Analysis of Customer Behavior in Online Retail Marketplace Using Hadoop

Garima Shrivastava; Shailesh Shrivastava

doi:10.21276/ijircst.2017.5.5.3

Abstract

To provide a modernized technology foundation for transforming the retail experience, retail CIOs and IT leaders need to support the "seven Cs" of customer interaction: connected, continuous, convenient, contiguous, consistent, collaborative and customized [1]. The biggest differentiator of any online marketplace is how well the store understands its customer and provides the personalized experiences. Retailers interact with customers across multiple channels, yet customer interaction and purchase data is often isolated in data siloes. This paper attempts to identify the use cases to accurately correlate eventual customer purchases with marketing campaigns and online browsing behavior. The data used in this analysis was collected from a start- up online retailer in India.

Keywords

Adaptive Retail, Big data, Hadoop, RDBMS.

References

[1] Sayali Gayakwad, Pranali Nale, Ravindr Bachate, “Survey on Big Data Analytics for digital world”, Advances in Electronics, Communication and Computer Technology (ICAECCT), 2016, published in IEEE International Conference.

[2] Gartner study, “Retail Industry Research Focuses on Digital Business Transformation”, ID: G00310308, Published 28 Jun 2016.

[3] Garima Shrivastava, Ujwala Thakur, “Big data in retail management”, International Journal of Innovative Research in Computer Science & Technology (IJIRCST) ISSN: 2347-5552, Volume-2, 2014, Issue-4.

[4] Gu, L.; Zeng, D.; Li, P.; Guo, S., “Cost Minimization for Big Data Processing in Geo-Distributed Data Centers”, IEEE, 2014, pp. 1.

[5] Dr.Gagandeep Nagra , Dr.R Gopal, “An study of Factors Affecting on Online Shopping Behavior of Consumers”, International Journal of Scientific and Research Publications, Volume 3, 2013, Issue 6, ISSN 2250-3153.

[6] Huang, Wenliang; Chen, Zhen; Dong, Wenyu; Li, Hang; Cao, Bin; Cao, Junwei, “Mobile Internet and Big Data Platform in China Unicom”, IEEE, Vol. 19, 2014, pp. 95-101.

[7] Joseph, R.C.; Johnson, N.A.,” Big Data and Transformational Government”, IEEE, Vol. 15, 2013, pp. 43-48.

[8] Turan, A., H., “Internet Shopping Behaviour of Turkish customers: comparison of two competing models”, Journal of Theoretical and Applied Electronic Commerce Research, Vol.7(1), 2012, pp.77-93.

[9] Nazir, S., Tayyab, A., Sajid, A., Rashid, H., Javed, I., “How online shopping is affecting consumers buying behaviour in Pakistan?”, International Journal of Computer Science Issues, Vol.9(3), 2012, pp.486-495.

[10] Uygun, M., Ozciftci, V., Divanoglu, S., “Factors affecting online shoping behaviour of consumers”, Organizasyon ve Yonetim Bilimleri Dergisi, Vol.3(2), 2011, pp.373-385.

Cites this article as

G. Shrivastava, S. Shrivastava, "Analysis of Customer Behavior in Online Retail Marketplace Using Hadoop", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), vol. 5, no. 5, pp. 376-380, 2017. doi: https://doi.org/10.21276/ijircst.2017.5.5.3

Corresponding Author

Garima Shrivastava

Microsoft data scientist, MS Computer Science Former Specialist Data Science and Mining JSS Step, Noida, India

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