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The Effects of User Tracking and Behavioral Management on Online Prices: A Theoretical Approach

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  • Christian Niemeier
  • Richard Pospisil

Abstract

Purpose: Only little is known about the technical aspect of dynamic, individual prices in various forms of online-shops as well as how exactly these prices are calculated. The aim of this article is to unveil the variables and patterns behind online dynamic pricing. Design/Methodology/Approach: A Software was witten to gather the necessary database in both, an experimental and non-experimental setting. In addion a statistical regression analysis was conducted to ensure data integrity, by reducing amplitudes and noise from the databasis. Findings: There is vast literature on the topic. Literature is outdated pretty fast, as technology is moving ahead of science finding. Variables such as the user origin, device type, on-page-behavior and eventual cookies from previous website visits rsp. Ads do matter in the finding of the price. In general it could be said, that prices on mobile devices are more dynamic than on desktop versions. Thus, buying on a mobile can either be way cheaper or way more expensive than on a desktop computer. Looking at the GPS data, speaking about the user origin, data shows that there could be a pattern proven, that “discriminates” some countries (e.g., Western-EU, USA) by favoring others, preferably lower-wage markets such as Eastern Europe (Croatia) or India. Practical Implications: The present results suggest how both, vendor and customer can optimize their setting, the data they share and the behavior they show, to optimize the price given in specific situation or on request level, based upon their individual pricing request. Originality/Value: The present study is one of the first studies in the economical framework that does not just list the variables existing, but also linking them together and scientifically prove patterns, as fast as available / statistical relevance could be given.

Suggested Citation

  • Christian Niemeier & Richard Pospisil, 2021. "The Effects of User Tracking and Behavioral Management on Online Prices: A Theoretical Approach," European Research Studies Journal, European Research Studies Journal, vol. 0(3B), pages 386-398.
    • Handle: RePEc:ers:journl:v:xxiv:y:2021:i:3b:p:386-398
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    References listed on IDEAS

    as
    1. Alessandro Bonatti & Gonzalo Cisternas, 2020. "Consumer Scores and Price Discrimination," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 87(2), pages 750-791.
    2. Frederik Zuiderveen Borgesius & Joost Poort, 2017. "Erratum to: Online Price Discrimination and EU Data Privacy Law," Journal of Consumer Policy, Springer, vol. 40(4), pages 521-521, December.
    3. Frederik Zuiderveen Borgesius & Joost Poort, 2017. "Online Price Discrimination and EU Data Privacy Law," Journal of Consumer Policy, Springer, vol. 40(3), pages 347-366, September.
    4. Hindermann, Christoph Michael, 2018. "Price Discrimination in Online Retail," EconStor Preprints 181294, ZBW - Leibniz Information Centre for Economics.
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    More about this item

    Keywords

    Price discrimination; price differentiation; dynamic pricing; online shopping.;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • M39 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Other
    • Z39 - Other Special Topics - - Tourism Economics - - - Other

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