The End of Fair Pricing: How Apps and Algorithms Let Companies Charge You More

Groundwork Collaborative's Lindsay Owens and American Prospect's David Dayan reveal how companies use smartphone apps, data aggregation, and algorithmic pricing to maximize what every individual customer will pay, creating a new era of price discrimination that challenges traditional economic models.

The surge in sophisticated pricing strategies represents a fundamental shift from the 20th century's "one price" philosophy back to marketplace-style haggling, but powered by artificial intelligence and mass surveillance rather than human negotiation.

Key Takeaways

• McDonald's app exemplifies data-for-discounts model, collecting location, payment, and behavioral data to predict optimal pricing for individual customers
• Algorithmic price-fixing through companies like RealPage and Agastio enables competitors to coordinate price increases without direct communication
• Personalized pricing algorithms consider factors like phone battery level, payday timing, and geographic location to maximize willingness to pay
• The shift from cost-cutting to revenue maximization marks a new business era where pricing sophistication matters more than production efficiency
• Legal frameworks struggle to address algorithmic collusion and surveillance-based price discrimination affecting protected classes
• Federal Reserve monetary policy becomes less effective when prices depend more on algorithms than traditional supply-demand dynamics
• Industries beyond airlines are adopting "junk fee" strategies, unbundling services to extract maximum revenue through add-on charges
• The "time tax" of navigating apps and rewards programs disproportionately impacts lower-income consumers with less flexibility to optimize deals

Timeline Overview

• 00:00–15:30 — McDonald's App Economy: How companies offer app discounts to collect personal data enabling personalized pricing based on location, spending patterns, and behavioral cues
• 15:30–28:45 — Algorithmic Price Fixing: Data aggregation companies like RealPage and Agastio help competitors coordinate pricing without direct communication, challenging traditional antitrust frameworks
• 28:45–42:20 — Historical Context: The evolution from marketplace haggling to 20th century fixed pricing and back to personalized discrimination through technology
• 42:20–55:15 — Surveillance Pricing Examples: Uber charging more for low phone batteries, Staples offering geographic discounts based on local income levels
• 55:15–68:30 — Legal and Regulatory Challenges: How existing antitrust and discrimination laws struggle with algorithmic pricing and proxy profiling
• 68:30–81:45 — Disparate Impact Analysis: Why personalized pricing often charges higher prices to poor and minority communities despite economic theory predictions
• 81:45–End — Macroeconomic Implications: How sophisticated pricing strategies challenge Federal Reserve policy and traditional inflation measurement

The McDonald's Surveillance Economy: Data for Discounts

The McDonald's app represents a paradigm shift where consumers trade personal data for lower prices, enabling unprecedented personalized pricing based on predictive algorithms that know customers better than they know themselves.

• McDonald's partners with Plexure, which also works with "Ikea, 7-Eleven, White Castle" to create sophisticated customer profiling systems
• The app collects "location, payment, and behavioral data" to understand "what you're doing on that phone where you are at particular times of day"
• Predictive pricing considers "payday timing" - offering "$3 McMuffin on Thursday" when money is tight but "$4" on Friday after payday
• Environmental factors influence prices: "if it knows that it's cold out it might raise the price of hot coffee if it knows it's hot out it might raise the price of a McFlurry"
• "Identity graphs" combine app data with "email, social media, browser, subscriptions, other app downloads, travel history, retail history"
• The system's "predictive power" can determine "what you're going to buy maybe before you even know" enabling precision pricing
• Customer isolation through apps eliminates price comparison: "if you're buying through an app there is no public price there's just a price for you"

This model transforms fast food from commodity pricing to personalized luxury pricing while maintaining the illusion of accessible discounts.

Algorithmic Price Fixing: Coordination Without Communication

Companies increasingly use third-party data aggregators to achieve price coordination that traditional antitrust law struggles to address, creating industry-wide price increases without explicit communication between competitors.

• Airlines pioneered this through ATPCO (Airline Tariff Publishing Company) which "collects real-time data on every fare" allowing competitors to "adjust their prices in real time"
• Agastio serves meat packing industry by collecting "real-time proprietary data from all meat packing producers" and distributing it in "giant books" to competitors
• RealPage enables rental market coordination by collecting "all of their pricing data all of their supply data" from landlords and distributing it broadly
• The legal challenge: "if you do it through an algorithm it's sort of more of an open question" compared to executives meeting "in a room"
• Algorithm-based coordination allows companies to discover "oh I can probably raise my price because I'm underpriced relative to my competitor"
• Result is systematic "tendency to ratchet prices upward" across entire markets without traditional collusion evidence

These platforms effectively legalize price fixing by automating what would be illegal if done through direct communication.

The Return to Marketplace Haggling

Modern personalized pricing represents a return to pre-industrial marketplace dynamics where individual negotiation determined prices, but now powered by artificial intelligence rather than human interaction.

• Historical context: markets originally involved haggling where sellers "took a look at you and maybe looked at your shoes" to set prices
• Quakers in Philadelphia opposed price discrimination as violating "religious principles that every man was equal under God"
• John Wanamaker created fixed pricing with "one price and goods returnable" plus the "money back guarantee" for department store efficiency
• 20th century established "by and large one price for goods" across retail markets
• Technology enables return to individual pricing: "we're really in some ways returning to the bazaar or the marketplace"
• Key difference: algorithmic precision replaces human judgment in price discrimination
• Scale transformation: "personalized pricing" affects millions simultaneously rather than individual transactions

The shift represents abandonment of egalitarian pricing principles in favor of maximum revenue extraction through technological surveillance.

Surveillance Pricing in Practice: The Battery and Geography Factors

Real-world examples demonstrate how companies exploit desperation, location, and circumstantial factors to maximize individual willingness to pay through algorithmic analysis of personal data.

• Uber Belgian study found the app "charged more if the individual's phone battery was low" - 12% battery paid more than 84% battery
• Geographic discrimination: Staples offered "different prices in different geolocations based on IP address" with "higher average income" areas receiving discounts
• Location-based pricing exploits segregation: "if either the destination or pickup point had a higher percentage of nonwhite residents" ride-sharing cost more
• Temporal exploitation: algorithms know "you only have an hour between jobs or while you're going to school to grab some lunch"
• Desperation pricing: companies target moments when "you have to eat and you're out and about"
• Capability varies by market power: pricing strategies work when "you have a tremendous amount of market power and therefore pricing power"

These practices demonstrate how algorithmic pricing exploits individual vulnerabilities rather than reflecting economic efficiency.

The Legal Frontier: When Algorithms Discriminate

Existing legal frameworks prove inadequate for addressing algorithmic price discrimination that often produces disparate impacts on protected classes while maintaining plausible deniability about intentional bias.

• Geographic pricing inevitably creates racial bias: "any set of pricing that relies in whole or in part on geography" produces "racial bias intended or unintended"
• Protected class proxies: algorithms can identify race, gender, age through "what type of browser they're using what type of phone where they are"
• Legal uncertainty: Rainmaker hotel pricing case dismissed because algorithms only "recommended" prices despite "90% of the time the recommendation is taken"
• FTC tools exist for "unfair and deceptive practices" but application to algorithmic pricing remains unclear
• Traditional antitrust focuses on explicit coordination while algorithmic coordination operates in legal gray areas
• Enforcement challenges: proving algorithmic discrimination requires technical expertise courts often lack

The legal system's inability to address algorithmic discrimination leaves consumers vulnerable to systematic exploitation.

The Time Tax: How Complexity Penalizes the Poor

The proliferation of apps, rewards programs, and dynamic pricing creates a "time tax" that disproportionately impacts lower-income consumers who lack time and resources to navigate optimization strategies.

• Organized consumers historically used "Sunday papers and pull together three sets of coupons" but modern systems exceed individual capacity
• Real-time pricing eliminates traditional deal-hunting: "by the time you get in your car and drive up to the Kroger the price of Cheerios has already changed"
• Poor single mothers "working two jobs" have "less time to try to game the system" compared to affluent consumers
• Class-based advertising segmentation shows poor people "ads for payday lenders" while wealthy see "brokerage accounts or luxury waterfront property"
• Electronic price tags enable instant price changes that make coupon strategies obsolete
• Result: "even consumers with considerable time" cannot "coupon clip their way out of this one"

The complexity of modern pricing creates systematic advantages for affluent consumers while penalizing those with limited time and resources.

Junk Fees: The Airline Model Goes Everywhere

Airlines pioneered unbundling strategies that extract maximum revenue through add-on fees, with consultants now spreading these "ancillary revenue" models across unrelated industries.

• Idea Works Company runs "ancillary revenue master class" - literally "a junk fee boot camp" teaching fee extraction
• Airlines unbundled tickets adding "baggage fees and change fees and fees if you want a better seat"
• Suburban Propane exemplifies expansion beyond airlines with comprehensive fee schedule including "safety practices and training fee, tank rental fee, transportation fuel fee"
• Complete fee list: "restocking fee, tank pickup fee, minimum monthly purchase fee, system leak test fee, reconnect fee, wheel call fee, forklift minimum delivery fee"
• Additional charges: "diagnostic fee, installation fee, early termination fee, emergency special delivery fee, late fee, return check fee, meter account maintenance fee"
• Strategy transforms simple transactions into complex fee structures maximizing revenue extraction

The proliferation of junk fees represents systematic effort to disguise true costs while maximizing revenue through psychological manipulation.

Macroeconomic Disruption: When Pricing Escapes Federal Reserve Control

Sophisticated corporate pricing strategies increasingly disconnect prices from traditional supply-demand dynamics, challenging Federal Reserve monetary policy effectiveness and conventional inflation measurement.

• Jerome Powell appeared "very uncomfortable" discussing algorithmic pricing, insisting on "freedom" for corporate pricing strategies
• Traditional monetary policy assumes pricing responds to interest rates and economic fundamentals
• Algorithmic pricing can ignore supply-demand signals when companies have sufficient market power
• "Whole of government approach" needed as "outsourcing any question about inflation to the central bank" proves insufficient
• Inflation measurement misses "price gouging, junk fees, dynamic pricing" that consumers experience differently than traditional inflation
• Policy makers must "study individual firm behavior industry level behavior" to understand modern economy
• Fed's traditional "playbook" inadequate for economy where prices depend on algorithms rather than market forces

The disconnect between algorithmic pricing and monetary policy creates new challenges for economic management and inflation control.

Common Questions

Q: How do apps like McDonald's use customer data for personalized pricing?
A: Apps collect location, spending patterns, and behavioral data to create predictive models that optimize pricing based on individual circumstances like payday timing and desperation levels.

Q: Is algorithmic price coordination legal?
A: Current antitrust law struggles with algorithmic coordination since companies use third-party platforms rather than direct communication, creating legal gray areas courts haven't fully addressed.

Q: Why do poor people often pay higher prices despite economic theory?
A: Algorithms exploit desperation, limited competition in low-income areas, and reduced time for deal optimization rather than simply reflecting ability to pay.

Q: How does personalized pricing affect Federal Reserve policy?
A: When prices depend more on algorithms than supply-demand dynamics, traditional monetary policy becomes less effective at controlling inflation and economic activity.

Q: What legal protections exist against discriminatory pricing?
A: Limited protections exist since geographic pricing creates racial proxies while algorithmic systems maintain plausible deniability about intentional discrimination.

The transformation of corporate pricing represents a fundamental shift in how markets operate, moving from transparent, competitive pricing toward opaque, surveillance-based price discrimination. While companies argue these strategies improve efficiency and customer targeting, the evidence suggests they primarily serve to extract maximum revenue from vulnerable consumers while creating systematic disadvantages for those with limited time, resources, or market power. The challenge for policymakers lies in developing new regulatory frameworks adequate for an economy where artificial intelligence and mass surveillance enable unprecedented price manipulation that traditional economic theory and legal frameworks weren't designed to address.

Practical Implications

• Understand app-based pricing trade-offs — Recognize that discount apps collect extensive personal data used for algorithmic price targeting and future revenue optimization
• Monitor market concentration enabling pricing power — Track industry consolidation that provides companies sufficient market power to implement sophisticated pricing strategies without competitive pressure
• Develop detection methods for algorithmic coordination — Create tools to identify when third-party platforms enable price coordination between competitors without explicit communication
• Strengthen legal frameworks for proxy discrimination — Update civil rights and consumer protection laws to address algorithmic systems that create disparate impacts through geographic and behavioral targeting
• Implement pricing transparency requirements — Consider regulations requiring companies to disclose when prices vary by customer or to show price ranges rather than personalized offers
• Educate consumers about pricing complexity — Provide resources helping people understand how personal data influences pricing and strategies for minimizing exploitation
• Adapt macroeconomic models for algorithmic pricing — Update Federal Reserve and other economic models to account for pricing strategies that operate independently of traditional market forces
• Create "time tax" awareness and mitigation — Recognize how complex pricing systems disadvantage time-constrained consumers and develop policies addressing these disparate impacts