Personalized Marketing Through Artificial Intelligence: Architectures, Techniques, and Emerging Frontiers

This paper has described an AI-driven personalized marketing system from architecture through production deployment, supported by empirical results from a 24-month e-commerce dataset. The proposed hybrid model—combining ML-based customer clustering, BERT- based NLP inference, deep learning recommendation, and contextual bandit re-ranking—achieves classification accuracy in the 93–95% range and satisfies response latency and retention targets in the production environment. The performance improvements over the rule-based baseline are substantial and consistent across evaluation runs. The paper has attempted to report limitations with the same prominence as results. The dataset is drawn from a single platform operating in a specific market, and the reported metrics should not be assumed to transfer without validation to other contexts. The CLV metric is subject to causal confounding. The differential privacy budget is permissive and may not satisfy all regulatory interpretations. The NLP pipeline underperforms on Hindi and Marathi inputs relative to English. Each of these limitations defines a concrete direction for future work. The Model Staleness Index, documented here for the first time, provides a practical mechanism for triggering retraining based on distributional drift before accuracy degradation becomes visible in business metrics. Its calibration methodology and sensitivity characteristics warrant further investigation across different platforms and behavioural contexts. The broader message of this paper is that deploying AI in marketing at production scale requires equal attention to data engineering, system reliability, privacy compliance, and model accuracy—and that honest documentation of failures and limitations is as valuable to the research community as documentation of successes.

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