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Using Automated Predictive Analytics in an Online Shopping Ecosystem

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Intelligent Computing and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1172))

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Abstract

This paper is based on a UCI dataset analysis, which includes data generated through Google analytics. The data attributes describe the shopping intent of consumers and their likelihood of exiting the e-commerce website during browsing. This study has selected five attributes out of a total of 18 that were generated and seeks to capture the shopping intent based on the website browsing behavior of shoppers. All selected attributes are in numeric form, and the dependent variable is pagevalue which represents the average value for the user-visited webpage before the completion of a transaction. The proximity of a forthcoming special day is checked for its mediation effect on other independent variables based on web browsing behavior. The findings give some very interesting and relevant insights into the use of analytics on human online behavior.

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Author information

Authors and Affiliations

  1. Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India

    Ruchi Mittal

Authors
  1. Ruchi Mittal
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Correspondence to Ruchi Mittal .

Editor information

Editors and Affiliations

  1. Dpt of Electrical and Electronics Engg, Government College of Engineering, Keonjhar, India

    Subhransu Sekhar Dash

  2. Electronics and Communication Sciences, Indian Statistical Institute, Kolkata, West Bengal, India

    Swagatam Das

  3. Indian Institute of Technology, New Delhi, Delhi, India

    Bijaya Ketan Panigrahi

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Mittal, R. (2021). Using Automated Predictive Analytics in an Online Shopping Ecosystem. In: Dash, S.S., Das, S., Panigrahi, B.K. (eds) Intelligent Computing and Applications. Advances in Intelligent Systems and Computing, vol 1172. Springer, Singapore. https://doi.org/10.1007/978-981-15-5566-4_20

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