Fixing Location-Based Pricing: How Proxies and Web Scraping Can Help

Is location-based pricing ethical and legal?

Adjusting prices based on demand, inventory, or seasonal trends is standard practice, and most consumers accept the logic behind it. But when algorithms use a shopper's personal data to estimate the maximum they're willing to pay, the picture gets more complicated. The fundamental question isn't just whether it's legal, but also whether it's fair. Regulators clearly have doubts. What started with an FTC investigation has already produced the country's first algorithmic pricing disclosure law, and dozens of similar bills are moving through state legislatures nationwide.

What makes this conversation difficult is that personalized pricing isn't automatically a bad thing. It can lower costs for price-sensitive shoppers and open up underserved markets. But the same systems can just as easily charge more in ZIP codes with fewer retail alternatives, quietly deepening inequities that consumers have no way to detect. The technology itself is neutral. How businesses choose to deploy it isn’t.

Companies adopting algorithmic pricing have a responsibility to ensure their models operate transparently and don't exploit consumers who lack the tools or awareness to push back. The sections below unpack how dynamic pricing works in practice, where the ethical and regulatory lines are being drawn, and how ethical data collection is becoming essential for both competitive strategy and accountability.

The price tag that follows you home

Algorithmic pricing is no longer a niche tactic reserved for airlines and ride-hailing apps. Major retailers across grocery, fashion, and electronics are now adjusting prices millions of times a year based on consumer data, and regulators are starting to push back.

The scale of algorithmic pricing is staggering. According to the Dynamic Pricing Index ‘25, which analyzed over 1.5M data points across 120+ global eCommerce platforms, the United States alone recorded 542,946 price changes in 2025, more than the next 4 countries combined.

In January 2025, the FTC released the preliminary findings from its surveillance pricing study, revealing that intermediary firms use a wide range of personal data, from precise geolocation to mouse movements on a webpage, to help retailers set individualized prices. The study identified at least 250 businesses across sectors like grocery, apparel, and home goods that had adopted these pricing strategies.

New York responded with the first law of its kind. The Algorithmic Pricing Disclosure Act took effect on November 10, 2025, requiring businesses to display a clear notice whenever a price is set by an algorithm using a consumer's personal data. The mandated label reads: "THIS PRICE WAS SET BY AN ALGORITHM USING YOUR PERSONAL DATA." Violations carry penalties of up to $1,000 per instance.

The regulatory momentum does not stop in New York. In 2025 alone, 24 state legislatures introduced over 50 bills aimed at regulating algorithmic pricing. This is quickly becoming a national conversation, and it raises a fundamental question: how do businesses compete fairly on price, and how does anyone verify that the rules are being followed? The answer to both involves the same underlying technology: proxy-powered web scraping.

How dynamic pricing actually works (and why it matters)

Before jumping to conclusions about fairness, it helps to understand what dynamic pricing is and what it is not. The term covers a broad spectrum of practices, and not all of them are problematic.

At its simplest, dynamic pricing means adjusting the cost of a product or service based on changing conditions. Airlines have done this for decades, varying ticket prices based on demand, seat availability, and time until departure. Ride-hailing platforms raise fares during peak hours. Hotel rates climb during holiday weekends. Most consumers accept these fluctuations because the logic is transparent: high demand means higher prices.

Our research data reveals just how pervasive this practice has become across categories. Fashion led all sectors with 427,340 pricing changes over the past year, followed by electronics with more than 351,000 adjustments and groceries with nearly 319,000 changes. Critically, the U.S. market showed a near-perfect 50/50 split between price increases and decreases, suggesting that these algorithms are constantly fine-tuning rather than simply pushing costs upward.

The more controversial variant is personalized algorithmic pricing, where the price a specific consumer sees is tailored to them individually. Algorithms can factor in a shopper's geographic location, the device they’re using, their purchase history, whether they’re a first-time or returning visitor, and even the time they spend hovering over a product page. The goal is to estimate each consumer's maximum willingness to pay and price accordingly.

This practice can work in the consumer's favor. A retailer might offer a steeper discount to a price-sensitive shopper in a competitive market, or reduce delivery fees in underserved areas to attract new customers. The FTC's own research acknowledges that personalized pricing can sometimes lead to lower costs for certain consumer segments. Amazon's average discount depth of 35.3% on price drops, tracked by our in-house experts, shows that algorithmic adjustments can deliver meaningful savings.

The problem emerges when these systems operate without oversight. When algorithms charge systematically higher prices in ZIP codes with fewer retail alternatives, or when consumers have no way to know whether they are seeing a fair price, the practice shifts from competitive strategy to potential exploitation. As the FTC put it, the harm from surveillance pricing is at its peak when pricing is opaque and when consumers face barriers to discovering competitor alternatives.

The core issue is transparency. And transparency, at scale, requires data.

Competitor intelligence in different regions

For eCommerce teams and pricing analysts, the rise of location-based pricing creates both a challenge and an opportunity. If your competitors adjust prices by geography, you need to see what they’re showing to customers in every market you serve. Walking into a competitor's store in each city is not scalable. Fortunately, tools like proxies and automated web scraping make large-scale competitor monitoring possible.

How geo-targeted price monitoring works

The process is straightforward in concept. Residential proxies route web requests through ethically-sourced, real IP addresses in specific locations. When a data collection tool connects through a residential IP in Brooklyn, the target website treats the request as though it came from a regular consumer browsing from that neighborhood. The same tool can then route a request through an IP in Dallas, then one in rural Ohio, collecting the price displayed for the same product in each location.

Because residential proxies use IPs assigned to actual households by internet service providers, they’re very useful in pricing research, where the entire point is to see the same experience a real consumer would see.

What these insights reveal

Consider a practical scenario. A mid-size electronics retailer wants to understand why a national competitor is gaining market share in the Southeast. The pricing team deploys a scraping operation through residential proxies in 60 ZIP codes across the region, collecting daily snapshots for four weeks. Our research shows that this kind of granular analysis is essential – even within a single country, pricing strategies vary dramatically by region and retailer.

The data might reveal that the competitor offers 12 to 15% lower prices on key product categories in that region, while maintaining standard pricing elsewhere. This pattern suggests several possibilities – the competitor may be running a regional acquisition campaign, may have opened a distribution center that reduced logistics costs, or may be deploying a loss-leader strategy to establish market dominance before raising prices.

None of these insights would be visible without geo-distributed data collection. Each one informs a different strategic response, from adjusting regional promotions to rethinking supply chain investments.

This type of competitive intelligence is a well-established business practice. It mirrors the kind of in-store price checking that retail teams have done for generations, simply executed at a scale that matches how modern eCommerce actually works.

The compliance side: monitoring pricing practices at scale

Competitive intelligence is one half of the equation. The other half is oversight. Now that New York requires disclosure of algorithmic pricing, and other states are likely to follow, someone needs to verify that retailers are actually complying.

Internal compliance audits

Large retailers that use dynamic pricing need to audit their own systems. Algorithms can produce unintended outcomes, especially when trained on historical data that reflects existing inequities. A pricing model that factors in neighborhood purchasing power could, without deliberate design, end up charging more in lower-income areas with fewer grocery stores. Internal compliance teams can use proxies and automated data collection solutions to test their own storefronts from different locations and devices, checking that pricing rules work as intended and that the required disclosures appear correctly.

External monitoring and enforcement

The need for independent verification is even more pressing. Regulators, consumer advocacy organizations, and investigative journalists all require tools to detect pricing patterns across thousands of products and hundreds of locations. Attorney General Letitia James has already encouraged New York consumers to compare prices they see online with those shown to others and to report suspected violations.

Imagine a state attorney general's office receiving a wave of complaints about a grocery delivery platform that appears to charge higher fees in certain neighborhoods. To investigate meaningfully, the office needs to collect price data from hundreds of ZIP codes at regular intervals, then run statistical analyses to identify whether the pattern is systematic. Manual spot-checks are insufficient. Data collection infrastructure provides exactly this capability, allowing investigators to see the same prices that real consumers in those areas would see.

The data collected could reveal, for example, that delivery surcharges and product markups are consistently elevated in food deserts, areas with limited physical grocery access where residents are already paying a premium for basic necessities. That kind of pattern analysis is what transforms anecdotal complaints into actionable enforcement.

Transparency laws are only meaningful when paired with monitoring tools that can operate at scale. Without proxy-based data collection, enforcement depends on individual consumer reports, an approach that favors the most digitally literate consumers and leaves systemic issues undetected.

Why this matters now

The regulatory environment is evolving rapidly. New York's Algorithmic Pricing Disclosure Act is the first statute of its kind, but it will not be the last. In 2025, both California and New York adopted antitrust laws targeting the use of competitor data in algorithmic pricing. More than 50 bills were introduced across 24 states. At the federal level, bipartisan senators have urged the FTC to resume its surveillance pricing investigation and take enforcement action.

The global dimension adds urgency. The Dynamic Pricing Index shows that automated repricing is a worldwide phenomenon, with Germany, India, the United Kingdom, and South Korea all recording tens of thousands of annual price changes across major platforms. European markets showed the highest stability share at 47.2%, meaning nearly half of all pricing patterns in the region were consistent. Yet within that stable baseline, retailers still made thousands of aggressive individual adjustments. For any business operating across borders, understanding these regional patterns is a competitive necessity.

Businesses that invest in automated monitoring now will have a competitive advantage as these rules are applied in most markets. They’ll understand how their own algorithms behave in the real world, how competitors are adapting to new requirements, and where pricing opportunities exist in emerging regulatory environments.

Regulators who build scraping capacity will be able to enforce transparency laws with real data rather than relying on sporadic consumer complaints. The organizations on both sides that embrace this infrastructure early will be the ones best equipped for what comes next.

Bottom line

Dynamic pricing is not going away. The algorithms will only get more sophisticated, the data inputs richer, and the pricing strategies more granular. The question for businesses, regulators, and consumers is whether we will have the tools to ensure these systems remain fair and transparent.

There’s already a clear direction – businesses that proactively monitor the competitive landscape make better pricing decisions and deliver more compelling offers to their customers. Regulators and watchdog organizations that invest in automated monitoring will be able to enforce transparency laws at the speed and scale that modern eCommerce demands. Both outcomes depend on the same core technology – residential proxies and scalable, ethical web scraping.