RetainIQ: Autonomous Churn Defense Engine

Predictive Analytics | Targeted Intervention | Revenue Retention

---

Executive Summary

RetainIQ is an end-to-end Customer Retention Intelligence System engineered to mitigate revenue attrition within high-volume subscription environments.

Moving beyond traditional binary classification, RetainIQ utilizes Causal Inference (Uplift Modeling) to distinguish between predicted churn and actionable intervention. By identifying the specific uplift potential of a customer, the system optimizes resource allocation, ensuring retention efforts are directed only toward cohorts where intervention will yield a positive ROI. The architecture integrates high-performance Gradient Boosting with a containerized microservice framework to support real-time enterprise deployment.

---

Technical Stack

Domain	Technology	Implementation Role
Machine Learning	XGBoost, Scikit-Learn	Gradient-boosted decision trees and automated feature pipelines.
Causal Inference	Uplift Modeling	Dual-model T-Learner architecture for treatment effect estimation.
Backend Architecture	FastAPI, Uvicorn	Asynchronous REST API for high-concurrency inference.
MLOps & DevOps	Docker, MLflow	Containerization, experiment tracking, and model registry.
Data Visualization	Streamlit, Matplotlib	Analytical dashboards for executive reporting and model monitoring.

---

System Architecture

graph TD
    subgraph "Data Engineering Layer"
    A[Raw Data Ingestion] --> B(Validation Pipeline)
    B --> C{Feature Engineering}
    C -->|Transformation & Scaling| D[Processed Feature Sets]
    end

    subgraph "Analytical Engine"
    D --> E[XGBoost Classifier]
    D --> F[Uplift T-Learner]
    E --> G((MLflow Model Registry))
    F --> G
    end

    subgraph "Deployment Layer"
    G -->|Artifact Retrieval| H[FastAPI Service]
    H --> I[Docker Containerization]
    I --> J[Streamlit Interface]
    end

Loading

---

Methodology Matrix

RetainIQ employs a rigorous analytical framework transitioning from descriptive data processing to prescriptive causal modeling.

Domain	Technique	Implementation Details	Strategic Impact
Feature Engineering	Scikit-Learn Pipelines	Utilizes ColumnTransformers for heterogeneous data; One-Hot Encoding for categorical variables; StandardScaler for normalization.	Eliminates data leakage and ensures a reproducible transformation schema for production-grade inference.
Predictive Modeling	XGBoost Classifier	Objective: binary:logistic. Optimized via Log Loss. Imbalance handled via dynamic scale_pos_weight adjustments.	Generates high-precision churn probability scores ($P(Churn)$) to serve as a primary risk filter.
Causal Inference	Two-Model Uplift (T-Learner)	Independent learners for Treatment ($T=1$) and Control ($T=0$) groups to estimate counterfactuals.	Isolates "Persuadables" from "Lost Causes," ensuring retention budget is directed toward incremental revenue gain.
Statistical Rigor	Power Analysis	Cohen’s h Effect Size calculation; $\alpha=0.05$, Power ($1-\beta$) $= 0.80$, MDE $= 5%$.	Validates that retention campaign results are statistically significant and actionable for executive decision-making.
MLOps Architecture	Containerized Microservices	Multi-stage Docker builds; MLflow for versioning and hyperparameter tracking.	Facilitates seamless integration into existing IT infrastructure with minimal environment friction.

---

Causal Analysis: Uplift Modeling

A critical deficiency in standard churn models is the inability to account for the "treatment effect." RetainIQ utilizes a counterfactual framework to calculate the Individual Treatment Effect (ITE):

$$ \text{Uplift Score} = P(\text{Churn}|\text{Control}) - P(\text{Churn}|\text{Treatment}) $$

Strategic Segmentation:

1. Persuadables (High Uplift): Customers who respond positively to intervention. These are the primary targets for retention offers.
2. Lost Causes (Zero Uplift): Customers likely to churn regardless of intervention. Resource allocation to this group is minimized to reduce waste.
3. Sleeping Dogs (Negative Uplift): Customers for whom intervention may trigger a churn event. These individuals are excluded from all outreach.

---

Deployment and Execution

Containerized Deployment

To deploy the prediction engine as a microservice:

# Build the production image
docker build -t retainiq-api .

# Initialize the containerized service
docker run -p 8001:8001 retainiq-api

API documentation and endpoints are accessible at port 8001.

Local Development Environment

# Install required dependencies
pip install -r requirements.txt

# Execute the reporting interface
streamlit run src/streamlit_app.py

---

Performance Benchmarking

• Model Discrimination: The XGBoost Classifier achieved an ROC AUC of 0.8187 on hold-out test sets.
• Targeting Precision: Uplift modeling identified that focusing on the top 20% of the customer base yields 3x the retention efficiency compared to non-targeted outreach.
• Latency: The FastAPI inference endpoint maintains a response time of <100ms, supporting real-time operational workflows.

---

Project Structure

RetainIQ/
├── .github/workflows/   # Continuous Integration pipelines
├── data/                # Ingested and curated datasets
├── docs/                # Statistical plans and technical documentation
├── mlruns/              # MLflow metadata and experiment tracking
├── models/              # Serialized model artifacts (.joblib)
├── src/                 # Core Source Code
│   ├── app.py           # FastAPI implementation
│   ├── features.py      # Feature engineering logic
│   ├── train.py         # Model training and validation
│   └── streamlit_app.py # Executive dashboard
├── Dockerfile           # Environment virtualization
└── requirements.txt     # System dependencies