🛒 Retail-360: Intelligent Customer Lifecycle Engine

📋 Executive Summary

This project demonstrates an end-to-end Data Science pipeline designed to optimize customer retention and revenue. By analyzing raw transactional data, we aim to:

1. Engineer Features: Transform raw SQL logs into a customer-level "RFM" (Recency, Frequency, Monetary) feature store.
2. Segment Customers: Utilize Unsupervised Learning (K-Means) to identify distinct customer personas.
3. Predict LTV: Build a Deep Neural Network (TensorFlow/Keras) to forecast future customer spending.
4. Audit for Bias: Conduct an AI Ethics check to ensure equitable model performance.

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📄 Model Card: Retail-360 LTV Engine

Model Details

• Developer: Mickey Moore
• Model Date: December 2025
• Model Version: 1.0.0
• Type: Deep Neural Network (Regressor) for Customer Lifetime Value prediction.

Intended Use

• Primary Use Case: Prioritizing marketing spend for high-value customers (Predicted LTV > $500).
• Intended Users: Marketing Analysts, CRM Managers.
• Out of Scope: Credit scoring or loan approval (model is not robust enough for financial risk assessment).

Factors & Limitations

• Data: Trained on 10,000 synthetic transaction records (2017-2018).
• Limitations: The model degrades in accuracy for "New Customers" with less than 3 orders (Cold Start Problem).
• Bias: Audit performed on 'Region'. Max disparity in MAE was found to be < $5.00, deemed acceptable for marketing purposes.

Ethical Considerations

• Privacy: All PII (Personally Identifiable Information) was hashed before training.
• Fairness: We explicitly optimized the model to minimize error variance across geographic regions to prevent "redlining" in marketing offers.