Dynamic pricing is no longer a novelty confined to last-minute airfare and surge-priced rideshares. What began as a mechanism to optimize price based on supply-and-demand in a few industries, is now rapidly expanding its reach, and growing more sophisticated in how it is used to target individual consumers.
The foundational logic of dynamic pricing is well established in transportation. Rideshare platforms like Uber and Lyft adjust fares based on real-time driver availability relative to trip demand, accounting for variables like weather, major events, time of day, and traffic conditions. For years, Airlines have varied prices based on booking lead time, travel dates, and loyalty status. Hotels in event-heavy cities have applied similar models for years. These practices, while sometimes frustrating, reflect genuine supply constraints.
What is new is the scope of expansion. NJ Transit recently proposed raising the round-trip fare from Penn Station to MetLife Stadium from $12.90 to approximately $150 during this summer’s FIFA World Cup. A roughly 1,163% increase for an 18-mile rail journey (Morning Brew). Shipping giants UPS and FedEx, which historically announced predictable annual rate increases, now introduce smaller, more frequent price adjustments throughout the year. Streaming platforms like Netflix time subscription hikes to content launches rather than fixed annual cycles (Forbes). Dynamic pricing is no longer confined to transportation; it is a cross-industry structural shift.
The more significant development is the move from supply-driven surge pricing toward what is increasingly being called surveillance pricing. Surveillance pricing is the use of personal data to set individualized prices based on estimated willingness to pay. Delta Air Lines is deploying AI to determine the maximum price each traveler will accept for a given flight, currently applied to 3% of ticket prices with plans to reach 20% by year-end (The Verge). JetBlue faces a proposed class action lawsuit alleging it uses customers’ browsing histories, location data, and other personal information to set individual fares (The Guardian). Uber has faced scrutiny after tests demonstrated that two identical trip requests were priced differently based on the requesting device’s battery level (Vice). The lower-battery phone was charged more, suggesting an inferred urgency signal.
The distinction between the pricing models matters. Traditional dynamic pricing responds to market conditions. Surveillance pricing responds to individual data profiles. The former is a function of supply and demand; the latter is a function of what a company believes a specific person will pay before walking away.
The trajectory raises legitimate questions for businesses and consumers alike. If airlines and transit systems are repricing based on personal data today, the logical extension reaches further – into grocery retail, restaurant pricing near high-demand venues, parking infrastructure, and local transit routes. The technology enabling these models is not industry specific.
For businesses evaluating pricing strategy, the opportunity is real, but so is the risk. Pricing that extracts short-term margin at the cost of consumer trust is not a durable growth strategy.
Red Chalk Group works with clients to develop pricing strategies that reflect genuine market dynamics rather than exploiting information asymmetry.
