P.A.C.E: PREDICTIVE ANALYTICS & CUSTOMER ENGAGEMENT PLATFORM — A REVIEW

Author

Rakhi Verma, Bhumi Bomewar, Pranav Valluvar, Vardhan Andraskar,Prof. Pradnya Meshram

Abstract

 Modern businesses generate vast volumes of structured transactional data alongside unstructured behavioral signals such as product reviews, browsing activity, and digital interactions. Despite this abundance, most operational customer-analytics systems still treat customer segmentation and demand forecasting as independent problems, relying on conventional distance-based clustering algorithms — K-Means, Hierarchical Clustering, or DBSCAN — applied only to numerical transactional attributes such as Recency, Frequency, and Monetary (RFM) value. This forces churn detection to remain reactive rather than predictive, and leaves retention decisions disconnected from the very segments and risk scores that identify them. This paper reviews the underlying literature on customer segmentation, churn prediction, deep representation learning, Transformer-based semantic modeling, time-series demand forecasting, and Explainable AI (XAI), and introduces P.A.C.E (Predictive Analytics & Customer Engagement Platform) — a unified customer-intelligence system that fuses structured behavioral features with unstructured textual signals to segment customers, flag churn risk and demand trends, and prescribe explainable, segment-specific retention actions. We synthesize the gaps present in current literature, describe P.A.C.E's proposed multi-stage architecture, methodology, and technology stack, and provide a comparative analysis against conventional RFM/K-Means pipelines and standalone churn-prediction and forecasting models. Finally, we discuss implementation challenges — including data fragmentation, model interpretability, and scalability — and outline the platform's future scope.

Keywords

Customer Segmentation, RFM Analysis, Churn Prediction, Deep Embedded Clustering, Explainable AI, BERT, Graph Neural Networks, Demand Forecasting, LSTM, Customer Relationship Management.

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References

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[5] R. Sharma, et al., “Comparative Study of Clustering Algorithms for Retail Segmentation,” International Journal of Computer Applications (IJCA), 2023.

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