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retention-analytics

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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.

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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.

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  • Python

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.

  • Updated
  • TypeScript

🔒 Proprietary AI Recruitment Suite by Shakeel Qureshi | Automates: sourcing → engagement → screening → retention using LLM/NLP (spaCy, custom models) | TalentSync™, EngageIQ™, FitScore™, TalentRisk™ | Contact: shakeel.techrecruiter@gmail.com

  • Updated

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