Understanding Surveillance-based Pricing: The Future of Consumer Sector
In the United States, a disconcerting trend is taking shape known as “surveillance-based pricing” (or Surveillance Pricing). This strategy employs artificial intelligence (AI) algorithms to analyze massive datasets collected from consumers, allowing companies to sell products and services in a highly personalized manner . Imagine a dynamic pricing model that is tailored specifically to you, adjusted based on your buying habits, preferences, and even personal circumstances. The implications of this approach are both fascinating and alarming .
Delta Air Lines: A Case Study in Personalized Pricing
Delta, what are you doing? Delta Air Lines has been exploring this strategy and faced considerable backlash from the public and lawmakers alike. Several American senators recently penned an open letter demanding clarification from Delta’s CEO about their intentions to implement dynamic pricing that varies according to what each customer might be willing to pay . This shift could potentially eliminate static prices, replacing them with a more fluid model that adjusts in real time.
How Personalized Prices Are Calculated
Companies like Fetcher , which collaborates with Delta and Virgin Atlantic, have been pioneering these complex systems since 2019. They utilize deep learning experts and a “Large Market Model,” an AI framework designed to generate customized prices based on an individual’s data. Fetcher’s CEO, Roy Cohen, even stated that this model is trained “with all the data we can collect,” suggesting that financial benefits for airlines could reach up to $4.4 billion annually.
The Ethical Dilemma: Surveillance to Know You Better
Spying to meet you better? This approach raises significant ethical questions. An ex-member of the Federal Trade Commission’s council, Lina Khan, highlighted the potential for disturbing scenarios. For example, an airline could adjust ticket prices upwards if it identifies that a passenger has recently experienced a personal tragedy—like the death of a family member. This kind of exploitation could make an already difficult situation even worse.
Dynamic Pricing: A Shift Away from the Static Model
In July, Delta’s president, Glen Hauenstein, expressed ambitions that by year’s end, 20% of their ticket prices would be determined by AI systems, a significant rise from the mere 3% that was in place at that time. The end goal is to abandon traditional pricing models entirely in favor of a system guided primarily by data, upsetting the perception of what tickets “should” cost based on inherent value.
The Pain Threshold: An Invasive Approach
One troubling aspect of this system is its ability to discern the “pain threshold” of individual passengers. For instance, if a traveler needs to book a last-minute flight for an emergency, the algorithm could discern this urgency and inflate the ticket price accordingly. Conversely, prices might be lower for routine trips, suggesting an imbalance that fuels a market of scarcity for the desperate.
The Consumer Surplus: How Businesses Leverage Data
Central to Delta’s ambitions is the concept of consumer surplus . This term refers to the difference between what a customer is willing to pay and what they actually pay. Companies aim to capture this surplus, and AI offers the potential to do so with pinpoint accuracy . However, this would likely leave consumers with less discretionary income for other expenses.
Legal Hurdles: The European Perspective
While Delta’s plans may face scrutiny in the United States, their implementation in Europe seems more complicated due to the General Data Protection Regulation (GDPR) . This law prohibits automated decisions based on personal data unless clear consent has been obtained, complicating efforts to tailor pricing strategies based on surveillance.
Dynamic Pricing: The Next Evolution
Understanding the distinction is crucial. While dynamic pricing is a well-established practice—adjusting costs based on demand and time—surveillance-based pricing represents an evolution of this model. Companies like Delta and Uber typically adjust rates according to market conditions , but the massive data collection inherent to surveillance pricing sets a new precedent that could change the consumer experience significantly.
In conclusion, the rise of surveillance-based pricing brings both opportunities and dilemmas. As companies become increasingly adept at leveraging data to influence purchasing decisions, the ethical implications of such strategies must be scrutinized. While the promise of customized pricing could enhance revenue streams for businesses, it also paves the way for potential exploitation of vulnerable consumers. The discussion around this practice is not just about economics but also about consumer rights and privacy in a digital age.