Dynamic pricing: the consumer law framework

AI dynamic pricing β€” where the price a customer sees depends on their profile, location, browsing history, time of day, device, and purchase likelihood β€” is a standard retail practice that creates consumer law obligations. The core Australian Consumer Law obligation: pricing representations must not be misleading or deceptive. Dynamic pricing does not become misleading merely because different customers see different prices β€” that is how markets work. Dynamic pricing becomes potentially misleading when: a "was" or "original" price is displayed that was not a genuine price at which the product was sold, when the price difference is not transparently disclosed, or when the pricing creates an impression of a discount or special offer that is not genuine.

The ACCC has been active on reference pricing β€” the practice of displaying an inflated "was" price alongside a "now" price to create the impression of a discount. AI-driven pricing that automates this practice does not immunise it from ACL enforcement. The ACCC has pursued reference pricing enforcement actions against major Australian retailers and has signalled that AI-generated pricing practices are within its enforcement scope.

AI personalisation and vulnerable consumers

Retail AI personalisation becomes a consumer protection concern when it targets customers based on vulnerability indicators β€” financial stress, health conditions, or other characteristics that reduce their capacity to make genuinely free purchasing decisions. AI systems that identify customers showing signs of financial stress (frequent small purchases, use of buy-now-pay-later, specific product combinations) and target them with high-interest credit products, gambling promotions, or high-markup convenience products may constitute unconscionable conduct under Australian Consumer Law. The ACCC and ASIC have both addressed the targeting of vulnerable consumers by digital platforms and retailers, and AI-driven targeting does not change the consumer protection analysis.