Big Data and Personalized Pricing

Steinberg, Etye

doi:10.1017/beq.2019.19

Cited by 32 publications

(25 citation statements)

References 43 publications

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“…Indeed, in a recent paper, Steinberg (2020) argues that while personalized pricing may lead 'to better overall social wel-fare…these advantages…are undermined when [it] does not contribute to any socially desired end (other than improved market efficiency in an imperfect market).' He uses a market failures approach, under which 'managers are morally constrained by the point of having competitive markets' (p. 109).…”

Section: Why Would Firms Choose Progressive Pricing?mentioning

confidence: 99%

Progressive Pricing: The Ethical Case for Price Personalization

Coker¹,

Izaret²

2020

J Bus Ethics

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Price discrimination is widely considered unethical/unfair by consumers, as has been borne out by decades of psychological research and mainstream press reporting. However, little academic work has been done to investigate the ethics of price discrimination. The work that has been done to date concludes that while price discrimination is not unethical, despite widespread lay perceptions, it is at best morally neutral. We argue price discrimination is more ethical than unitary pricing, when done 'progressively,' meaning firms charge customers as a function of their willingness-to-pay. We introduce this specific kind of price discrimination as 'Progressive Pricing' and demonstrate it ethically outperforms a 'Unitary Pricing' scheme (where everyone pays the same price, regardless of their willingness-to-pay), at least within a broadly consequentialist framework. We do this by comparing a Unitary Pricing scheme to a Progressive one, analyzing them through the lenses of four different consequentialist 'Social Welfare Functions' (Utilitarian, Egalitarian, Prioritarian, and Leximin), which are used by welfare economists and philosophers to rank the distributions of different social outcomes, concluding that Progressive Pricing is preferred regardless of which Social Welfare Function(s) one finds most plausible.

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Section: Why Would Firms Choose Progressive Pricing?mentioning

confidence: 99%

Progressive Pricing: The Ethical Case for Price Personalization

Coker¹,

Izaret²

2020

J Bus Ethics

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“…For the purposes of this paper, the discussion of competition over consumer surplus between consumers and suppliers is meant to emphasize the adversarial aspect of the interaction between consumers and suppliers. For more discussion on construing the consumer-supplier relation as a competition over consumer surplus, see Steinberg (2020) and Moriarty (2021). For discussion of the moral status of this market adversity, see .…”

Section: Market Failures Market Fairness and Information Asymmetriesmentioning

confidence: 99%

“…For example, if we think of the MF*A as focusing on the fairness of the market as a locus of transactions between individuals; and if we think (as I have suggested) that we should construe this fairness in terms of these different individuals having equal standing in the transaction; perhaps one way to articulate this would be in terms of relational equality. Indeed, some work has already begun incorporating the idea of relational equality to normative questions in business ethics (Landes & Néron, 2015;Néron, 2015;Steinberg, 2020). It would, thus, be interesting to see how we can combine the MF*A with the JFA by appealing to relational equality as a standard of justice.…”

Section: Ways Forwardmentioning

confidence: 99%

Run for Your Life: The Ethics of Behavioral Tracking in Insurance

Steinberg

2021

J Bus Ethics

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In recent years, insurance companies have begun tracking their customers' behaviors and price premiums accordingly. Based on the Market-Failures Approach as well as the Justice-Failures Approach, I provide an ethical analysis of the use of tracking technologies in the insurance industry. I focus on the use of telematics in car insurance and on the use of fitness tracking in life insurance. The use of tracking has some important benefits to policyholders and insurers alike: it reduces moral hazard and fraud, increases actuarial fairness, and incentivizes safe behavior. These benefits, however, are outweighed by significant moral objections. First, the use of tracking technologies significantly undermines the fairness of the interaction between insurance companies and policyholders. Specifically, the use of tracking eliminates information asymmetries, but in such a way that favors insurers exclusively. Furthermore, tracked behaviors and the ability to choose to behave safely are highly correlated with other variables such as income. Therefore, tracking-based insurance relies on and exacerbates existing inequalities and injustices, which undermines the benefits that tracking is supposed to introduce.

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“…Here, an upper threat is the First-Degree Price Discrimination aiming at individual consumer's absolute maximum price or reservation price. Typically, this is theoretically possible only in a monopolist market structures, but Steinberg (2020) asserts that advancements in Big Data identifying individual consumer's reservation prices may soon be possible even in a perfect competition scenario using Big Data. Yet, Mayer-Schönberger and Cukier (2013) asserts a more likely scenario of analytics exacerbation within central planning economic systems, where predictive policing, identical to consumer´s behavior prediction; however, with a broader societal radius it is applicable to law enforcement and intelligence communities.…”

Section: Consumer´s Backgroundmentioning

confidence: 99%

Digital footprints´ wrangling – Are analytics used for better or worse?: Evidence of Danish data governance practices on tech-based services

Abrantes¹,

Ostergaard

2021

Preprint

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Incumbents face nowadays one of their great data governance challenges, regarding the balancing of Big Data´s utilization and the imperative of uplifting the consumer´s sentiment. In the context of the European Union (EU), the prior is especially applicable as a growing opposition against the allegedly hazardous commercial exploitation of digital footprints is ascribed to distort information bundles to the consumer and gradually bias their individual capacity to take informed free choices, configuring a phenomenon of concealed “data autocracy”. Yet, this occurs despite the introduction on May-18 of the General Data Protection Regulation (GDPR).Hence, we have conducted a multi-methods enquire with foci on the Danish market covering the footprint´s awareness and undertaking´s rationale targeting equidistantly the data-owners and data-brokers. This was fashioned with a descriptive-exploratory research purpose to enlighten both the sentiment (perceptions) and the behaviour (actions) and determine the extent of (un)willingness surrounding the dataveillance over personal lives (and firm´s quotidian) and their coherence with regard to self-protection. A dyad of triangulation procedures were applied then for validation purposes and extending our findings.The results of the empirical testing confirmed the most marked disruption of Big Data at individual-level as to the range of benefits and drawbacks. A gap (perception versus action) acknowledged on the two parties uncovered a (paradoxical) laissez-faire of data-owners and institutional isomorphism of data-brokers, and the need for capability building and for the government authorities´ intervention, re-conceptualizing scope and boundaries of collection securitization for building a far more efficient model against citizen´s rights usurpation.JEL: M15; M31; M38.

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Big Data and Personalized Pricing

Abstract: Technological advances introduce the possibility that, in the future, firms will be able to use big-data analysis to discover and offer consumers their individual reservation price (i.e.

Cited by 32 publications

References 43 publications

Progressive Pricing: The Ethical Case for Price Personalization

Progressive Pricing: The Ethical Case for Price Personalization

Run for Your Life: The Ethics of Behavioral Tracking in Insurance

Digital footprints´ wrangling – Are analytics used for better or worse?: Evidence of Danish data governance practices on tech-based services

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