Tailored pricing is here and personal data is the price signal

AI is quietly changing how prices are set online. Beyond demand-based shifts, companies increasingly tailor offers to individuals, using browsing history, purchase habits, device, and location to predict willingness to pay. Two shoppers may see different prices for the same product at the same moment.

Dynamic pricing raises or lowers prices for everyone as conditions change, such as school-holiday airfares or hotel rates during major events. Personalised pricing goes further by shaping offers for specific users, rewarding cart-abandoners with discounts while charging rarer shoppers a premium.

Platforms mine clicks, time on page, past purchases, and abandoned baskets to build profiles. Experiments show targeted discounts can lift sales while capping promo spend, proving engineered prices scale. The result: you may not see a ‘standard’ price, but one designed for you.

The risks are mounting. Income proxies such as postcode or device can entrench inequality, while hidden algorithms erode trust when buyers later find cheaper prices. Accountability is murky if tailored prices mislead, discriminate, or breach consumer protections without clear disclosure.

Regulators are moving. A competition watchdog in Australia has flagged transparency gaps, unfair trading risks, and the need for algorithmic disclosure. Businesses now face a twin test: deploy AI pricing with consent, explainability, and opt-outs, and prove it delivers value without crossing ethical lines.

Would you like to learn more about AI, tech and digital diplomacy? If so, ask our Diplo chatbot!