Surveillance Pricing: When Algorithms Charge You More Because of Who You Are
FTC confirms companies use your data to set personalized prices. Wendy's surge pricing backlash. RealPage rent-fixing lawsuit. How to detect and fight back.
By Compens.ai Editorial Team
Insurance Claims Expert
Surveillance Pricing: When Algorithms Charge You More Because of Who You Are
Updated: December 2025
The Price Is Different for You
Two people open the same app, looking at the same product. One sees $29.99. The other sees $39.99. The difference? An algorithm analyzed their data—browser history, location, purchase patterns, device type, even credit score—and decided one would pay more.
This is surveillance pricing, and it's everywhere. Retailers like Amazon and Walmart have used dynamic pricing for years. Uber charges different riders different rates for identical trips. Hotels show different prices based on your device. And now, thanks to artificial intelligence, the practice is spreading to everything from fast food to insurance.
The FTC launched an investigation in 2024 and released findings in January 2025 confirming what many suspected: companies use an alarming range of personal data to set individualized prices, often targeting vulnerable consumers for higher charges.
When Wendy's announced plans for "surge pricing" in February 2024, the public backlash was swift and fierce. But Wendy's was just saying out loud what many companies already do quietly.
Surveillance Pricing by the Numbers
| Statistic | Figure | |-----------|--------| | Companies using AI for pricing | 80%+ of major retailers | | Uber price variation | Up to 4x for same route | | Hotel price variation by device | 20-30% difference | | Amazon price changes | 2.5 million+ daily | | Consumers aware of personalized pricing | Only 35% | | Support for banning personalized pricing | 72% |
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Understanding Surveillance Pricing
What Is Surveillance Pricing?
Surveillance pricing uses your personal data to set prices specifically for you. Unlike traditional dynamic pricing (which changes prices based on supply and demand), surveillance pricing changes prices based on who you are.
What companies know about you:- •Location (down to your specific neighborhood)
- •Device type (iPhone vs. Android, new vs. old)
- •Browser history and shopping patterns
- •Previous purchase amounts
- •Credit score and income estimates
- •Time of day and purchase urgency
- •Whether you've searched competitors
- •Your social media activity
- •Loyalty program data
Types of Algorithmic Pricing
Dynamic Pricing
Prices change based on supply and demand—like airline tickets or Uber surge pricing. The same price applies to everyone at that moment.
Example: Uber charges more during rush hour because demand exceeds supply.
Personalized Pricing (Surveillance Pricing)
Prices change based on individual characteristics. Different customers see different prices at the same time.
Example: You see a higher price because the algorithm knows you always buy anyway.
Algorithmic Collusion
Companies use shared pricing algorithms that learn to coordinate price increases without explicit agreement.
Example: The DOJ is suing RealPage for allegedly enabling landlords to coordinate rent increases through shared software.
Where You'll Encounter It
E-commerce:- •Amazon changes prices millions of times daily
- •Online retailers track browsing and adjust prices
- •"Abandoned cart" emails may offer lower prices
- •Airlines pioneered dynamic pricing
- •Hotels charge more based on search history
- •Car rentals adjust by device and location
- •Uber and Lyft use surge pricing
- •Prices vary by location, time, and individual factors
- •Algorithm knows when you "need" the ride
- •Premiums based on data beyond traditional factors
- •"Usage-based" insurance tracks driving behavior
- •Health data increasingly factors into pricing
- •Electronic shelf labels enable in-store dynamic pricing
- •Grocery apps show personalized prices
- •Loyalty programs enable price discrimination
- •RealPage and similar software coordinate rent prices
- •Landlords use algorithms to set "market" rates
- •DOJ lawsuit alleges algorithmic collusion
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The FTC Investigation
What the FTC Found
In January 2025, the FTC released findings from its surveillance pricing study:
Personal data used includes:- •Precise location
- •Browser history
- •Device type and age
- •Time of browsing
- •Purchase history
- •Loyalty status
- •Credit information
- •Demographic data
- •Cosmetics companies targeting promotions based on "skin types and skin tones"—raising discrimination concerns
- •Prices adjusted based on perceived urgency or need
- •Higher prices for consumers in certain zip codes
- •Differential pricing that disadvantages vulnerable groups
Discrimination Concerns
The FTC noted that surveillance pricing "can discriminate to the detriment of historically disadvantaged groups":
How discrimination occurs:- •Zip code pricing correlates with race
- •Device type correlates with income
- •Algorithm learns to charge more to vulnerable populations
- •"Personalization" can mask discriminatory intent
- •Civil Rights Act may apply to discriminatory pricing
- •State anti-discrimination laws
- •Privacy laws with non-discrimination provisions
Investigation Status
The FTC investigation was launched under the Biden administration. New FTC leadership has signaled it's not a priority, leaving state regulators and private litigation as primary enforcement mechanisms.
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The RealPage Scandal: Algorithmic Rent Fixing
What Happened
In 2024, the Department of Justice sued RealPage, a property management software company, alleging its pricing algorithms enabled landlords to fix rents:
The allegations:- •RealPage software recommends rent prices
- •Multiple landlords in same market use recommendations
- •Algorithm uses competitors' non-public data
- •Results in coordinated price increases
- •Consumers pay inflated rents
- •RealPage software used for millions of apartments
- •Present in most major rental markets
- •Rent increases coordinated across competitors
Why It Matters
The RealPage case is the first major federal prosecution of algorithmic pricing as antitrust violation:
Legal theory:- •Even without explicit agreement, using shared algorithm to set prices is illegal coordination
- •"Machine learning" doesn't excuse antitrust violations
- •Companies can't hide behind "the algorithm made us do it"
- •DOJ case is ongoing
- •Multiple private class actions filed
- •Bipartisan concern about algorithmic pricing
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Your Rights and Protections
Federal Law
No comprehensive federal surveillance pricing law exists. However:
FTC Act Section 5: Prohibits unfair or deceptive practices. Surveillance pricing could be "unfair" if it causes substantial harm consumers can't avoid.
Civil Rights Laws: Price discrimination based on protected characteristics (race, religion, national origin) may violate civil rights laws.
Antitrust Laws: Algorithmic collusion—like RealPage—can violate Sherman Act.
State Privacy Laws
State privacy laws include non-discrimination provisions:
California (CCPA/CPRA):- •Businesses cannot discriminate against consumers who exercise privacy rights
- •Cannot charge different prices for exercising data rights
- •Right to know what data is collected
- •Similar non-discrimination provisions
- •Vary in strength and enforcement
Limitation: These laws generally prohibit discriminating against those who exercise privacy rights, not surveillance pricing generally.
Emerging Protections
Several states are considering surveillance pricing legislation:
- •Disclosure requirements for algorithmic pricing
- •Prohibitions on discriminatory pricing
- •Right to "fair" pricing
- •Transparency about data used
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Protecting Yourself
Detection Strategies
Check prices across devices:- •Compare on phone vs. computer
- •Try incognito/private browsing
- •Check from different locations (VPN)
- •Note differences for same product
- •Check logged-in vs. logged-out prices
- •Compare prices with friends/family
- •Note loyalty program "benefits" that may be higher base prices
- •Prices that change after browsing
- •Higher prices when you seem "ready to buy"
- •Different prices based on time of day
- •Prices that drop if you abandon cart
Defense Tactics
Limit data collection:- •Use privacy-focused browsers (Firefox, Brave)
- •Enable "Do Not Track" and cookie blocking
- •Use VPN to mask location
- •Clear cookies and browsing history regularly
- •Don't stay logged in when browsing
- •Check prices in incognito mode
- •Compare across multiple sites
- •Use price tracking tools (CamelCamelCamel, Honey)
- •Abandon carts to trigger discounts
- •Clear cookies before purchasing
- •Use email aliases for accounts
- •Don't link social media to shopping accounts
- •Opt out of data sharing when possible
- •Consider separate browsers for shopping
Price Comparison Tools
Browser Extensions:- •Honey (finds coupons, tracks prices)
- •Keepa (Amazon price history)
- •PriceBlink (cross-site comparison)
- •InvisibleHand (automatic price alerts)
- •CamelCamelCamel (Amazon)
- •Google Shopping
- •PriceGrabber
- •Slickdeals
- •Google Flights
- •Kayak
- •Hopper (price predictions)
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Fighting Back
Filing Complaints
Federal Trade Commission:- •Report deceptive pricing: reportfraud.ftc.gov
- •Document evidence of discriminatory pricing
- •Include screenshots showing price differences
- •Many states investigating algorithmic pricing
- •naag.org (directory)
- •Include specific examples and evidence
- •For financial products (insurance, loans)
- •consumerfinance.gov/complaint
Class Actions
Multiple lawsuits are challenging algorithmic pricing:
RealPage rent fixing:- •Class actions on behalf of tenants
- •Alleging inflated rents from algorithm
- •Check topclassactions.com for updates
- •Challenges to discriminatory surge pricing
- •Claims of algorithmic price manipulation
- •Cases alleging deceptive price practices
- •Discrimination in personalized pricing
Advocating for Change
Support legislation:- •Contact state legislators about pricing transparency laws
- •Support federal surveillance pricing regulations
- •Join consumer advocacy organizations
- •Share price discrepancies on social media
- •Report to consumer protection journalists
- •Demand transparency from companies
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Industry-Specific Concerns
Ride-Sharing
The problem:- •Surge pricing can be extreme (4x+ normal rates)
- •Algorithm knows when you "need" a ride
- •Prices may vary based on individual factors
- •Limited competition in many markets
- •Compare Uber vs. Lyft prices
- •Check at different times
- •Consider scheduled rides to avoid surge
- •Use transit or alternatives when surge is high
Insurance
The problem:- •Insurers use massive data sets for pricing
- •"Usage-based" insurance tracks behavior constantly
- •Data beyond traditional factors affects rates
- •Discrimination concerns with health/life insurance
- •Shop multiple insurers
- •Ask what data affects your rate
- •Opt out of tracking programs if beneficial
- •Check for state insurance regulations
Housing
The problem:- •RealPage-style algorithms coordinate rents
- •Landlords use "market rate" software
- •Tenants face coordinated increases
- •Algorithm recommends maximum extractable rent
- •Research if your landlord uses RealPage
- •Document rent increases
- •Join tenant organizations
- •Report suspected collusion to DOJ
E-Commerce
The problem:- •Prices change constantly based on demand and user
- •Personalized prices hard to detect
- •"Deals" may be inflated from personalized base
- •Limited transparency
- •Use price tracking extensions
- •Shop in incognito mode
- •Compare across retailers
- •Check reviews for price manipulation complaints
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The Ethics of Algorithmic Pricing
The Business Defense
Companies argue surveillance pricing:- •Allows discounts for price-sensitive customers
- •Enables efficient market pricing
- •Reduces waste through demand management
- •Provides "personalized" value
The Consumer Perspective
Critics argue:- •Exploits information asymmetry
- •Discriminates against vulnerable populations
- •Erodes trust in markets
- •Extracts maximum payment from each consumer
What Fair Pricing Would Look Like
Transparency:- •Disclose when prices are personalized
- •Explain what data affects price
- •Allow consumers to see "standard" price
- •Prices cannot vary by protected characteristics
- •No algorithmic redlining
- •Equal access regardless of data profile
- •Opt out of personalized pricing
- •Access to non-personalized alternatives
- •Control over data used in pricing
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Resources
Consumer Advocacy
- •Consumer Reports: consumerreports.org
- •Public Knowledge: publicknowledge.org
- •Electronic Frontier Foundation: eff.org
- •U.S. PIRG: pirg.org
Price Comparison
- •CamelCamelCamel: camelcamelcamel.com
- •Keepa: keepa.com
- •Honey: joinhoney.com
- •Google Shopping: shopping.google.com
Privacy Protection
- •Privacy Badger: privacybadger.org
- •uBlock Origin: ublockorigin.com
- •Firefox: mozilla.org/firefox
- •DuckDuckGo: duckduckgo.com
Government Resources
- •FTC: ftc.gov
- •CFPB: consumerfinance.gov
- •State AGs: naag.org
- •DOJ Antitrust: justice.gov/atr
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Conclusion: The Price of Surveillance
Surveillance pricing represents a fundamental shift in the relationship between buyers and sellers. Instead of posted prices that apply to everyone, algorithms now calculate the maximum each individual will pay—and charge accordingly.
The Wendy's backlash showed that consumers reject this practice when they see it clearly. But most surveillance pricing happens invisibly, embedded in the algorithms that set prices before you even see them.
Key takeaways:
- •Your data sets your price: Browser history, location, device, and more affect what you pay
- •Discrimination is real: Algorithms can—and do—charge more to vulnerable populations
- •You can fight back: Privacy tools, price comparison, and strategic shopping help
- •The law is catching up: RealPage lawsuit and state legislation signal change
- •Transparency is key: Demand to know when prices are personalized
The algorithm knows what you're willing to pay. The question is whether you'll let it charge you that much.
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This guide provides general information about pricing practices and consumer rights. It does not constitute legal advice. Consult with an attorney for specific situations.
Sources: FTC, Consumer Reports, Future of Privacy Forum
Last Updated: December 2025