A Survey of Data Mining Techniques for Predicting Online Shopping Patterns and Enhancing Customer Engagement

Simran Nagar, Jayshree Boaddh and Rahul Patidar

International Journal of Scientific Research in Science and Technology, 2025, vol. 12, issue 5, 674-679

Abstract: This survey paper reviews the application of data mining techniques to predict online shopping patterns and enhance customer engagement. The rapid growth of e-commerce has led to a massive influx of customer data, making it crucial for businesses to leverage this information to stay competitive. This paper provides a comprehensive overview of various data mining algorithms, including classification, clustering, association rule mining, and sequence analysis, that are used to analyze customer behavior. We explore how these techniques are applied to solve key challenges such as predicting purchase intent, personalizing product recommendations, segmenting customers, and optimizing marketing campaigns. Furthermore, the paper discusses the integration of these predictive models with strategies for improving customer engagement, such as tailored communication, loyalty programs, and personalized user interfaces. We analyze the strengths and weaknesses of different approaches and identify emerging trends and future research directions in this field, including the use of deep learning and real-time analytics. The goal of this survey is to provide a valuable resource for both researchers and industry practitioners interested in leveraging data mining to create more effective and customer-centric online shopping experiences.

Keywords: Data Mining; Classfication; Clustering; Behavioural Data (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v12:y2025:i5:id:1255

DOI: 10.32628/IJSRST25126390

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