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First Degree Price Discrimination Using Big Data

Author

Listed:
  • Benjamin Reed Shiller

    (Economics Department, Brandeis University)

Abstract
Second and 3rd degree price discrimination (PD) receive far more attention than 1st degree PD, i.e. person-specific pricing, because the latter requires previously unobtainable information on individuals’ willingness to pay. I show modern web behavior data reasonably predict Netflix subscription, far outperforming data available in the past. I then present a model to estimate demand and simulate outcomes had 1st degree PD been implemented. The model is structural, derived from canonical theory models, but resembles an ordered Probit, allowing methods for handling massive datasets. Simulations show using demographics alone to tailor prices raises profits by 0.14%. Including web browsing data increases profits by much more, 1.4%, increasingly the appeal of tailored pricing, and resulting in some consumers paying twice as much as others do for the exact same product. There is an updated version of this paper. Personalized Discrimination Using Big Data, working paper #108.

Suggested Citation

  • Benjamin Reed Shiller, 2013. "First Degree Price Discrimination Using Big Data," Working Papers 58, Brandeis University, Department of Economics and International Business School, revised Jan 2014.
    • Handle: RePEc:brd:wpaper:58
    as

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    File URL: http://www.brandeis.edu/economics/RePEc/brd/doc/Brandeis_WP58R2.pdf
    File Function: Revised version, 2014
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    File URL: http://www.brandeis.edu/economics/RePEc/brd/doc/Brandeis_WP58R.pdf
    File Function: Revised version, 2013
    Download Restriction: no
    File URL: http://www.brandeis.edu/economics/RePEc/brd/doc/Brandeis_WP58.pdf
    File Function: First version, 2013
    Download Restriction: no
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    References listed on IDEAS

    as
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