International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 12 | Dec 2025
p-ISSN: 2395-0072
www.irjet.net
Decoding Customer Intent through Big Data–Driven Insights Dr. P. Guhan 1, E. Yuvasri2 1Principal, Jaya College of Arts & Science, Chennai.
2PG Student, Department of Computer Applications, Jaya College of Arts & Science, Chennai.
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Abstract - Businesses can anticipate purchases, tailor
such large-scale datasets, modern systems must integrate robust data engineering pipelines, advanced machine learning models, and real-time processing capabilities. This paper presents a scalable and practical methodology for building customer behaviour prediction systems that operate effectively across both batch and near-real-time environments, ensuring an optimal balance between predictive performance, latency, scalability, and interpretability.
recommendations, and optimize marketing strategies by leveraging big data analytics to predict customer behaviour. Traditional analytical methods are no longer adequate to capture intricate and dynamic customer patterns due to the exponential growth of digital footprints created by web logs, mobile applications, transactions, and social media interactions. This paper proposes a comprehensive framework for forecasting consumer actions in e-commerce by integrating large-scale data ingestion, preprocessing pipelines, automated feature engineering, scalable machine learning models, and production-grade deployment mechanisms. This architecture is based on contemporary big-data technologies, such as Apache Spark for distributed processing, Kafka for real-time streaming, HDFS for scalable storage, and sophisticated algorithms like XG Boost, Light GBM, Transformers, and deep neural networks. We describe modular system designs with a focus on real-time personalization, explainable AI (XAI), data drift monitoring, and continuous model retraining (MLOps), as well as a thorough review of previous research and limitations in current predictive systems. For comprehending the real factors influencing consumer choices, creating synthetic data to increase model resilience, and using ethical AI techniques to guarantee equity and openness in extensive ecommerce analytics.
2 Literature Review [1]. Early efforts relied on logistic regression and decision trees on structured transactional data to predict churn and purchase probability. These approaches were effective on small to moderate datasets but suffered when scaled. [2]. Ensemble methods (Random Forests, Gradient Boosting Machines) improved predictive power and robustness. Feature engineering—RFM (Recency, Frequency, Monetary), sessionisation, and user segmentation—became key to performance. [3]. with richer behavioral signals (clickstreams, sequence data), RNNs, CNNs, and Transformer variants have been applied for session- and sequence-level predictions (e.g., next-item recommendation).
Key Words: Big Data, Customer Behavior Prediction, Ecommerce Analytics, Machine Learning,Clickstream Analysis, User Segmentation
[4]. Big-data frameworks (Hadoop, Spark) and streaming platforms (Kafka, Flink) are widely used for scalable training and inference. Model serving at scale often involves feature stores and online model servers (e.g., TF Serving, Seldon)...
1. INTRODUCTION Predicting customer behaviour—including purchase intent, churn probability, click-through likelihood, and customer lifetime value—has become a critical capability for competitive e-commerce platforms. Conventional analysis For competitive e-commerce platforms, the ability to predict customer behaviour—including purchase intent, churn probability, click-through likelihood, and customer lifetime value—has become essential. Because businesses now produce enormous amounts of highvelocity data from web logs, mobile applications, transaction systems, and various third-party sources, traditional analytics techniques are no longer adequate. Big data analytics addresses these challenges by leveraging distributed storage and processing frameworks capable of handling terabytes or even petabytes of heterogeneous data. To extract meaningful patterns from
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3. Methodology 3.1 Objectives • With the high accuracy and minimal latency, forecast one or more customer behaviors (e.g., purchase within 7 days, churn in 30 days, next product category). • Incorporate forecasts into recommendation engines and marketing automation. 3.2 Data Sources
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Transactional data: Orders, returns, payment method, timestamps.
Behavioral logs: Clickstream, page views, session durations, scroll/click events.
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