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Price Discrimination and Big Data

For many industries where it is possible to sell products online, first-degree price discrimination associated with dynamic pricing is viewed as an effective method to generate higher profits. However, in comparison to more common and less controversial second- and third-degree discrimination, first-degree price discrimination provokes much debate regarding the fairness of this approach in relation to consumers. Although dynamic pricing can be discussed as unfair because of manipulating personal data, this type of price discrimination is appropriate to be applied in online commerce because of its positive impact on economics.

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First-degree price discrimination is a subject for analyzing in many sources because of its specific controversial nature and a potentially negative impact on consumers. However, the model developed for Netflix demonstrated that “combining traditional demographic data with information about Web browsing habits led to much more accurate predictions” about subscriptions, and the process of adjusting prices according to consumers’ readiness to pay “led to higher profits in simulations” (Fung). This approach to determining prices for potential consumers depending on their browsing history and demographic data should be viewed as legal. Kestenbaum explained this aspect noting that “Price differences can’t be based on race, religion, national origin or other bases for illegal discrimination.” Thus, the discussed method of pricing is appropriate for all other non-discriminatory cases. However, there is a view that first-degree price discrimination should be avoided in online retailing because of manipulating personal data (“Exposing Price Discrimination”). Therefore, e-commerce organizations can use only information that is legally available to them. In this context, the practice of setting different prices can be viewed as not ideal ethically, but it is legal.

One more argument to state that this price discrimination is appropriate in spite of being not fair in all cases is associated with positive effects on economic growth. According to Rosalsky, “flexible pricing is good for society, at least when it comes to overall economic efficiency and growth … more trades get done and more stuff gets made.” This idea is also supported by other experts claiming that these algorithms promote purchases if they are used efficiently and securely (“Exposing Price Discrimination”). However, those who oppose price discrimination and cohort pricing state that “there is a moral responsibility in personalization to make sure that consumers aren’t abused in how they make decisions” (Kestenbaum). For example, the practice of first-degree price discrimination is applied in the airlines industry for a long time, and it is possible to assume that e-commerce will adopt this practice or cohort pricing without affecting users unethically.

If online retailers actively adopt the principles of flexible pricing in their practice, they will not use the most aggressive method. Kestenbaum discusses this situation with reference to cohort pricing, stating that many retailers will avoid using such pricing. For example, Amazon will not be “an early adopter of cohort pricing because they present themselves as more transparent and deal-oriented than the use of cohort pricing would allow” (Kestenbaum). Currently, in online shopping, first-degree price discrimination is not perceived as extremely unfair. Rosalsky claims that the algorithms for collecting and analyzing personal browsing data and cookies will improve in the future. Consequently, it is possible to expect more benefits for retailers, but fewer threats to consumers. Algorithms and big data can be used in electronic commerce to stimulate purchases and generate gains; on the other hand, the use of these systems should be effectively regulated (Rosalsky). Thus, “the government’s antitrust authorities should monitor them [algorithms] and intervene if there’s a case they’re being used anti-competitively” (Rosalsky). The reason for that is the necessity to guarantee that price discrimination is a fair approach that is not oriented against consumers.

While referring to the analyzed sources, it is possible to state that first-degree price discrimination becomes more typical for modern markets, especially for the sphere of online retailing. Despite the fact that many experts and general consumers view dynamic pricing as an unfair practice discriminating some people, there are also many ideas that this approach to setting prices is correlated with the modern economic and marketing realities. As a result, this flexible and algorithmic pricing can contribute to developing a competitive effective market and industries generating high revenues on a regular basis. It is declared in the examined literature that big data are used for modelling price offers in a secure manner. Therefore, it is possible to expect that the utilization of personal information collected as a result of analyzing online activities will be safe for most consumers preferring shopping online.

Works Cited

“Exposing Price Discrimination in Online Shopping (Marketplace).” YouTube, Web.

Fung, Brian. “How Netflix Could Use Big Data to Make Twice as Much Money off You.” The Washington Post, 2013. Web.

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Kestenbaum, Richard. “Your Friend May Pay Less Than You for the Same Things You’re Buying.” Forbes, 2018. Web.

Rosalsky, Greg. “When Computers Collude.”, 2019. Web.

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1. StudyCorgi. "Price Discrimination and Big Data." January 6, 2022.


StudyCorgi. "Price Discrimination and Big Data." January 6, 2022.


StudyCorgi. 2022. "Price Discrimination and Big Data." January 6, 2022.


StudyCorgi. (2022) 'Price Discrimination and Big Data'. 6 January.

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