How Big Data Is Used for Price Discrimination and the New Regulations to Stop It

With the upcoming National Day and Mid‑Autumn holidays, many people plan trips, and the Ministry of Culture and Tourism has just released the interim regulations on online tourism services that will take effect on October 1, explicitly banning the use of big‑data techniques for price discrimination.

The article describes the online version of “kill‑familiar” (price gouging based on a consumer’s prior behavior), where search frequency and purchase intent are analyzed to dynamically raise prices. A personal example shows how the author, looking for an SSD on a major e‑commerce platform, received higher‑priced recommendations after multiple searches, resulting in a price drop of only ¥40 after 20 days despite the product’s perceived value.

It then details the new regulation: Article 15 of Chapter 2 (“Operations”) prohibits online tourism operators from abusing big‑data analysis to set unfair transaction conditions based on travelers’ consumption records or preferences, protecting consumer rights.

The piece also reflects on the rapid development of big data, noting its integration into everyday activities such as online shopping, game data analysis, and travel planning, while emphasizing the need to safeguard data security, personal privacy, and national security.

Key concerns listed include social safety, personal privacy, and national security, highlighting that massive data generation from ubiquitous mobile devices makes personal information difficult to control.

The article stresses that the regulation aims to curb operators’ “personalized pricing” tactics and promote integrity, urging users to be aware of how their data is used.

Relevant regulatory text is quoted:

平台经营者应当对平台内经营者的身份、地址、联系方式、行政许可、质量标准等级、信用等级等信息进行真实性核验、登记，建立登记档案，并定期核验更新。在线旅游经营者应当提供真实、准确的旅游服务信息，不得进行虚假宣传。

Legal commentary notes that, under consumer protection law, victims of price fraud can claim triple damages with a minimum of ¥500, reinforcing the importance of defending consumer rights against big‑data “kill‑familiar” practices.

Finally, the author asks readers whether they have experienced such price discrimination and calls for broader application of the anti‑kill‑familiar measures across industries.