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International Journal of Science, Strategic Management and Technology

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A SURVEY ON E-COMMERCE CUSTOMER BEHAVIOR ANALYTICS: CHALLENGES, INSIGHTS AND TOOLS

AUTHORS:
Ashok Kumar Sahoo
Mentor
Smruti Ranjan Swain
Affiliation
Department of Master of Computer Applications

GIFT Autonomous, Bhubaneswar, Odisha, India
DOI: 10.55041/ijsmt.v2i6.060
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract
Large volumes of customer data are generated every day from modern e-commerce platforms and digital technologies such as online shopping systems, cloud computing, and business intelligence applications. Analysis of these massive datasets requires advanced analytical techniques and scalable platforms to extract meaningful insights for business decision making. Therefore, customer behavior analytics in e-commerce has become an important area of research and development. The basic objective of this paper is to explore the impact of customer behavior analytics, challenges in e-commerce data processing, open research issues, and various analytical tools associated with it. This paper also discusses the role of Databricks in handling large-scale customer datasets and generating interactive business insights through visualization techniques. As a result, this study provides a platform to understand customer purchasing patterns, product preferences, and sales trends at different stages of e-commerce analytics. Additionally, it opens new opportunities for researchers and organizations to develop intelligent and data-driven solutions for improving customer experience, operational efficiency, and business profitability.
Keywords
E-commerce Analytics; Customer Behavior; Databricks; Big Data; Business Intelligence; Data Visualization; Customer Insights; Sales Analytics
Article Information
PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 6
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Jun 06 2026
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Sahoo, A. K. (2026). A Survey on E-commerce Customer Behavior Analytics: Challenges, Insights and Tools. International Journal of Science, Strategic Management and Technology, 02(6). https://doi.org/10.55041/ijsmt.v2i6.060

Sahoo, Ashok. "A Survey on E-commerce Customer Behavior Analytics: Challenges, Insights and Tools." International Journal of Science, Strategic Management and Technology, vol. 02, no. 6, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i6.060.

Sahoo, Ashok. "A Survey on E-commerce Customer Behavior Analytics: Challenges, Insights and Tools." International Journal of Science, Strategic Management and Technology 02, no. 6 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i6.060.

References

  1. Gandomi and M. Haider, “Beyond the Hype: Big Data Concepts, Methods, and Analytics,” International Journal of Information Management, vol. 35, no. 2, pp. 137–144, 2015.

  2. Zaharia et al., “Apache Spark: A Unified Engine for Big Data Processing,” Communications of the ACM, vol. 59, no. 11, pp. 56–65, 2016.

  3. Databricks Inc., “The Databricks Lakehouse Platform,” Databricks Documentation, 2024. Available: https://www.databricks.com/

  4. A. T. Hashem, I. Yaqoob, N. B. Anuar, S. Mokhtar, A. Gani, and S. U. Khan, “The Rise of Big Data on Cloud Computing: Review and Open Research Issues,” Information Systems, vol. 47, pp. 98–115, 2015.

  5. Jin, B. W. Wah, X. Cheng, and Y. Wang, “Significance and Challenges of Big Data Research,” Big Data Research, vol. 2, no. 2, pp. 59–64, 2015.

  6. Y. Al-Jarrah, P. D. Yoo, S. Muhaidat, G. K. Karagiannidis, and K. Taha, “Efficient Machine Learning for Big Data: A Review,” Big Data Research, vol. 2, no. 3, pp. 87–93, 2015.

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  8. Jacobs, “The Pathologies of Big Data,” Communications of the ACM, vol. 52, no. 8, pp. 36–44, 2009.

  9. Mishra, C. Lin, and H. Chang, “A Cognitive Adopted Framework for IoT Big Data Management and Knowledge Discovery Perspective,” International Journal of Distributed Sensor Networks, vol. 2015, pp. 1–13, 2015.

  10. P. Acharjya, S. Dehuri, and S. Sanyal, Computational Intelligence for Big Data Analysis. Springer International Publishing, Switzerland, 2015.

Ethics and Compliance
✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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