Have consumers ever noticed or realized that on Zepto, products are at a higher price for an iPhone user than an Android user or that the cabs on Uber and Ola are more expensive when booked by an iPhone user? In this digitalized era, all of this is done by coding. This coding method, algorithmic pricing, has become a common practice across various industries.
What Is Algorithmic Pricing?
Algorithmic pricing is a dynamic, automated method for determining product prices with the help of data analysis and machine learning algorithms. The prices of goods and services are often changed using vast amounts of data relative to market demand, competitor pricing, and consumer behaviours so that companies can optimize their revenue strategies with prices that align with the current market condition, hence sustaining competition.
Businesses can use algorithmic pricing to forecast a price that maximizes profits. This price can be updated (typically in real-time) in response to modifications in the input data that the tool is based on. Whereas “personalized pricing” bases suggestions on specific customers or groups of customers and “dynamic pricing” bases recommendations on the entire market.
For instance, the airline business has long used this technique to adjust ticket prices according to demand, the day and time of booking, or competition from other airlines. Similarly, e-commerce platforms use real-time price adjustments to be competitive and gain maximum product profits.
Algorithmic Pricing & Device Usage
One of the most important examples of algorithmic pricing would be device-based differential pricing- the price offered for a particular product or service that varies by the consumer’s device, which may be an Apple device or an Android device.
Recent reports have highlighted that ride-hailing platforms like Uber and Ola, hotel booking platforms, and e-commerce platforms like Zepto sometimes charge different prices based on the user’s phone. For example, users with high-end devices like the latest iPhone models might see higher prices than those using mid-range Android devices. The strategy used here assumes that users of high-end devices have a higher willingness or ability to pay.
Challenges That Result From Algorithmic Pricing
Even though this may be an economic development strategy, it may also be unfair to consumers, who must pay more depending on their device model. This includes:
1. Economic discrimination occurs when specific users routinely pay more for the same good or service because of price differences based on a user’s device or browsing history.
2. Consumers do not know that their devices are changing prices according to algorithms. Thus, they cannot be sure about the digital markets and lack transparency.
3. If competitors also use similar pricing algorithms, then inadvertently, customers will join themselves in paying more in the market, and there may be a case of competition among each other.
4. The data used for training the pricing algorithms also contains personal information, a significant privacy, and data protection issue.
Algorithmic Pricing Regulations
Regulatory agencies are looking at algorithmic pricing worldwide to ensure it does not hamper competition or hurt consumers. The CCI monitors all such actions so that competition remains in the market. The CCI is now examining situations wherein algorithmic pricing may lead to consumer abuse or anti-competitive behaviour so that no malpractice occurs.
Under section 3 of the Competition Act of 2002, price fixing is prohibited in India. To establish the market pricing, the clause forbids any agreement between or “a practice carried on, or decision taken by, any association of enterprises or association of persons, including cartels, engaged in identical or similar trade of goods or provision of services.”
Uber & Ola Case
Allegations have also been made against giants like Ola and Uber, which charge different fees according to the operating system of a user’s smartphone. According to reports, it was found that the fares of iPhone users were higher than those of Android users. Presently, ride-hailing services use dynamic pricing algorithms that alter prices based on various factors, such as location, demand, traffic, and others. However, according to the claims, another element affecting price computations is the kind of device used to book the rides. On receiving the notification from the CCI about this method, both Ola and Uber have denied using such pricing methods in reply to these accusations. Ola claims the pricing policy is consistent for all devices and is ready to work with the Central Consumer Protection Authority (CCPA) to resolve the matter.
Zepto Case
Quite remarkably, the quick-commerce website Zepto has also faced criticism as allegations claim that the site charges differently based on the gadget. For instance, capsicum selling at ₹107 for 500g on an iPhone when accessed through an Android device was priced at ₹21. This discovery caused debates and concerns about why there must be discriminatory pricing across different devices used to view the app.
Due to the results of the above incidents, the algorithmic pricing algorithms of the digital platforms have been scrutinized for the incidence of fairness and transparency. The Minister of Consumer Affairs, Pralhad Joshi, announced that the CCPA would investigate claims of unequal pricing. This reflects the government’s commitment to ensuring fair trade standards and guarding consumer rights.
The CCI and other regulatory agencies, including the CCPA, are expected to probe into device-based differential pricing after recent complaints to determine whether such practices amount to unfair trade practices or violate competition laws. They have taken the first step by sending notices to these companies. The results of such investigations would go a long way in establishing guidelines on algorithmic pricing strategies adopted by digital platforms without compromising user trust or engaging in discriminatory actions.
Conclusion
Therefore, the Competition Commission seems sufficient to deal with price fixing using algorithms. However, the development of pricing algorithms has posed a significant challenge to competition law regimes worldwide, and there is “the end of competition as we know it.” Indian laws must adopt the approach taken by the Court of Justice of the European Union in dealing with such matters and assume that firm executives were aware of the collusion being carried out by firms, given that the regulating body finds it nearly impossible to find evidence of an agreement between firms when self-learning is used to fix the market price.
Authors: Riya Gupta & Shatakshi Agarwal