Support ticket volume: Service quality proxy

Business Impact

• Intervention Window: 31-45 days before churn is optimal

  • 6,347 customers (6.3%) in this critical window
  • Early intervention can prevent 60-70% of predicted churn

• Cost-Effective Strategies:

  • Discount (15%): $3.75 cost per customer, 7.5% risk reduction
  • Priority Support: $5 cost per customer, 10% risk reduction
  • Feature Unlock: $3 cost per customer, 7% risk reduction
  • Best ROI: Priority Support on high-value customers

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Demo & Media

Screen Recordings

Full Application Walkthrough

🎥 Demo Video (5-10 minutes)

[Insert Loom / YouTube link here]

Content:

• End-to-end pipeline execution
• EDA visualizations walkthrough
• Model training and evaluation
• Retention Simulator demonstration
• ROI optimization scenarios

Quick Demo (30-60 seconds)

🎥 Quick Overview

[Insert short demo link here]

Content:

• Key features highlight
• Risk scoring demonstration
• Retention strategy simulation
• Business impact visualization

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Screenshots

Architecture & Pipeline

📷 Pipeline Overview

[Insert screenshot: Data flow diagram or pipeline visualization]

Shows: Raw data → SQL pipeline → Feature engineering → ML models → Simulator

Data Generation

📷 Synthetic Data Overview

[Insert screenshot: Data generation output or sample data preview]

Shows: Customer, usage, billing, and support data structure

SQL Pipeline

📷 Feature Engineering

[Insert screenshot: SQL query results or feature table]

Shows: Staging tables, data quality checks, engineered features

Exploratory Data Analysis

📷 Lifecycle Segmentation

[Insert screenshot: Lifecycle analysis visualization]

Shows: Customer distribution across lifecycle stages, churn by stage

📷 Churn by Plan & Region

[Insert screenshot: Churn rate by plan tier and region]

Shows: Interactive Plotly charts showing churn patterns

Model Performance

📷 ROC Curves Comparison

[Insert screenshot: ROC curves for LR vs RF]

Shows: Model performance comparison, AUC scores

📷 SHAP Feature Importance

[Insert screenshot: SHAP global importance plot]

Shows: Top features driving churn predictions

Retention Simulator

📷 Main Dashboard

[Insert screenshot: Overview section with metrics]

Shows: Total customers, average risk, risk distribution

📷 Risk Scoring Visualization

[Insert screenshot: Risk histogram and top 10 customers]

Shows: Churn risk distribution, high-risk customer table

📷 Simulation Controls

[Insert screenshot: Sidebar with filters and retention levers]

Shows: Date filters, segmentation, risk targeting, intervention controls

📷 Business Summary

[Insert screenshot: Before/after metrics and ROI visualization]

Shows: Churn rate comparison, cost breakdown, retained revenue, net ROI

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Code Snippets

Data Generation

# Generate synthetic customer data with retention probability curves
def generate_customers(n_customers=100000):
    # Customer attributes with realistic distributions
    # Retention probability based on plan, region, and usage patterns
    return customers_df

SQL Feature Engineering

-- Calculate usage trends and engagement metrics
WITH usage_trends AS (
    SELECT 
        customer_id,
        AVG(usage_minutes) as avg_usage_minutes,
        -- 30-day trend calculation
        (AVG(CASE WHEN date >= CURRENT_DATE - 30 THEN usage_minutes END) -
         AVG(CASE WHEN date < CURRENT_DATE - 30 THEN usage_minutes END)) 
         / NULLIF(AVG(CASE WHEN date < CURRENT_DATE - 30 THEN usage_minutes END), 0) 
         as usage_trend_30d
    FROM staging.usage_daily
    GROUP BY customer_id
)

Model Training

# Random Forest with feature engineering
rf_model = RandomForestClassifier(
    n_estimators=200,
    max_depth=8,
    min_samples_leaf=100,
    class_weight='balanced',
    random_state=42
)
# Achieves ROC AUC: 0.96, Precision@Top 10%: 51%

Risk Scoring in Simulator

# Real-time risk scoring with preprocessing
X_processed = preprocessor_rf.transform(X_rf)
churn_risk_scores = model.predict_proba(X_processed)[:, 1]
df_model["churn_risk_score"] = churn_risk_scores

Retention Simulation

# Calculate uplift and ROI
adjusted_risk = base_risk * (1 - discount_uplift - support_uplift - feature_uplift)
customers_retained = expected_churn_before - expected_churn_after
net_roi = retained_revenue - total_cost

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How to Run (Reproducibility)

Environment Setup (uv — preferred)

uv venv
uv sync

Environment Setup (pip)

python -m venv .venv
.venv\Scripts\Activate.ps1  # Windows PowerShell
pip install -e .

Full Pipeline Execution

# Stage 1: Generate synthetic data
python pipeline/data_generator.py

# Stage 2: SQL pipeline (staging → features)
python pipeline/run_sql_pipeline.py

# Stage 3: Exploratory data analysis
python src/eda.py

# Stage 4: Train models
python pipeline/train_model.py

# Stage 5: Launch retention simulator
streamlit run app/retention_simulator.py

Expected Runtime: < 5 minutes for full pipeline

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Project Structure

churn-retention-analytics/
├── pipeline/               # Pipeline execution scripts
│   ├── data_generator.py  # Stage 1: Generate synthetic data
│   ├── run_sql_pipeline.py # Stage 2: SQL transformation pipeline
│   └── train_model.py     # Stage 4: Train ML models
├── src/                    # Python modules & analysis
│   ├── eda.py              # Stage 3: Exploratory data analysis
│   └── README.md
├── sql/                    # SQL transformation pipeline
│   ├── staging.sql
│   ├── data_quality.sql
│   ├── churn_features.sql
│   └── README.md
├── app/                    # Streamlit applications
│   └── retention_simulator.py
├── docs/                   # Stage-level documentation
│   ├── stage_1_data.md
│   ├── stage_2_sql.md
│   ├── stage_3_eda.md
│   ├── stage_4_modeling.md
│   └── stage_5_simulator.md
├── data/                   # Data storage
│   ├── raw/
│   └── analytics/
├── models/                 # ML artifacts
├── eda_outputs/            # EDA visualizations
├── notebooks/             # Interactive exploration
└── README.md              # This file

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License

This project is provided as-is for portfolio and educational purposes.