SaaS User Retention Predictive Model Architecture

Design a comprehensive machine learning pipeline for predicting user churn in a B2B SaaS platform. Outline the entire workflow including feature engineering strategies, specifically addressing: 1) Which behavioral signals are most predictive, 2) How to handle sparse usage data, 3) Recommended model architectures (gradient boosting vs neural networks), and 4) A robust cross-validation strategy that accounts for time-series dependencies. Include pseudocode demonstrating feature transformation techniques and model evaluation metrics specifically tailored to technology product retention.