By Dr Son Tan Nguyen -
Snapshot
- Algorithmic price personalisation is a nascent practice by businesses that will likely expand as they increasingly adopt the application of big data and AI.
- In the absence of specific legislation, a patchwork of laws – including consumer, privacy and anti-discrimination laws – may apply to use of this practice by businesses and its misuse against consumers.
- This article discusses what constitutes algorithmic price personalisation, how it is being used, how Australian laws currently regulate it and how practitioners can best prepare for matters concerning it.
Powered by advanced algorithms, big data and AI, algorithmic price personalisation is revolutionising the way businesses interact with consumers. While this practice offers benefits such as price efficiency and increased market access for low-income consumers, it also raises significant concerns regarding unfairness and exploitative practices. Although Australian law offers a foundational framework for addressing the risks of algorithmic pricing, significant gaps and uncertainties remain, posing challenges to achieving effective consumer protection.
What is algorithmic price personalisation?
Algorithmic price personalisation is a strategic approach which employs pricing algorithms to tailor prices to individual consumers based on specific attributes, aiming to align as closely as possible with their willingness to pay. Prices do not necessarily reflect costs but are customised through complex algorithms which analyse demographic data (e.g. age, gender, income), geographic variables (e.g. location), behavioural patterns (e.g. browsing history, purchase behaviour) and psychographic factors (e.g. attitudes, lifestyle). Data for these algorithms is collected through observation, volunteered information, behavioural tracking or purchased from data brokers. Unlike dynamic pricing, which adjusts based on external market conditions, algorithmic price personalisation relies on consumer-specific characteristics. Facilitated by machine learning and AI, these algorithms can segment consumers into micro-groups, predict willingness to pay and refine pricing strategies over time. As data accumulates, machine learning continuously adapts, enhancing the accuracy and precision of price personalisation.
