CMA: collusion could be addressed with personalised pricing

Out-Law News | 10 Oct 2018 | 10:51 am | 3 min. read

The risk of businesses colluding with one another over the price of goods and services would diminish if there was extensive use of personalised pricing algorithms in digital markets, the UK's Competition and Markets Authority (CMA) has said.

In a new economic research paper (63-page / 680KB PDF) the CMA said that the "increasing availability of data, and the sophisticated use of pricing algorithms, increases the scope for tacit coordination or personalised pricing", but said both are "unlikely" to coexist.

"In our view, it is unlikely for both tacit coordination and personalised pricing to occur within the same market," the CMA said.

"The traditional conditions that facilitate tacit coordination (such as transparency) make it harder to engage in highly personalised pricing because they mean price comparisons are easier for customers. The increasing use of data and algorithms does not change this. Conversely if pricing is truly personalised then it is difficult for competitors to observe and detect any deviation, making collusion less stable," it said.

The CMA's paper on pricing algorithms identified "evidence of widespread use of algorithms to set prices particularly on online platforms". However, it contained no firm conclusions about the effects of their use on market competition.

The CMA said that the use of pricing algorithms could benefit consumers, including by reducing transaction costs for businesses, reducing market frictions and in giving consumers "greater information on which to base their decisions". However, it acknowledged that there are concerns that algorithms could be "facilitate collusive outcomes" and increase the price of goods and services for consumers.

The regulator's research highlighted "characteristics of markets or pricing algorithms" that "might make tacit coordination more likely".

"The main impact of increasing use of data and algorithms appears to be that it can exacerbate traditional risk factors, such as transparency and the speed of price setting," the CMA said. "Algorithms can almost instantly observe all competitors’ prices, detect any deviation and implement a price response that is objective and easily understandable by competitors. As such, algorithmic pricing may be more likely to facilitate collusion in markets which are already susceptible to coordination, such as where firms’ offerings are homogenous."

"For these ‘marginal’ markets, the increasing use of data and algorithmic pricing may be the ‘last piece of the puzzle’ that could allow suppliers to move to a coordinated equilibrium. There could also be greater scope for coordination where algorithmic pricing takes place in an online context where price monitoring and response can happen particularly quickly," it said.

The CMA examined a number of "theories of harm" and said the "most immediate risk" is likely to stem from cases where "competing sellers use the same algorithm or data pool to determine prices".

"This may occur when, instead of using their own data and algorithms, rivals find it more effective to use a third-party algorithm supplier who may gain access to data, or an understanding of their pricing policy from several suppliers," the CMA said. "A concern could arise if this gives the platform or ‘hub’ the ability and incentive to increase prices above the competitive level, maximising collective profits."

The CMA said there could be features of the way pricing algorithms work which could indicate to competition authorities the potential for "tacit coordination". One factor could be where their use "leads firms to adopt very simple, transparent, and predictable pricing behaviour (like price matching, or price cycles)", it said.

The regulator said that competition authorities could also "examine whether the algorithm can place weight on or value future profits" to determine if businesses are colluding on price.

"If the algorithm’s objective function is very short-term (e.g. maximise profit on each and every sale, with no regard for the impact of its current actions on future profits) then the algorithm is less likely to lead to coordination," the CMA said. "For tacit coordination to take place, the algorithm must be willing to sacrifice short term profits in favour of a longer-term, more profitable outcome."

"Even for the most sophisticated algorithms that learn to profit maximise over many periods using many variables there should still be a set objective function that the algorithm computes to determine its success. This objective function could in principle be audited by a competition authority, and this may provide some information about the extent to which it is capable of tacit coordination," it said.

Competition specialist Alan Davis of Pinsent Masons, the law firm behind, said:: “The report is an interesting insight into how algorithms can be used to achieve both pro-competitive and anti-competitive effects. Importantly, the report highlights that the CMA does not consider that pure ‘machine learning’ algorithms are sufficiently advanced to pose an immediate issue, rather it is primarily concerned with explicit collusion or hub and spoke effects where the same algorithm is used to pool data and generate the same results."

"The CMA equally also notes that the use of algorithms will not necessarily lead to collusion due to specific market characteristics and the likely differing approaches of retailers to algorithm technologies. Overall, the report does not identify specific concerns in relation to any particular company’s actions, but rather sets the scene for future investigations," he said.

The CMA's paper was informed by a review of academic literature and existing competition policy relating to the use of algorithms and its effect on competition, as well as by interactions with some commercial algorithm providers and other competition authorities. It also carried out "pilot tests for the presence of personalised pricing".