By Zach Weigel
If you’re an avid online shopper, then this article is for you. Those familiar with data analytics and/or economics are also apt to be interested by what I’m about to tell you. Thus brace yourself for a dose of some thought-provoking, spine-tingling knowledge.
A recent investigative piece by The Atlantic magazine has revealed that application of data analytics has permeated into how sellers determine the pricing of their goods and services. Specifically, online retailers such as Amazon.com and Overstock.com have been starting to experiment with how they determine the prices of their products. And these aren’t the only two companies.
What do I mean by “experimenting with how prices are determined”? To put this in perspective it’s useful to contrast these new pricing schemes with the traditional economic model. Ordinarily, the price of a good or service is thought to be determined by the interaction between supply and demand. In this case, ideally the price of an item would reflect the intersection, or equilibrium, between the quantity of product available and the demand for said product. But with the new pricing strategies made possible by data analytics, this model is rapidly evolving.
Today, with data analytics at a seller’s disposal, the seller can better understand what the ideal price is for a given customer based on predicative indicators such as their online-browsing history. In this case, an online retailer such as Amazon can use an algorithm that mines your browsing history to predict what price you will be willing to pay. By accumulating a vast array of information from your browsing history, these algorithms can predict how affluent an individual customer is and whether they are a thrifty or lavish spender. Thus, sellers can now predict the ideal price an individual is willing to pay instead of setting the ideal price based on an aggregation of consumer demand. With this in mind, gone are the notions of universal prices because prices can now be individuated to consumers thanks to the predictive technology of data analytics.
Moreover, data analytics can also provide sellers with information about when consumers are more likely to purchase something. For example, in an intuitive sense, a consumer might be more likely to purchase a cold drink on a warm day, so a gas station would be able to extract a greater profit by raising the prices of cold drinks on a warm day. By the same token, online sellers can use data analytics to pin down when consumers are more likely to purchase something. For example, a consumer may be more likely to buy an item at 4 p.m. than 4 a.m. so an online seller could raise the price of the item at 4 p.m. in order to generate a greater profit.
This is capitalism at its finest. You would expect a seller to try to make the greatest profit possible; however, the advantage that data analytics provides poses moral concerns. Sellers have the upper hand because they can effectively rig prices to extract the maximum profit from individual consumers. Is this justifiable?
Clearly, this is an exploitative strategy that privileges sellers and consequently disadvantages consumers. So even if sellers can change prices to turn a greater profit, should they?
I’m inclined to say no. Price rigging is manipulative, and from that standpoint it is discriminatory. If a seller can treat individual consumers differently based upon defining characteristics predicted in their browsing history, that’s textbook discrimination. As Bernie Sanders would say, “It’s rigged.”
Sellers need to be held accountable. Let’s fix this problem before it grows.