How e-Commerce Uses Game Theory to Capture Consumer Dollars
The rise and rapid proliferation of technology has forced companies to adapt in order to stay relevant and competitive. Specifically, the consumer retail industry has recently navigated this changing landscape by utilizing collected consumer data to make prices dynamic depending on seasonal factors and even the strategies of their competitors. These companies are trying to harness the power of technology to achieve perfect price discrimination, where the seller knows every buyer’s willingness to pay and can therefore maximize retail prices without exceeding the buyer’s walk away price.
Game theory is applied and integrated in this scenario in order to assess, as the article asks, “What is the dominant competitor’s reaction to me? And if I know the reaction to me, what is my first, best move? Which is the Nash equilibrium?” This ties in with the idea of pure Nash equilibrium we discussed in class, where both players use strategies that are best responses to each other. In the context of e-Commerce, the retailer and the consumer are the two players. A game theory graph would illustrate the company’s best response to the consumer’s willingness to pay and the consumer’s response to the retail price the company is offering for the product. There would be no dominant strategy in this game because of the availability of information about other sellers and the access to other sellers the customer has in addition to the connectivity to millions of buyers via the Internet the company has. Either party can forego engaging in this transaction and just find another customer/company to sell/buy to/from. This can be illustrated with the given example of how Amazon dropped prices on Black Friday of a Samsung TV from $350 to $250 and decided on this final price using collected data, which allowed them to surpass the competition. Amazon took this a step further by hiking the price of HDMI cables, a complementary product, knowing based on consumer data that people are less likely to shop around in pursuit of the lowest prices for smaller items than bigger ticket items. The customers’ willingness to pay was any price lower than what the competitors were offering, which turned out to be the $250 since the article implies that no other retailer offered prices that low.
The implications of this show how companies are controlling not only prices but consumers’ perception of prices, thereby using this data to surpass the competition by limiting how consumers perceive the choices they have in front of them. Furthermore, this serves as an illustration of another way we experience a loss of control in our lives. However, another side of the argument says that perfect price discrimination can positively impact consumers, since their prices will be individually tailored. At the same time, this constant changing of prices can end up overwhelming and deterring consumers from purchasing the item all together.
As of now, it is too soon to tell how and if this becomes a normalized practice that we all must adapt to. It definitely is interesting to consider the ripple effects it will have on consumer behavior in the future in addition to potential consumer protection regulation, as we are operating within a rigged sandbox where companies hold all the cards in their favor through informational advantages.