A deep exploration of loyalty as a multi-dimensional behavioral system shaped by intent, habit, and sensitivity. This article introduces a geometric framework for modeling customer behavior, predicting churn trajectories, and designing ML systems that understand loyalty as a dynamic state, not a metric.

A customer intelligence engine that predicts subscription probability, models purchase frequency, and computes a unified loyalty risk score. Includes explainability, segment insights, and a scenario simulator, all integrated into an interactive Streamlit dashboard.

Next-generation analytics & ML-powered churn prediction for Solana gaming. Self-training models predict player churn 14 days in advance. Live dashboard + REST API analyzing 60M+ on-chain transactions across 12 games.

🔒 Proprietary AI Recruitment Suite by Shakeel Qureshi | Automates: sourcing → engagement → screening → retention using LLM/NLP (spaCy, custom models) | TalentSync™, EngageIQ™, FitScore™, TalentRisk™ | Contact: [email protected]

🔍 Map customer loyalty through multi-dimensional behavioral geometry to gain insights into complex human behavior and enhance engagement strategies.

YouTube traffic quality auditing

🔍 Analyze subscription loyalty risks with machine learning models and gain insights into customer retention and frequency patterns.