PERSONALIZED PRICES IN THE DIGITAL MARKETPLACE: A STUDY OF ALGORITHMIC PRICE DISCRIMINATION AND THE CONSUMER PROTECTION LAW
DOI:
https://doi.org/10.67874/ijlcs.60Keywords:
Algorithmic Discrimination, Consumer Protection, Unfair Trade Practice, Transparency Pricing, Data GovernanceAbstract
There is a growing usage of data driven algorithms in this digital e-commerce era, enabling sellers on the platforms and platforms itself engaging in the price discrimination and personalised pricing and adjustments in real time. The adoption of these practices though on the surface level appear to be efficient and competitive but raise serious concerns regarding transparency, opacity and violation of consumer rights by leveraging consumers personal data to create such personalised pricing in the digital marketplaces. The paper begins to analyse the challenges posed by the algorithmic price personalisation and goes on to examine the effectiveness of the Consumer Protection Act of India, 2019 to effectively address these challenges. The paper employs a doctrinal approach through comparative lens, to understand the statutory provisions and case laws addressing the issue with reference to e-commerce. The paper further delves into the consideration of interplay of consumer law and allied legal framework including data protection and competition law, to identify the appropriate regulatory framework for algorithmic pricing. The questions answered in the paper are: (i) can personalised pricing be regarded as an unfair trade practice as mentioned in the consumer protection act;(ii) how much transparency in pricing, data use, and segmentation is required to be considered as a violation to consumer’s right to be informed and (iii) what kind of remedies are available and desirable. The paper argues that present consumer protection law already has measures to target the algorithmic price personalisation but a clearer and transparent provisions, guidance and regulatory reforms are required to make protections effective in digital marketplaces. The paper proposes a calibrated approach that is strong transparency and auditability requirements of price algorithms, targeted unfair dealing and discrimination measures, enhanced data governance and pro-active regulatory approach. In conclusion, the paper suggests that harmonisation of consumer protection law with technological oversight is necessary to maintain the market, transparency and fairness while still allowing personalised prices in a regulated manner.
References
Nilaydri Shyam & Arun Sharma, Waiting for a Sales Renaissance in the Fourth Industrial Revolution: Machine Learning and Artificial Intelligence in Sales Research and Practice, 69 Indus. Mktg. Mgmt. 135 (2018).
Mona Ashok, Rohit Madan, Anton Joha & Uthayasankar Sivarajah, Ethical Framework for Artificial Intelligence and Digital Technologies, 62 Int’l J. Info. Mgmt. 102433 (2022).
X. Liu, Y. Wang & J. Zhang, AI-Generated Content in Digital Marketing: Opportunities and Challenges, 87 J. Mktg. 30 (2023).
Shahriar Akter, Umme Hani, Yogesh K. Dwivedi & Anuj Sharma, The Future of Marketing Analytics in the Sharing Economy, 104 Indus. Mktg. Mgmt. 85 (2022).
Liyi Chen, Alan Mislove & Christo Wilson, An Empirical Analysis of Algorithmic Pricing on Amazon Marketplace, in Proceedings of the 25th International Conference on World Wide Web (WWW ’16) 1339 (Jacques Bourdeau et al. eds., ACM Press 2016).
Deloitte Digital, Personalising the Customer Experience (2021), [https://www.deloittedigital.com/mt/en/insights/perspective/Personalising-The-Customer-Experience.html](https://www.deloittedigital.com/mt/en/insights/perspective/Personalising-The-Customer-Experience.html) (last visited Mar. 1, 2026).
Katherine Fan, Delta Is Using AI to Determine Some Ticket Prices. What Does That Mean for Travelers?, AFAR (July 31, 2025).
Eugenio Miravete, Price Discrimination: Theory (2005).
A. Israeli & E. Ascazra, Algorithmic Bias in Marketing, Harvard Bus. Sch. Teaching Note No. 521-020 (2020).
N. Duani, A. Barasch & V. Morwitz, Demographic Pricing in the Digital Age: Assessing Fairness Perceptions in Algorithmic Versus Human-Based Price Discrimination, J. Ass’n for Consumer Research (advance online publication 2024), [https://doi.org/10.1086/729440](https://doi.org/10.1086/729440).
T. Kim, K. Barasz, L.K. John & M.I. Norton, When Identity-Based Appeals Alienate Consumers, Harvard Bus. Sch. NOM Unit Working Paper No. 19-086 (2022).
I. Abada, J.E. Harrington, X. Lambin & J.M. Meylahn, Algorithmic Collusion: Where Are We and Where Should We Be Going? (Working Paper 2024).
Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 Apr. 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation), 2016 O.J. (L 119) 1.
Morgan Lewis, Algorithmic Pricing Emerges as Enforcement Priority for EU & UK Antitrust Regulators, LawFlash (Oct. 14, 2025).
Directive (EU) 2019/2161 of the European Parliament and of the Council of 27 Nov. 2019 amending Council Directive 93/13/EEC and Directives 98/6/EC, 2005/29/EC and 2011/83/EU as regards the better enforcement and modernisation of Union consumer protection rules, 2019 O.J. (L 328) 7, recital 45.
Competition & Mkts. Auth., Algorithms: How They Can Reduce Competition and Harm Consumers (Jan. 19, 2021).
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