Machine Learning For Customer Lifetime Value Prediction

Machine Learning For Customer Lifetime Value Prediction

Growth Analytics · Revenue Modeling

Python · Feature Engineering · Cross-Validation · Model Serving

Operational Focus: individual customer lifetime value (CLV) regression and future cash-flow trajectory prediction.

Project Abstract & Technical Scope

This project introduces an end-to-end, production-ready machine learning framework for Machine Learning For Customer Lifetime Value Prediction. Within real-world operational environments, systems face severe obstacles including right-censored transaction data, non-contractual repeat behavior, and unpredictable high-roller outliers. The objective of this work is to implement a robust, leak-free computational pipeline that translates raw inputs into deterministic, high-confidence decision metrics.

The system ingests and processes records sourced from cohort repeat purchase intervals, margin per order, return rates, customer acquisition cost (CAC), and tenure. Raw attributes undergo automated data sanitization, multivariate imputation, distribution rebalancing, and outlier filtering. Continuous numerical features are scaled using robust statistical scaling techniques, while categorical, spatial, and temporal attributes receive cyclical encoding, high-cardinality target transforms, or dense embeddings.

Modeling evaluates diverse algorithmic paradigms: BG/NBD Probabilistic Model, Gamma-Gamma Spend Model, LightGBM Regressor, Random Forest Regressor. Rigorous validation protocols employ stratified, temporal, or grouped cross-validation to prevent train-test contamination. Hyperparameter optimization is systematically executed via Bayesian search strategies (Optuna), targeting optimization of RMSE, Normalized MAE, Spearman Rank Correlation, 12-Month Projected Value Accuracy rather than uninformative global accuracy.

To ensure practical viability and regulatory transparency, global and local feature contributions are derived using TreeSHAP and Partial Dependence profiles. The winning configuration is serialized into portable ONNX format and served via an asynchronous FastAPI microservice equipped with telemetry logging for real-time concept drift detection.

Tools & Technologies

The standard modern data science stack utilized for feature extraction, model tuning, and REST deployment:

Modular Machine Learning Workflow

Candidate Algorithms Benchmarked

• • BG/NBD Probabilistic Model
• • Gamma-Gamma Spend Model
• • LightGBM Regressor
• • Random Forest Regressor

Final production selection is based on cross-validated Pareto efficiency balancing RMSE, Normalized MAE, Spearman Rank Correlation, 12-Month Projected Value Accuracy against inference latency.