The hidden danger behind dynamic pricing.

Dynamic pricing: How a silent shift in technology turned everyday shopping into a high-stakes game

The price tag that once protected shoppers

For more than a century, the price tag stood as a symbol of fairness. A sticker of glue and paper represented a promise that everyone paid the same amount for the same product. This principle emerged in the nineteenth century when shopping resembled a contest of negotiation rather than a transaction.

Clerks sized up customers based on clothing, accent, body language or even how long the queue was. If a buyer disagreed with the clerk’s price, the only defence was negotiation. It was exhausting and discriminatory and benefited those with confidence, time or wealth.

Quakers were among the first to challenge this system. Their belief in fairness pushed them to display fixed prices in their shops. This simple act changed everything. Customers could compare prices between stores.

Businesses were forced to compete openly. The price tag became an equaliser, a quiet defender of the average shopper. When Stan Avery introduced adhesive labels in the 1930s, price tags became universal. That little sticker represented the end of arbitrary pricing and the beginning of transparent commerce.

Over the last decade, however, this foundation has begun to crumble. The traditional price tag is being replaced by dynamic systems powered by algorithms, data-harvesting platforms and automated surveillance. The shift has been so subtle that many shoppers have barely noticed. Yet the impact is profound and global.

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How dynamic pricing returned shopping to the past

Dynamic pricing means prices change constantly in response to conditions such as demand, inventory levels, time of day or local competition. Anyone who has booked a flight or taken a ride-hail service has seen its effects.

Prices go up or down with no explanation. Companies claim this is efficient, modern and responsive. In reality, dynamic pricing has more in common with nineteenth-century haggling than the transparent markets created in the twentieth century.

This shift has been fuelled by artificial intelligence. Algorithms now analyse millions of data points in real time. They predict how much a specific consumer will tolerate before abandoning a purchase. The goal is not to provide value. The goal is to extract the highest possible price from each individual customer. While companies present dynamic pricing as a natural part of market behaviour, it often benefits businesses far more than buyers.

There are three ways this technology is exploited. Some practices are illegal. Others are legal but damaging. A final category falls into a grey area that many experts argue should not be permitted at all.

When algorithms enable illegal collusion

For most of the twentieth century, price fixing required deliberate coordination. Executives had to meet behind closed doors, compare notes and agree to raise prices together. Modern dynamic pricing has made that kind of conspiracy unnecessary.

When several companies sign up for the same pricing system, the algorithm processes similar data sets, interprets market signals in a uniform way and recommends near-identical pricing structures. The outcome mirrors collusion even though no human conversation has taken place.

A clear example surfaced in the hotel industry. Several major chains adopted third-party revenue management software that analysed occupancy forecasts, local events and competitor rates. As the system became widely used, hotels relying on it began setting room prices that moved in near-perfect unison.

The software’s design encouraged operators to maintain rates above what the market would normally bear and discouraged discounting during slow periods. This created an environment in which travellers paid more, not because of natural demand, but because the technology shaping decisions pushed multiple businesses toward the same inflated strategy. Regulators raised concerns that such systems could distort competition at scale, even when each company believed it was acting independently.

There is an additional layer of risk in markets where firms use different algorithms. Although these systems are developed separately, their models often monitor competitors so closely that they trigger the same response patterns. Supermarkets experimenting with dynamic shelf pricing provide a clear illustration.

When two rival chains introduced automated price-changing tools that tracked each other’s promotions in real time, both systems immediately cancelled out every discount. As soon as one store dropped the price of a product, the competitor matched it within seconds. Managers realised that reducing prices no longer produced an advantage, so the algorithms stopped recommending cuts altogether. Shoppers ended up paying more because neither retailer was willing to initiate competition.

A study of these practices found that supermarkets using dynamic pricing tools tended to charge significantly more than before the software rollout, with increases reaching double-digit percentages in some regions. The picture was unmistakable. The more pricing decisions shifted from human judgement to competing automated systems, the less consumers benefited from the competitive tension that price tags were meant to protect.

How legal dynamic pricing pushes prices higher

Even when companies avoid collusion, algorithmic pricing can create unintended but harmful effects. When multiple competitors use dynamic tools, they often enter a stalemate where none is willing to lower prices. Price cuts lose their power and the market becomes sluggish. Dynamic systems treat competition as a threat rather than a challenge.

This is one of the reasons supermarkets, airlines, hotels and digital retailers push for digital price tags. These tags allow instant changes across thousands of items without employees touching a single label. Even small shops now rent algorithmic pricing services for low monthly fees. This expands the reach of dynamic pricing far beyond large corporations.

Such systems may respond quickly to market conditions but tend to favour higher prices. When demand rises or data suggests customers are less price-sensitive, algorithms automatically push prices upward. Although companies frame this as efficient pricing, the lack of transparency leaves consumers uncertain about why they are paying more at one moment than another.

Personalised pricing and the rise of surveillance shopping

The most concerning development is personalised pricing. This is where dynamic pricing meets mass data collection. Algorithms study individual consumers rather than general trends. They examine income, location, browser history, spending habits and even emotional cues. The more a company knows about a person, the more precisely it can tailor prices.

Loyalty programmes are central to this shift. Companies present these programmes as helpful systems offering free rewards, discounts or points. In truth, they are powerful data-harvesting tools. A simple tick box when signing up can give a company permission to track a customer’s movements, online behaviour and device details. Retailers use this information to create detailed customer profiles. Some even sell these profiles to third parties.

Once companies know what someone is likely to pay, they can manipulate prices. Studies show that online stores have quoted higher prices to users on high-end devices. Other sites push more expensive products to customers with specific browsing patterns. The potential for abuse is immense.

Imagine a pharmacy raising the cost of medication because its system knows a customer has no alternatives. A rental site detecting that someone needs immediate housing could raise prices for that user alone. A smart speaker overhearing gaps in household supplies could trigger higher prices for those items. This is not speculation. These are realistic extensions of systems that already exist.

This future turns shopping into a contest between individuals and machines. The once-neutral price tag becomes a mirror reflecting how much corporations think a person can afford.

Why governments must catch up with technology

Regulators have begun addressing algorithmic collusion, but the pace of regulation lags far behind the speed of technological change. Most countries lack clear rules limiting how companies gather and use consumer data for pricing. In many regions, personalised pricing is entirely legal.

There are several measures governments could introduce. One option is restricting how often companies can change prices. If all retailers could only update daily at a fixed time, competition would return naturally. Legislators could also strengthen data protection laws to limit the personal information companies collect through loyalty schemes and apps. Transparency requirements would also help consumers understand when and why prices change.

Without stronger laws, consumers face an invisible opponent. Algorithms operate around the clock, equipped with far more information than any buyer could hope to understand. The balance of power shifts completely towards corporations.

Protecting yourself from data-driven pricing

While large-scale reform requires legislation, individuals can take steps to protect themselves. One of the most effective methods is limiting the personal data available to companies and brokers. Many firms purchase information from data brokers who collect details from online activity, loyalty programmes, public records and even location data.

Removing this information manually is difficult. Each broker has its own process, and many make withdrawal intentionally complicated. This is why services such as Incogni have grown in popularity. Incogni contacts data brokers on behalf of users and requests removal from their databases. Clearing these records reduces the amount of personal information available to pricing algorithms. This weakens a company’s ability to personalise prices and can help restore a measure of fairness.

For consumers frustrated by rising costs, unpredictable prices and constant surveillance, removing personal data is a practical step to regain control. It will not eliminate dynamic pricing systems, but it limits how effectively they can target individuals.

A future shaped by algorithms

Shopping has always involved strategy, but algorithms have changed the nature of the game. The fairness introduced by the price tag in the twentieth century is disappearing. In its place is a system in which prices are shaped by invisible calculations and data gathered from nearly every aspect of daily life.

Dynamic pricing promises efficiency and flexibility, yet it threatens the trust that shoppers once took for granted. It risks creating a world in which each person sees a different price for the same product, based not on competition or supply but on their personal data profile.

Until governments introduce strong protections, consumers must take responsibility for safeguarding their own information. Removing data from brokers through services like Incogni offers a clear way to push back against a system that relies heavily on personal details to maximise profits.

Dynamic pricing may be the future, but transparency and fairness must remain part of it. Without action, shoppers will continue facing an opponent unlike anything in history: an algorithm that watches, learns and adjusts prices with one relentless goal, to take as much as possible from every consumer it can see.

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