Companies Use Surveillance Pricing and AI to Charge You More Based on Your Personal Data

Major Retailers Deploy Surveillance Pricing to Extract Maximum Revenue

Surveillance pricing has emerged as a dominant strategy among major corporations, fundamentally altering how Americans pay for goods and services. Lindsay Owens, CEO of Groundwork Collaborative and author of Gouged: The End of a Fair Price and What That Means for Your Wallet, reveals how companies like Walmart, Kroger, and Instacart systematically harvest consumer data to implement personalized pricing strategies1

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. The practice represents a sharp departure from the fixed-price model that dominated retail for 150 years since John Wanamaker introduced price tags in his Philadelphia department store in the late 1800s1

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Owens, who previously served as an economic policy adviser in Senator Elizabeth Warren's office, testified before the Senate Judiciary Subcommittee on Crime and Counterterrorism earlier this year2

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. She explained how corporations track purchases, location data, and loyalty programs to predict exactly how much individual shoppers will pay. This form of price discrimination operates at the intersection of data surveillance and algorithmic manipulation, creating what economists call first-degree price discrimination where companies estimate each customer's willingness to pay1

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Loyalty Programs Function as Sophisticated Data Harvesting Operations

Consumer data collection through loyalty programs has transformed from a simple rewards system into comprehensive surveillance infrastructure. Owens describes these programs as "sophisticated data harvesting" operations where consumers enter a devil's bargain, trading personal information for promised discounts that companies increasingly fail to deliver1

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. A Duke economist named Curtis Taylor predicted this outcome in 2004, warning that once loyalty programs went high-tech, firms would identify their most eager customers and charge them more rather than less1

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The scope of this data collection became starkly visible when one customer requested their McDonald's app data and received a 515-page dossier that estimated a zero percent chance they would ever stop being a customer1

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. This level of profiling enables companies to exploit customer loyalty rather than reward it, fundamentally inverting the stated purpose of these programs.

Generative AI and Agentic Commerce Push Cart Totals Higher

AI-driven pricing represents the next frontier in extracting revenue from consumers. Walmart reports that its AI shopping assistant, Sparky, generates cart totals 35 percent higher than non-AI-assisted purchases1

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. This dramatic increase demonstrates how generative AI supercharges existing impulses to upsell customers and prevent comparison shopping. Agentic commerce, while still in early stages, shows clear warning signs of exacerbating consumer protection challenges1

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AI agents marketed as tools to automate shopping decisions may actually work against consumer interests unless regulated. Owens advocates for requiring AI agents to operate under fiduciary standards similar to realtors and lawyers who must work on behalf of their clients1

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. Without such protections, these systems function as automated snake oil salesmen rather than helpful assistants.

Senate Hearing Reveals Split on Personalized Pricing Impact

The Senate Judiciary Subcommittee hearing featured contrasting perspectives on individualized prices for shoppers. While Owens and three other witnesses criticized surveillance pricing practices, Z. John Zhang, a Professor of Marketing at the University of Pennsylvania's Wharton School, defended the practice2

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. Zhang argued that firms don't always benefit from personalized pricing and that it can intensify competition, with companies offering higher quality products and stronger brands gaining the most advantage2

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Owens countered that surveillance pricing erodes transparency and predictability, making it harder for families to budget or comparison shop effectively2

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. Companies can track whether shoppers are likely to compare prices across retailers, adjusting their offers accordingly to maximize extraction while maintaining the appearance of competitive pricing.

Technology Enables Exploitation of Consumer Vulnerability

The distinction between willingness to pay and ability to pay reveals how surveillance pricing exploits desperate situations. A parent needing Tylenol delivered overnight for a sick child faces inflated prices based on urgency rather than product value1

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. This dynamic transforms every transaction into an opportunity for companies to identify and exploit vulnerability, whether driven by time constraints, lack of alternatives, or demonstrated brand loyalty.

Owens emphasizes that big tech reinvented the rip-off by supercharging age-old impulses to overcharge customers1

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. New technologies make it possible to squeeze more profit from Americans in almost every transaction, with algorithms determining prices based not on market conditions or demographic groups but on individual behavioral profiles compiled through extensive surveillance.