There are three levels of price discrimination depending on the degree of differentiation.
The first level of price discrimination, also known as perfect price discrimination (perfect discrimination), that is, different prices charged to different customers, the pricing for each customer is closer to the customer's willingness to pay, to achieve "thousands of thousands of prices", is the seller's profit maximization pricing strategy. The second level of price discrimination, according to different purchase volume, set different prices. Tertiary price discrimination, i.e. setting different prices for consumers in different markets. Including specific country pricing, membership discounts, listing time differential pricing. During the double "11" shopping festival, the pricing mechanism of merchants is the second and third level price discrimination. E-commerce platform competition is fierce, simple price discounts are easily compared by consumers. Through the complex and confusing combination of discounts, reducing the consumer's ability to distinguish between the price comparison of goods on different platforms.
Beijing-Shanghai high-speed railway has implemented the floating fare mechanism since December 23, 2020, and the adjusted fares have risen and fallen. Although the secretary of the Beijing-Shanghai high-speed rail director said, "the introduction of this policy, the core of it is not a price increase, through the establishment of a rise and fall of a mechanism that can regulate supply and demand ...." , but floating pricing is essentially a third level of price discrimination, the purpose is to increase profits.
Perfect price discrimination is the ultimate goal chased by merchants. It achieves total consumer surplus capture and maximizes profits.
To achieve perfect price discrimination it is necessary to understand each customer's willingness to pay and track the hotness of goods in the market in real time. Big data collects information about consumers, analyzes each consumer's geographic location, age, preferences, spending habits, and financial capabilities, and saves a "profile" of each consumer. Big data analysis provides technical support for merchants to realize perfect price discrimination. For example, if the analysis results show that consumers have a high income level and low price sensitivity, then the commodity pricing no discount or low discount; if the customer has a low income level and high price sensitivity, then the commodity pricing high discount. Another example is that old customers have high stickiness and habitually consume on the same platform, while new users have low stickiness, and price discounts are highly attractive to them and are conducive to cultivating the consumption habits of new users on this platform.
Some price discrimination is common in the market, open, and accepted by consumers by default, including:
Differences in quantity, i.e., buying more and counting less; differences in time, e.g., the longer the electronic product is on the market, the lower the price, the lower the price of night flights; differences in age, e.g., the elderly, the price of children preferential treatment; differences in the cost of time, e.g., collecting discount coupons, taking part in the game to get the discount coupon; customer Time cost difference, such as collecting discount coupons, participating in games to get discount coupons; customer cost difference, such as new user bonus; but it is difficult for consumers to accept the system to set different prices for the same products according to their "profiling". This is because they feel their privacy is being violated, and because user profiling is non-public, non-transparent information.
Theoretically, big data "kills maturity" is one of the means to realize perfect price discrimination in economics. But the implementation process will face social criticism, after all, the Internet era is no secret, will soon be discovered by the users and feel unhappy.
Currently, China's consumer protection law does not explicitly include big data "kill familiar" behavior into the scope of regulation, there is no punitive legislation. 20 August 20, 2020, China issued the "Interim Provisions on the Management of Online Tourism Business Services," which Article 15 clearly stipulates that the online tourism operator shall not use big data and other technological means to target different consumer characteristics. technical means, for travelers with different consumption characteristics, to set differentiated prices for the same product or service under the same conditions. But also limited to online travel agency (OTA) enterprises, the penalty is also limited to "can be reminded through interviews and other administrative guidance, warning, stop, and ordered to rectify".
As early as 2000, Amazon used big data analysis to implement differential pricing for DVDs. Older users were priced $4 more than newer users. When it came to light, CEO Bezos personally apologized and refunded the difference. Amazon did not stop using big data pricing because of this, and has since improved to more subtle dynamic pricing, such as raising the price of an item based on news reports, which adjusts the price of products on the platform up to 2.5 million times a day, reportedly boosting profits by up to 25%. Currently, China's e-commerce platforms widely use dynamic pricing mechanisms, and users will often find that the price of goods changes a short time apart.
The dynamic pricing mechanism makes the implementation of perfect price discrimination more and more subtle, consumers are dissatisfied but can do nothing about it, and price-sensitive consumers have to pay more time costs for this. Big data analytics makes perfect price discrimination possible, and tangible profits give merchants enough incentive to keep chasing and using it to improve pricing mechanisms.