Final answer:
Big data and predictive analytics are utilized to enhance dynamic pricing strategies, allow for price personalization, and increase price transparency. They heavily depend on machine learning and can help reduce the risk of imperfect information, which affects prices and market quantities. Price floors have the greatest impact when set well above the equilibrium, while price ceilings have the most significant effect when set well below it.
Step-by-step explanation:
The student is asking about the role of big data and predictive analytics in the context of pricing strategies. The correct choice that accurately describes the use of big data and predictive analytics in pricing is none of the options given (a-d). Instead, big data and predictive analytics are actually used to improve dynamic pricing strategies, enable price personalization, and enhance price transparency for consumers. This technology relies heavily on machine learning to analyze vast amounts of data and make accurate predictions about consumer behavior, which can then be used to set prices more effectively.
To reduce the risk of imperfect information, companies can implement robust data collection and analysis systems, use a variety of data sources to cross-verify information, and apply advanced analytics to ensure the accuracy of predictions. Imperfect information can lead to mispricing, which affects the price, quantity, and quality of goods and services in the market.
Price Floors and Price Ceilings
A price floor will have the largest effect when it is set substantially above the equilibrium price, as it will create a surplus where the quantity supplied exceeds the quantity demanded. Conversely, a price ceiling will have the largest effect when set substantially below the equilibrium price, resulting in a shortage where the quantity demanded exceeds the quantity supplied.