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Analysis and characterization of comparison shopping behavior in the mobile handset domain

Author

Listed:
  • Mona Gupta

    (Indian Institute of Technology Delhi)

  • Happy Mittal

    (Indian Institute of Technology Delhi)

  • Parag Singla

    (Indian Institute of Technology Delhi)

  • Amitabha Bagchi

    (Indian Institute of Technology Delhi)

Abstract

In this work we characterize the session-level behavior of users on an Indian mobile phone comparison shopping website. We also correlate the popularity of handset on various news sources to its popularity on the shopping website. There are three aspects to our study: data analysis, correlation between news sources of product information and popularity of a handset, and behavior prediction. We have used KL divergence to show that a time-homogeneous Markov chain is observed when the number of clicks varies from 5 to 30. Our results depict that Markov chain model does not hold in entirety for comparison shopping setting but tells us how far the Markov chain model holds for this setting. Our analysis corroborates intuition that increasing price leads to decrease in popularity. After the strong correlation between various variables and user behavior was found, we predict the users macro (the overall sales of handset) and micro behavior (whether a user will convert or exit the site) using Markov logic networks. Our predictive model validates the intuition that past browsing behavior is an important predictor for future behavior. Methodology of combining data analysis with machine learning is, in our opinion, a new approach to the empirical study of such data sets.

Suggested Citation

  • Mona Gupta & Happy Mittal & Parag Singla & Amitabha Bagchi, 2017. "Analysis and characterization of comparison shopping behavior in the mobile handset domain," Electronic Commerce Research, Springer, vol. 17(3), pages 521-551, September.
    • Handle: RePEc:spr:elcore:v:17:y:2017:i:3:d:10.1007_s10660-016-9226-7
    DOI: 10.1007/s10660-016-9226-7
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    References listed on IDEAS

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    Cited by:

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    2. Renatas Špicas & Airidas Neifaltas & Rasa Kanapickienė & Greta Keliuotytė-Staniulėnienė & Deimantė Vasiliauskaitė, 2023. "Estimating the Acceptance Probabilities of Consumer Loan Offers in an Online Loan Comparison and Brokerage Platform," Risks, MDPI, vol. 11(7), pages 1-30, July.

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