Personalised pricing has shifted from theory to scaled practice in digital markets, blurring the traditional divide between third-degree and first-degree discrimination through profiling, experimentation, interface design, and artificial intelligence. While it can expand output and fund innovation in digital markets, its opacity and targeting can enable exploitative mark-ups and selective responses to rivalry that standard effects-based tests struggle to detect. This study argues that Article 9 of Regulation 1/2003, the commitments procedure, offers a proportionate, forward-looking response to many personalised pricing risks, while classic Article 102 TFEU tools should address proven predatory or excessive pricing. This study maps the mechanisms by which personalisation can undermine rivalry and consumer choice, situates the analysis within recent guidance and the Alrosa framework. Also, it explains the complementary role of market studies and sector inquiries in surfacing patterns and establishing baselines in fast-iterating, design-mediated environments. It integrates insights from behavioural economics and human–computer interaction to show how interface frictions, dark patterns, and price framing can amplify discriminatory outcomes even when underlying algorithms are ostensibly accuracy-optimised. Recognising Article 9′s limits, this study proposes a calibrated sequencing: swift structural and behavioural guardrails via commitments, with fallback to infringement proceedings where harm is shown or non-compliance occurs. It thus offers both a doctrinal and institutional blueprint for regulating personalised pricing that preserves efficiency-enhancing personalisation while strengthening accountability and contestability in European Union digital markets.
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Ahmet Kocabaş (2026) studied this question.
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