Over a decade ago, when I was studying industrial economics at university, I clearly remember my professor explaining the concept of "price discrimination" in class. "Price discrimination" is a neutral term that simply refers to a merchant's ability to charge each customer the highest price they are willing to pay for the same product through differentiated pricing strategies. Common real-world examples include tiered ticket prices — student, senior, and adult rates. A more extreme example is the "haggling" that takes place in markets where prices are not publicly disclosed. In China at that time, there were still many markets specializing in cheap counterfeit luxury goods — take Beijing's Xiushui Street, for instance, where merchandise hung in abundance but nothing was marked with a price. Shopping there was a psychological battle: consumers and sellers would push and pull, feigning reluctance as they negotiated the price of a "GUGGI" bag or an "AMANI" shirt. After two or three rounds, a consumer would walk away with their purchase, only to discover that the person coming out of the next stall with the exact same item had paid just one-third as much.
The Theoretical Model Becomes Reality
Merchants exploit information asymmetry and opacity to establish an unequal power structure, on the basis of which they "size up each customer individually." Although a transaction ultimately requires a willing buyer and a willing seller, some people simply end up paying far more than others. The example my professor gave left a deep impression on me, because I was always the eternal loser in these psychological battles — I always paid twice as much as everyone else. Today, people like me have a more vivid name: "leeks" (a term for those who are repeatedly taken advantage of).
My professor noted that a rationally self-interested merchant would capture all consumer surplus — extracting every last dollar you could possibly be pressured into paying — and if it knew enough about your decision-making preferences, risk appetite, willingness to pay, and financial capacity, it could theoretically achieve "perfect price discrimination" with a unique price for every individual. But one-on-one haggling is obviously too costly to scale, so at that time, my professor said, perfect price discrimination was still an abstract theory with no real-world equivalent.
Yet in just ten years — perhaps even less — the theoretical model has grown wings through technology and become reality.
In the era of big data, where the internet connects every aspect of life, consumers are more transparent to merchants than ever before: I know what you regularly buy; I know what different things you purchase at different times; I know your acceptable price range; I know whether you're the deliberate type who adds items to a wishlist or shopping cart and mulls them over, or the decisive type who buys on the spot; I know which web pages helped you make your purchasing decision; I know what your friends like to buy and what needs you and your friends have recently discussed; I may even know your activity records on other apps. There's no need to engage in a sweaty, one-on-one psychological battle at a street market — your desires are completely exposed before the merchant. With a bit of data analysis, they are fully capable of attaching a price tag to your desire that hits exactly your psychological limit — no more, no less.
Even more conveniently, for digital goods that are difficult to price at a standard rate, this approach seems entirely justified. The price of a plane ticket, a night at a guesthouse, a book, an article, or a song cannot be determined by cost alone — it is driven far more by supply and demand. The digital world is naturally more conducive to dynamic pricing based on supply and demand to maximize value, but on top of that, should one also add the "insights" derived from prying into and capturing consumer preferences? The line between angel and devil is sometimes just a single thought away.
User A from Shenzhen, China, once complained online that he regularly booked hotels in the cities he traveled to on business through a certain website, usually at 360 yuan per night. Then one day, while standing in a hotel lobby, he noticed the room rate displayed was 300 yuan per night. When he inquired, he was told the price had changed several months ago. He assumed the booking website hadn't updated its prices in time. But when he logged in again using his wife's account, he was stunned to find that the price shown to his "wife" was actually 298 yuan per night. It was clear that A had been a victim of real-life "big data exploitation of loyal customers" on the platform he had been using since 2012 — and he was far from alone.
Heading Toward Perfect Price Discrimination
We are entering an era of perfect price discrimination. The reason I am writing this article is that I have noticed the media industry beginning to enter this arena as well. The Wall Street Journal uses an algorithm comprising over 60 variables to identify information about each logged-in user, assess the probability of their future subscription, and build a personalized dynamic paywall — some users can read 10 articles for free, while others may hit the wall after just 2, resulting in a 40% increase in subscribers within one year. The Financial Times and Swiss media outlet NZZ have adopted similar approaches. For media organizations, this is of course a welcome development — yet dynamic pricing and big data exploitation of loyal customers are separated by only a thin line. How far can the media go in carefully exploring this path?