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An End-to-End Customer Intelligence Framework Using Automated MLOps, Behavioral Segmentation, Conversion Prediction, and Prescriptive Analytics for Retail Marketing Optimization

KADARLA SAKETH, KARAMSETTY RAGAKARTHIK, Dr. K Satish Kumar, Dr. K Mahesh Kumar

Abstract


Retail organizations frequently rely on manual data processing and broad marketing strategies that fail to leverage customer behavioral intelligence effectively. This study presents an end-to-end customer intelligence framework designed to automate customer analytics, behavioral segmentation, conversion prediction, and prescriptive decision-making within a unified MLOps architecture. The proposed framework employs a dual-ingestion strategy consisting of an automated watchdog-based monitoring service and an interactive Streamlit interface to support bothunattended and user-driven data processing workflows. Customer behavior is analyzed through feature engineering techniques that derive loyalty and lifetime value indicators from transactional records. K-Means clustering is applied to identify distinct customer personas, while an XGBoost classification model estimates subscription and conversionprobabilities.The resulting intelligence is translated into actionable business recommendations through a rule-based prescriptive analytics engine. Processed outputs are automatically integrated into alivebusinessintelligence environment through Microsoft Power BI, enablingreal-time marketing decision support. The framework demonstrates how machine learning, automation, and business intelligence can be combined to transformraw retail transaction data into targeted and operationally relevant customer insights.

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