Technology content creator Salem Al-Dalbehi raised, in a video, a practical example of price differences between users when purchasing products or ordering transportation services, linking it to the concept of "surveillance pricing."
Although experiments of this kind are not sufficient to conclusively prove that companies use this practice, they open the door to an important examination of one of the most compelling issues in the economics of digital platforms: How does big data affect pricing strategies?

This article takes that idea as a starting point for discussing the concept from an economic perspective, rather than verifying the accuracy of any particular case.

From Uniform Pricing to Personalized Pricing

In traditional economics, a product was generally sold at the same price to all buyers.

In the digital economy, however, companies have come to possess vast amounts of data that allow them to change prices instantly or even tailor them to different groups of customers.

This represents an evolution of the concept of price discrimination, studied by economist Arthur Pigou more than a century ago. Today, however, it relies on artificial intelligence and big data instead of traditional market segmentation.

The Difference Between Three Concepts That Are Often Confused

1- Dynamic Pricing

The price changes because of:

  • Supply and demand.
  • Congestion.
  • Inventory.
  • Time.
  • Seasonality.

Examples include Uber, airline, and hotel prices.

2- Price Discrimination

A company sells the same product to different customers at different prices in order to maximize profit.

Examples include:

  • Student discounts.
  • Corporate rates.
  • Senior-citizen cards.

3- Surveillance Pricing

This is the more recent concept.

It relies on personal data to estimate the price that each user is likely to accept.

Algorithms may draw on data such as:

  • Search history.
  • Purchase history.
  • Device type.
  • Geographic location.
  • Spending patterns.
  • Level of loyalty.

This concept remains the subject of regulatory and academic debate, and there is insufficient transparency regarding the extent to which it is used commercially.

Why Are Companies Interested in This Type of Pricing?

Economically, the goal is very simple.

The company tries to approach:

The maximum amount each consumer can afford to pay.

The more accurately it can estimate this figure, the greater the producer surplus and the higher the revenue, without needing to increase the number of customers.

Are Individual Experiments Enough to Prove Surveillance Pricing?

The answer: No.

Price differences between users may result from:

  • Differences in the timing of the request.
  • Changes in supply and demand within seconds.
  • Differences in location.
  • Differences in inventory.
  • Marketing tests (A/B testing).
  • Differences in currency or taxes.

Therefore, a single experiment cannot be considered scientific evidence of personalized pricing.

Where Does the Economic Problem Lie?

If prices come to depend on personal data rather than production costs or market conditions, challenges may arise, including:

  • Reduced market transparency.
  • Difficulty comparing prices.
  • A widening information gap between companies and consumers.
  • The potential harm to competition if data are used in a monopolistic manner.

How Can Consumers Reduce the Impact of Data Collection?

The amount of data platforms use can be reduced through practices such as:

  • Using private browsing.
  • Deleting cookies.
  • Comparing prices across more than one device.
  • Reviewing app permissions.
  • Limiting the sharing of personal data.
  • Comparing the price before and after logging in.
  • Using tracking-blocking tools when necessary.

However, there is no method that guarantees preventing price differences entirely.

Data have become one of the most important economic assets in the digital age. They are no longer used solely to improve services or target advertising; they may also affect how prices are determined. As artificial intelligence advances, debate will intensify over the acceptable limits of using personal data in pricing, making this issue one of the most important topics in platform economics and regulatory policy in the years ahead.