Price Discrimination: How Companies Charge Different People Different Prices for the Same Thing

What Is Price Discrimination?

Price discrimination is the practice of charging different prices to different customers for the same (or substantially similar) product or service, where the price difference isn't justified by a difference in cost. The seller segments the market, identifies different groups' willingness to pay, and captures more total revenue than a single-price strategy would allow.

If that sounds like something companies do all the time, you're right. It is. You encounter price discrimination every single day, often without realizing it. Student discounts at the movies. Senior pricing at restaurants. Airline tickets that cost $200 on Tuesday and $800 on Friday for the exact same seat. Software that costs $10/month for individuals and $50/month for enterprises. All of these are price discrimination.

I want to be clear about something upfront: price discrimination isn't inherently bad, and it isn't always illegal. In fact, most forms of price discrimination are perfectly legal and arguably beneficial. It becomes problematic when it's based on protected characteristics (race, gender, etc.) or when it's wielded by monopolists to extract value from consumers who have no alternatives. But the basic practice of charging different prices to different segments? That's just smart marketing strategy.

The Three Degrees of Price Discrimination

Economists classify price discrimination into three "degrees," a framework that dates back to Arthur Cecil Pigou's work in the early 20th century. Understanding these degrees is essential for any marketer working on pricing.

First-Degree (Perfect) Price Discrimination

First-degree price discrimination means charging each individual customer the maximum price they're willing to pay. It's called "perfect" because the seller captures 100% of the consumer surplus, every dollar that a consumer would have been willing to spend above the market price goes to the seller instead.

In practice, pure first-degree price discrimination is almost impossible because it requires knowing each customer's exact willingness to pay. But some markets come close. Car dealerships, where every buyer negotiates a different price, approximate first-degree discrimination. So do B2B enterprise software sales, where pricing is "contact us" and every deal is customized. Auctions are another example: each bidder reveals their maximum willingness to pay.

What's changed dramatically in the last five years is that AI and data analytics are making first-degree discrimination more achievable than ever. The FTC's 2024 investigation into "surveillance pricing" examined how companies use personal data (browsing history, purchase history, location, device type, even battery level) to estimate individual willingness to pay and set personalized prices. This is first-degree price discrimination powered by algorithms, and it's a frontier that regulators are watching closely.

Second-Degree Price Discrimination

Second-degree price discrimination charges different prices based on the quantity consumed or the version of the product selected. The seller doesn't need to know anything about the individual customer. Instead, they offer a menu of options and let customers self-select into the pricing tier that matches their needs.

Examples are everywhere:

I think second-degree price discrimination is the most elegant of the three because it uses customer self-selection rather than seller classification. The seller doesn't decide who pays what. The buyer does, by choosing which version, quantity, or tier to purchase. It feels fairer to consumers because the choice is in their hands, even though the menu is strategically designed to maximize the seller's total revenue.

Third-Degree Price Discrimination

Third-degree price discrimination charges different prices to different customer groups based on observable characteristics, typically demographics, location, or status. This is the most common form and the one most people think of when they hear "price discrimination."

The logic is straightforward: different groups have different price elasticities. Students are more price-sensitive than working professionals. Weekend travelers are more price-sensitive than business travelers. By offering different prices to different groups, the seller can serve price-sensitive customers (who wouldn't buy at the full price) while still charging the full price to less price-sensitive customers.

Price Discrimination in 2025-2026: The AI Revolution

The biggest story in price discrimination right now is how AI and machine learning are transforming it from a blunt instrument into a precision tool.

Dynamic Pricing Goes Everywhere

Delta Air Lines announced in 2025 that its AI-driven dynamic pricing engine, which adjusts fares in near real-time based on demand signals, currently affects about 3% of domestic tickets, with plans to scale to 20% by year-end. The pricing engine considers flight details, browsing behavior, seat availability, weather, local events, and historical patterns to calculate what each passenger segment might be willing to pay.

JetBlue introduced surge pricing for checked luggage in 2024, charging between $35 and $50 for a first checked bag depending on whether it's "peak" or "off-peak" travel dates. Even ancillary airline services are now dynamically priced.

Surveillance Pricing Under Scrutiny

The FTC's investigation into surveillance pricing, launched in July 2024, represents a significant regulatory response to AI-powered first-degree price discrimination. The investigation found evidence that ride-sharing apps charge different prices for identical rides at the same time, with researchers noting correlations between pricing and user data like phone battery level, typical spending patterns, and willingness to accept surge pricing.

In December 2024, airline executives testified before a Senate subcommittee about AI-driven dynamic pricing, with critics arguing that personalized pricing crosses the line from efficient market behavior into discriminatory extraction.

The Knowledge at Wharton Perspective

Research from Wharton suggests that the most effective dynamic pricing strategies balance revenue optimization with perceived fairness. Companies that are transparent about their pricing algorithms tend to face less consumer backlash than those that price opaquely. The recommendation: use dynamic pricing to offer discounts (which consumers welcome) rather than to impose surcharges (which consumers resent), even when the economic effect is identical.

The Three Conditions for Price Discrimination

Price discrimination doesn't work in every market. Three conditions must be met, and understanding these helps explain why it's common in some industries and rare in others.

Market power. The seller must have some ability to set prices above marginal cost. In a perfectly competitive market where every seller charges the same price, price discrimination is impossible. This is why you see price discrimination in airlines (limited routes, brand loyalty) but not in commodity markets like wheat futures.

Ability to segment. The seller must be able to identify or create distinct customer groups with different willingness to pay. Digital businesses have a massive advantage here because they can segment users based on behavioral data that physical businesses can't easily observe.

Prevention of arbitrage. Customers who receive the low price must not be able to resell to customers who face the high price. This is why price discrimination works for services (you can't resell a haircut) and for digital goods (licenses are tied to accounts) but is harder for physical products that can be resold through grey markets or parallel importing.

Real-World Examples Across Industries

Airlines: The Masters of Price Discrimination

Airlines are probably the most sophisticated practitioners of price discrimination on the planet. A single flight might have 15-20 different fare classes, each with different prices, restrictions, and availability rules. The person in seat 14A paid $189. The person in 14B paid $650. Same flight, same experience, different prices. AI-driven revenue management systems now adjust fares continuously, sometimes changing prices multiple times per day based on demand patterns.

Holiday airfares can triple or quadruple due to demand-based pricing, with fares going from $300 roundtrip to $800+ for peak travel dates. And airlines have found that AI-powered pricing increases profits by 5-30% compared to traditional fare management.

Software/SaaS: The Versioning Play

Virtually every SaaS company practices second-degree price discrimination through tiered pricing. The free tier exists to attract price-sensitive users. The professional tier captures mid-market willingness to pay. The enterprise tier captures maximum corporate budgets with features like SSO, audit logs, and priority support that cost the vendor almost nothing to provide but justify dramatically higher pricing.

Here's what I find interesting: the features that separate SaaS tiers are often chosen not based on their cost to deliver, but on their correlation with willingness to pay. SSO (single sign-on) is a famously expensive add-on in SaaS despite being relatively cheap to implement, because companies that need SSO tend to be larger and less price-sensitive.

Entertainment: The Matinee Model

Movie theaters charging less for matinee shows is classic third-degree price discrimination. The movie is the same. The screen is the same. The only difference is timing, which correlates with willingness to pay. People who can attend a 2 PM showing on a Tuesday are more likely to be price-sensitive (students, retirees, shift workers) than Friday evening audiences.

Geographic Pricing: The Same Software, Different Countries

Software companies routinely charge different prices in different countries based on local purchasing power. A Netflix subscription in India costs roughly $3/month versus $15-23/month in the United States. Same content library (mostly), dramatically different pricing. This is third-degree price discrimination based on geography, enabled by the fact that you can't easily "resell" a Netflix account from one country to another.

Is Price Discrimination Fair?

This is the question that makes pricing conversations interesting, and I don't think there's a clean answer.

On one hand, price discrimination can increase total welfare. When a pharmaceutical company charges high prices in wealthy countries and low prices in developing countries, more people get access to the drug than a single global price would allow. Student discounts enable people with lower incomes to access products they otherwise couldn't afford. Volume discounts reward committed customers.

On the other hand, when price discrimination is opaque (you don't know others are paying less) or based on exploitative data practices (your phone battery level determines your Uber fare), it feels wrong. The FTC's surveillance pricing investigation reflects genuine public concern about where the line is.

My view: price discrimination that's transparent, opt-in, or based on verifiable characteristics (student ID, senior age, geographic location) is generally pro-consumer. Price discrimination that's hidden, algorithmic, and based on personal vulnerability (you're in a rush, your battery is dying, you've searched for this product three times) is troubling, and I think regulators are right to scrutinize it.

How Price Discrimination Connects to Other Concepts

Price discrimination is deeply connected to several other concepts in the Markeview glossary:

Price elasticity is the foundation. Price discrimination works because different customer segments have different elasticities. If everyone had the same elasticity, there'd be no benefit to charging different prices.

Customer equity helps determine how much price discrimination is optimal. Over-extracting from high-willingness-to-pay customers can damage long-term relationship value.

Cross-price elasticity matters when price discrimination affects not just the priced product but also substitute and complementary products.

Competitive pricing strategies must account for competitor price discrimination. If your rival offers student discounts and you don't, you'll lose the student segment entirely.

Conversion rate optimization and price discrimination are increasingly intertwined in digital marketing, where personalized pricing aims to maximize the probability that each visitor converts.

Thought Leaders

Arthur Cecil Pigou created the three-degree classification system in The Economics of Welfare (1920) that economists still use today.

Richard Thaler (Nobel laureate) has studied how perceptions of fairness constrain firms' ability to price-discriminate, finding that consumers accept price differences based on cost differences but reject them when they appear exploitative.

Hal Varian (UC Berkeley, former Chief Economist at Google) wrote extensively on versioning and second-degree price discrimination in information goods, providing the theoretical framework for modern SaaS pricing.

Lina Khan (former FTC Chair) brought regulatory attention to algorithmic and surveillance pricing, arguing that AI-powered personalized pricing represents a new frontier of price discrimination that existing law is ill-equipped to address.

FAQs

What is price discrimination in simple terms?

Price discrimination is when a company charges different prices to different customers for the same product or service. The price difference isn't based on different costs to serve those customers but rather on their different willingness or ability to pay.

Is price discrimination legal?

Most forms of price discrimination are legal. Student discounts, senior pricing, geographic pricing, volume discounts, and dynamic pricing are all legal in most jurisdictions. Price discrimination becomes illegal when it's based on protected characteristics (race, gender, religion) or when it constitutes anticompetitive behavior under antitrust law.

What are the three types of price discrimination?

First-degree (charging each customer their maximum willingness to pay), second-degree (offering different versions or quantities at different prices and letting customers self-select), and third-degree (charging different prices to different identifiable groups like students, seniors, or geographic regions).

How do airlines use price discrimination?

Airlines use multiple forms simultaneously: third-degree (business vs. leisure travelers, advance purchase vs. last-minute), second-degree (economy, premium economy, business, first class), and increasingly first-degree (AI-powered dynamic pricing based on individual browsing behavior and demand signals).

What is surveillance pricing?

Surveillance pricing is the use of personal consumer data (browsing history, purchase history, location, device type) to set individualized prices. The FTC opened an investigation into this practice in 2024, examining how companies use data to estimate individual willingness to pay.

Does price discrimination hurt consumers?

It depends. Price discrimination that makes products accessible to price-sensitive consumers (student discounts, developing-country pricing) increases welfare. Price discrimination that exploits personal data to extract maximum revenue from individuals with few alternatives can harm consumers. The net effect depends on the specific practice and market context.

How is dynamic pricing different from price discrimination?

Dynamic pricing adjusts prices over time based on demand, typically showing the same price to all customers at any given moment. Price discrimination charges different prices to different customers at the same time. In practice, the distinction is blurring as AI enables real-time personalized pricing that combines both.

Can consumers avoid price discrimination?

To some extent. Using incognito browsing, VPNs, clearing cookies, and comparing prices across devices can reduce algorithmic pricing. Shopping during off-peak times, using student or senior IDs, and buying in bulk are ways to benefit from price discrimination structures.

Sources & References

1. Federal Trade Commission. "Predatory or Below-Cost Pricing." https://www.ftc.gov/advice-guidance/competition-guidance/guide-antitrust-laws/single-firm-conduct/predatory-or-below-cost-pricing
2. Analysis Group. "The Rise of Surveillance Pricing." 2025. https://www.analysisgroup.com/globalassets/insights/publishing/2025_the_rise_of_surveillance_pricing.pdf
3. Databricks. "Dynamic Pricing in Airlines: How AI Can Reshape Revenue Strategy." https://www.databricks.com/blog/dynamic-pricing-airlines-how-ai-can-reshape-revenue-strategy
4. The Points Guy. "Here's What You Need to Know About Surge Pricing in Travel." https://thepointsguy.com/news/dynamic-pricing/
5. Knowledge at Wharton. "Dynamic Discounting: How to Do Dynamic Pricing Right." https://knowledge.wharton.upenn.edu/article/dynamic-discounting-how-to-do-dynamic-pricing-right/
6. Al Jazeera. "Surveillance Pricing: Why You Might Be Paying More Than Your Neighbour." October 2025. https://www.aljazeera.com/features/2025/10/15/surveillance-pricing-why-you-might-be-paying-more-than-your-neighbour
7. G2. "What Is Price Discrimination? Types, Benefits, and Examples." https://learn.g2.com/price-discrimination
8. Frommers. "AI-Driven Surge Pricing Comes to Airfares and Hotel Rates." https://www.frommers.com/tips/money-and-currency/ai-driven-surge-pricing-comes-to-airfares-and-hotel-rates/
9. Marketing Scoop. "6 Dynamic Pricing Examples in 2025." https://www.marketingscoop.com/ai/dynamic-pricing-examples/

Written by Conan Pesci | April 2026 | Markeview.com

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