Summary of Key Points
This news report focuses on Ctrip being fined 5.179 billion yuan for practices such as using big data to exploit loyal customers and engaging in monopolistic behavior. However, it highlights that the fine only addresses superficial aspects of monopoly (such as exclusive partnerships and mandatory minimum pricing). The real issue at hand—algorithms used for differentiated pricing—is still not resolved. The Consumer Protection Commission has suggested reforms, including reengineering the algorithms and ensuring price transparency, but Ctrip’s response is vague (mere statements about “making algorithms more ethical”). The essence of big data-based price discrimination is that platforms use algorithms to precisely target users’ highest willingness to pay. The challenges in addressing this problem lie in the difficulty for consumers to provide evidence and the lack of transparency in the algorithms. This is not unique to Ctrip; it’s a common issue in the entire platform economy, leading to a significant “trust deficit” among consumers towards online platforms.
I. Big Data-Driven Price Discrimination: The Business Logic Behind Platform Profiteering
Why do platforms target their most loyal users? The primary motivation is profit.
- For example, Netflix’s research shows that using machine learning to analyze user browsing data can increase profits by 14.55% (48 times more than traditional methods). This indicates that platforms are not there to serve you; they want to determine how much you are willing to spend.
- Ctrip’s tactics are even more direct: it charges higher prices to users who use its services frequently and are less likely to compare prices (such as Diamond members). You might think membership levels are privileges, but in reality, they serve as markers for potential profit opportunities.
- The characteristics of travel consumption—low frequency, essential needs, and information asymmetry—create an environment conducive to price discrimination. How often do you book a hotel? By the time you decide, you may already be on your way, leaving no time to compare prices—platforms exploit this.
II. The 5.1 Billion Yuan Fine: Solving Monopoly Issues, but Not the Root Cause of Price Discrimination
The fine primarily targets monopolistic practices (such as forcing hotels to partner exclusively with Ctrip and requiring the lowest prices across all platforms), but the core issue—algorithms—remains unaddressed.
- Of Ctrip’s 19 proposed reforms, only one mentiones improving algorithms, without any specific measures: how will algorithms be audited? Who will oversee them? How can consumers compare prices? These are all empty promises.
- In contrast, the Consumer Protection Commission recommends reengineering the underlying logic of the algorithms (for example, eliminating price differences based on user status), but Ctrip merely states its intention to “make algorithms more ethical,” without providing a clear plan.
III. The Difficulty in Fighting Price Discrimination
Why do consumers often lose lawsuits? Because algorithms are essentially black boxes.
- Take the case of Ms. Hu from Shaoxing: as a Diamond member, she was charged twice the listed price for a hotel. The court ordered Ctrip to refund and compensate three times the amount, but it avoided confirming that the price difference was due to algorithmic discrimination (rather than inventory changes or discounts).
- Although laws like the Anti-Monopoly Law prohibit such practices, the burden of proof falls on consumers. You must prove that the platform used your data to charge more, yet the algorithms are considered trade secrets, making it impossible to verify.
IV. The Consumer Protection Commission’s Proposals: Targeting the Root Causes
The commission has made four key recommendations that hit at the core issues:
1. Reengineer Algorithms: Ensure that everyone sees the same base price for the same room type at the same time, and disclose the reasons for any price fluctuations (e.g., higher prices during peak seasons).
2. Price Transparency: Eliminate mandatory bundled services (such as insurance) and inform consumers that they can compare prices offline.
3. Triple Compensation: Verify price discrimination complaints within 24 hours and compensate three times the difference if confirmed. This shifts the burden of proof to the platform.
4. Cross-Border Regulation: Address price discrepancies between online and offline prices for international hotels (e.g., 7,000 yuan online vs. 4,000 yuan offline in Japan).
- However, these are just suggestions that have not yet been implemented by platforms.
V. A Common Problem in the Industry: The Trust Deficit
Price discrimination is not unique to Ctrip. Meituan was fined 3.4 billion yuan, but similar issues persist; services on Didi and for Apple users are more expensive than those for Android users, and Tmall’s VIP members pay more for products.
- Why don’t platforms change their practices? Because price discrimination is a lucrative model. Removing personalized pricing would significantly reduce profits (e.g., Netflix’s profit would decrease by 14.55%).
- The ultimate victims are consumers. When using apps, they feel anxious about whether the prices are unfair. This trust deficit discourages many from booking hotels or flying online, suppressing potential demand.
The Final Question
Next time you use Ctrip, will the prices displayed be “market prices” or artificially inflated ones tailored by algorithms? No one knows, as the algorithms do not reveal this information to users.
In summary, while fines can address monopolistic issues, true solutions require transparency and accountability in algorithmic pricing practices. Trust cannot be established through mere statements; it requires mechanisms that allow consumers to see how prices are determined and provide a way to seek compensation quickly. Otherwise, promises of “ethical algorithms” remain empty words.