Analysis of Customer Behavior in Online Retail Marketplace Using Hadoop

Authors

  • Garima Shrivastava Microsoft data scientist, MS Computer Science Former Specialist Data Science and Mining JSS Step, Noida, India Author
  • Shailesh Shrivastava PMP, MS Software Engineering, Technical Program Manager, Ericsson Inc, Noida, India Author

Keywords:

Adaptive Retail, Big data, Hadoop, RDBMS

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. 

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References

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Published

2017-09-01

How to Cite

Analysis of Customer Behavior in Online Retail Marketplace Using Hadoop . (2017). International Journal of Innovative Research in Computer Science & Technology, 5(5), 376–380. Retrieved from https://acspublisher.com/journals/index.php/ijircst/article/view/13463