Perfect price discrimination
Pricing algorithms may soon achieve perfect price discrimination and then we may no longer need pricing regulations
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Human society was forged in the market place—a battleground where, since time immemorial, buyers and sellers have met to engage in daily combat. Buyers are constantly on the lookout for a deal—the opportunity to buy a product that they want at a fair price. Sellers on the other hand, are always looking to exploit the varying willingness of purchasers to buy a product so that they can quote a price that best matches what the buyer is willing to pay.
Key to success in this battle is information—knowledge about the actual cost of production and true consumer motivations. As production scaled with industrialization, the information advantage swung in favour of sellers who used it to control price. In response, governments around the world scrambled to find ways to protect the rights of consumers. They implemented unfair trade practice regulations and drove markets towards adopting a single, non-negotiable price for each product. In a sense, this forced buyers and sellers into an uneasy truce in which both parties had to agree to forgo something for the greater good of market stability. Buyers gave up the opportunity to find a better bargain while sellers could no longer exploit the customer’s willingness to pay more than the published price.
These concepts were eventually codified into law and countries around the world began to adopt regulations to promote price certainty. In India, manufacturers are required to list the maximum retail price (MRP) at which their goods can be sold—and above which retailers are forbidden from selling. This ensures that consumers have full price transparency by ensuring that all hidden costs (taxes, distributor margins, etc) are included in the published price.
The trouble with this construct is that distributor margins can vary depending on whether the product is being sold in a high-end mall or at a kirana store. As a result, while the obligation to stipulate the maximum price offers price transparency it has resulted in forcing the manufacturers to list a higher retail price than would otherwise have been necessary.
One of e-commerce’s greatest contributions to the market economy is in improving the extent to which data is being used to make sales decisions. Online marketplaces offer consumers an unprecedented opportunity to discover the best price for a product. As a result, customers can shop around till they find a seller who gives them terms they prefer. In order to remain attractive, retailers are forced to offer discounts to the MRP, sacrificing margins for greater sales volumes and in the process driving prices steadily downwards. Consumers have, consequently, managed to wrest back some of the informational advantage.
Retailers, in general, have been comparatively slow to respond to the threat of this new information symmetry, continuing to price their products using more art than science. But as their bottom line started to take a beating, they too have been forced to develop specialized data-driven sales strategies.
It didn’t take sellers long to realize that, just as online marketplaces offer consumers the opportunity to find the best deal, they provide sellers with a unique tool with which to better understand consumer behaviour. Whenever customers shop online they leave behind a digital trail that offers incredibly granular insights into their price sensitivity, behaviour, response to advertising and influence of reputation—at levels of detail that were previously unthinkable.
The more savvy retailers have begun to deploy significant resources to eke out these insights. They use their online platforms to regularly experiment with shoppers —offering strategic discounts and carefully timed promotions to be able to better observe customer responses. These experiments are designed to predict the shape of the demand curve, allowing retailers to find the optimal, profit-maximizing figure for a given product based on prevailing circumstances.
The hope is that these big data algorithms will eventually teach sellers how to achieve “perfect price discrimination”—the ability to quantify the ideal price for a product that has been calibrated so as to precisely coincide with the value that a purchaser is willing to pay. Once sellers know the walk-away price of each buyer they will be able to list their products just south of that number in order to extract the maximum possible profit out of the sale.
In this new world, MRP, as a concept, is redundant. Once retail algorithms are able to ascertain each individual customer’s perception of the ideal price, the fact that one consumer pays more than another will no longer matter as they are both paying exactly what, in their individual perception, the product is worth. So long as these algorithms are fair and capable of dynamically meeting the value expectations of the buyer, we may no longer need the regulatory protection that our price regulations were designed to offer.
Rahul Matthan is a partner at Trilegal. Ex Machina is a column on technology, law and everything in between. His Twitter handle is @matthan.