I remember sitting in school, staring at two intersecting lines on a graph, the neat point where supply met demand, and price was marked. It felt logical, even predictable. That model, however, is evolving. An individual's price is no longer always anchored to market valuation, nor does it necessarily settle at the point where supply meets demand. Digital markets have quietly rewritten the rules.
In India, for decades, the standard Maximum Retail Price (MRP) system has been the bedrock of Indian retail and is mandated by the Legal Metrology Act, 2009. The MRP dictates the absolute highest price a packaged good can be sold for, preventing arbitrary price inflation and creating a baseline of transparency and trust. Consumers are conditioned to expect this fixed ceiling, which naturally pushes back against hidden threads of algorithmic pricing.
Now imagine an alternate scenario: you are buying the same product, in the same city as your neighbour, and when you compare notes, you find that you have paid more. This is not because of a sale or a coupon, but because an algorithm assessed your profile differently. Online shoppers have grown accustomed to prices that shift by the day, the hour, even the minute. But what if the price you see is shaped not by market conditions, but by data about who you are, your location, and your online behaviour?
A notable example emerged in India in 2025, when the Department of Consumer Affairs issued notices to cab aggregators Ola and Uber over allegations of differential pricing where customers were reportedly quoted different fares depending on whether they booked their ride via an iPhone or an Android device. The make of your phone had seemingly become a signal of purchasing power. It raised a question worth examining: as platforms grow more sophisticated in reading consumer data, who sets the boundaries of how that data is used in pricing? Understanding how these pricing systems work (called algorithmic pricing) and where they need guardrails, is becoming one of the more pressing questions in consumer protection today.
Algorithmic pricing
Algorithmic pricing is not a new phenomenon. Businesses such as airlines, hotels have long tracked customers' search behaviour and purchase history to offer targeted promotions and discounts. What has changed is the scale and sophistication. The explosion of internet commerce, the proliferation of connected devices, and the availability of vast amounts of consumer data have dramatically expanded what these algorithms can do and who they can reach. While algorithmic pricing can drive efficiency and competition, it can equally be turned into a tool that restricts it.
Within algorithmic pricing, two distinct approaches have emerged. Dynamic pricing where fares and rates shift for everyone based on broad market conditions - this is a pricing mechanism already well established across industries. Fare or rate shifts for everyone based on supply and demand. Personalised pricing though is still evolving, quietly becoming more refined with every digital interaction and it is this trajectory that is prompting researchers, regulators, and consumer advocates to ask what rules, if any, should govern it What is different now and concerning to researchers is the possibility that online retailers could use personal data to set a higher base price for individual consumers, without their knowledge, when algorithms detect things like urgent need or high disposable income.
Personalized Pricing and Consumer Confusion
Pricing customers differently based on context is not new. Long before algorithms, personalised pricing existed in unorganised markets at the farmer's stall, the local bazaar, the neighbourhood shop where a seller would size up a customer and negotiate accordingly. A well-dressed buyer, an unfamiliar face, or someone in an obvious hurry might quietly end up paying more than a regular. What has changed is not the principle, but the scale and precision. What once relied on a seller's instinct now runs on data.
Why does personalized pricing need more consumer attention than dynamic pricing and why should consumers care about the difference? Dynamic pricing operates on a kind of democratic logic: the surge fare on a rainy evening, the hotel room that costs more during a festival weekend, these prices shift in response to broad market forces like supply, demand, and time, and they shift the same way for everyone. You may pay more, but so does the person sitting next to you. This is not necessarily fair but at least it is more equitable.
Personalized pricing, also known as "surveillance pricing," breaks that shared reality entirely. What sets surveillance pricing apart is not just the data it harvests, but the question it is designed to answer. It is no longer about the "willingness to pay," but the "maximum ability to pay”. What sets personalised pricing apart is a subtle but significant shift in logic: rather than anchoring on what a consumer is willing to pay, it attempts to determine what a consumer is able to pay. To arrive at that number, AI systems draw on vast amounts of personal data: browsing history, location, device type, past purchases and read them collectively as indicators of economic profile. A premium device may signal higher income. A neighbourhood with above-average median earnings may influence the price shown.
Even the time of day a consumer shop can be read as a signal of urgency or financial comfort. The result is a price that is, in many ways, individually calibrated not by market conditions, but by what the data suggests a specific consumer can bear.
Can cost customisation actually mend the pockets of marginalised consumers?
Theoretically this might sound like achieving economic equity at digital markets. While some proponents argue that personalized pricing can make goods more affordable for consumers on tight budgets, studies point in a more complicated direction. The absence of regulation and transparency has, in practice, allowed firms to use these tools to maximise profit rather than extend access. There are few theories that convincingly demonstrate personalised pricing will improve equity or meaningfully benefit economically marginalised consumers.
The scale of the problem is already visible. A recent report found that 63 percent of online payment users in India encountered hidden charges or drip pricing during digital transactions up from 52 percent the previous year. Across e-commerce, banking, travel, ride-hailing, insurance, and digital lending, deceptive pricing practices continue to affect millions of consumers, even as regulatory frameworks have begun to take shape. According to a report by Datum Intelligence on dark patterns in India's online marketplaces, deceptive digital pricing practices are costing Indian consumers between ₹25,000 crore and ₹28,000 crore every year, with nearly nine in ten of India's 304 million online buyers losing somewhere between ₹78 and ₹87 every month often without realising it.
The data suggests that existing interventions have so far struggled to keep pace with the sophistication of the practices they are meant to govern.
Economists Jidong Zhou and Andrew Rhodes, from Yale built a model to not only understand how consumers respond to data sharing, but also how firms respond to it. This pricing technique is exacerbated in cases where there is an imbalance in data ownership. A company like Amazon knows far more about its consumers than the local hardware store, which makes it capable of targeting consumers with prices in a way the local hardware store cannot. In these cases of information asymmetry, Zhou says, we should be “more suspicious” about the effect of personalized pricing on consumer welfare. If coverage is high (that is, markets in which most people buy the product, especially those products that are relatively inexpensive to produce without disruption), personalized pricing will likely benefit the average consumer. But if coverage is low (e.g., because the cost of producing the goods is high or volatile), or consumer data is held unevenly, then personalized pricing can be bad for consumers. This uneven distribution of data, does not only affect consumers, but also local markets that do not have the purchasing power of acquiring high end algorithmic pricing softwares.
Personalised pricing rests on a foundational assumption: that the firm already knows you. For consumers in digitally marginalised areas, those with poor connectivity, limited digital literacy, or only beginning to engage with online commerce - that assumption fails. Their data is sparse or absent, and in that absence, algorithms do not default to fairness. The equity argument holds only when the system has enough data to make a considered determination; for a first-time digital user from a low-income household, that data does not yet exist, she does not benefit from the tailoring, she simply pays more, without knowing why. This creates an uncomfortable paradox: the consumers who stand to gain the most from price relief are precisely those whose data is least available to enable it, and the solution of sharing more data carries its own costs and risks. Those who share may face higher prices; those who do not, may face them too leaving everyone caught in an uncertain middle ground where the promise of personalised pricing as an equalising force remains largely theoretical, contingent on conditions of data availability and regulatory oversight that do not yet exist for the consumers who need it most.
Regulatory rails across the globe and where does India stand
As agentic AI systems grow more capable, the scale and autonomy with which surveillance pricing can be deployed is set to expand significantly. This leaves lawmakers with questions circling around ethical efficiency and consumer welfare. This ambiguity leaves the government with very little to formulate regulations around personalized pricing.
Across the globe, the US, EU and other countries' governments have started taking measures to regulate the spillover effects of personalized pricing, where a few States in the US have started banning such practices. There are also experts who explain why banning might not fully eliminate personalized pricing - firms will just find personalized discounts and use other hidden charges depending on the nature of purchase.
Early signs of surveillance pricing has entered India through quick commerce apps, like instamart zepto , where an iphone user pays more for fruits and vegetables compared to someone using android in the same location. We also see personalized discount notifications on swiggy and zomato, that might inflate the price for another consumer.
India has yet to enact a law similar to New York State's Algorithmic Pricing Disclosure Act. This Law effective from November 10, 2025, requires companies using surveillance price setting to declare that 'price was set by an algorithm using your personal data.' Under the Consumer Protection Act, 2019, it may amount to an unfair trade practice if the platform gives a different price to different users without clear disclosure, misrepresents that a discount is genuine when it is actually individualized, or uses hidden profiling to create a misleading impression of scarcity, urgency, or savings. It may also be challenged if the pricing method is unfair, deceptive, or likely to mislead an ordinary consumer. Under the Information Technology Act, 2000 and the Sensitive Personal Data/Information SPDI Rules, 2011, issues arise if personal data, especially sensitive personal data, is collected, stored, or shared without proper consent, without a privacy policy, or in a way that is not reasonably secure; however, personalized pricing alone is not automatically an Information Technology (IT) Act violation unless it involves unauthorized data handling or security failure. Under competition law and e-commerce fairness principles, the problem is not simply that prices differ, but that the platform may be using opaque algorithmic discrimination, exploiting user vulnerability, or creating unfair market distortion without transparency, which can raise concerns about unfair business practices and consumer harm. So, the legal breach is usually not “personalized pricing” in itself, but the lack of transparency, misleading representation, unauthorized data use, or exploitative discrimination behind it.
In this context, Indian companies also need to consider the Digital Personal Data Protection DPDP Act (2023), which provides guidelines for the use of digital data and prohibits its misuse. The rules of the DPDP Act, which were published on November 13, 2025, also provide guidelines to e-commerce firms regarding the time period for retaining data under the Third Schedule of the DPDP Act, after which the data must be erased. Given these guidelines, businesses must exercise caution when using personal data for pricing purposes.
While having a linear high coverage of economic distribution at all times is very unlikely, given market volatility and the nature of economic unpredictability, It is important for governments to draw solid policies and amendments, to regulate healthy competition between firms and protect consumers from uneven prices.
References:
https://insights.som.yale.edu/insights/will-banning-personalized-pricing-work
https://nrf.com/blog/algorithmic-pricing-innovation-misunderstood
https://www.wsj.com/finance/investing/personalized-pricing-ban-9c0d92a9
https://insights.som.yale.edu/insights/sharing-your-data-comes-at-cost-and-not-just-to-you
https://www.gov.uk/government/publications/personalised-pricing-and-disclosure
https://one.oecd.org/document/DAF/COMP(2018)13/en/pdf
https://www.europarl.europa.eu/RegData/etudes/STUD/2022/734008/IPOL_STU(2022)734008_EN.pdf
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