Introduction
In the 21st century, competition in the corporate domain has increased exponentially. The main reason for the increase is a huge number of business establishments compete for the same pool of clients in a given niche. Companies use destructive technology to amaze a considerable consumer base by installing strategies that make the organization more appealing. As the world navigates through the information age, companies such as Alibaba, Amazon, and eBay leverage technological advancement to make strategic pricing decisions. Algorithmic pricing is a strategic competitive method where a seller sets a price that helps in maximizing its profits (Ariel & Stucke, 2016, p. 21). This paper evaluates first-degree discrimination, the fairness of price discrimination, and how organizations should regulate prices to maximize their profits and increase their competitive advantage.
Price Discrimination and Fairness
Price discrimination is a process where the business charges the maximum possible cost for every unit of a product sold. First-degree discrimination is achieved when an organization sells similar products for various fees depending on the customer. The concept of price discrimination is based on the premise that customers have different capabilities, and having a uniform price does not increase the return on investment. The principle applies where customers with high purchasing ability are charged higher costs while their counterparts get charged lower per their financial capabilities. Amazon is considered as one of the most successful online retail stores that price goods based on the customer’s abilities. The organization uses special machine learning tools to set direct prices for different customers based on the data (Kim, 2021, p. 34). For example, if Africa records the lowest sales in stone-cut supreme jewelry, Amazon lowers the price to encourage more customers to buy the product. On the contrary, Asia and America are the regions that record the highest sales ranking and experience higher pricing because the customer base knows the product’s value.
Despite many people considering price discrimination unfair to customers, economists have a contrary opinion. Price discrimination is fair to customers because each of them has an opportunity to purchase within their current financial dispensation. When a company uses uniform pricing, the people in the lower economic cadre may not realize the quality of a product. Discriminating the price by lowering the cost for the regions with lower sales is likely to improve sales and lead to brand awareness. On the other hand, areas with a higher buying index are likely to support the increased prices because the brand image and quality of the product are known (Ariel & Stucke, 2016, p. 39). Price discrimination further lowers market congestion and allows customers to shop for their products without waiting for long hours. Using uniform prices is unfair since it alienates customers from low-income regions. Price discrimination is, therefore, the fairest means of charging customers because customers from all walks of life have a chance to purchase the product.
Working on the Pricing System
The introduction of big data in the business domain makes decision-making and pricing easier. Price discrimination is determined by the statistical and probabilistic data available to the business. Amazon operates a substantial online database where the data available can be analyzed, and the result used to offer a new price. The pricing system uses self-learning algorithms to set the costs after a continuous analysis of the market data and the variables such as demand, supply, and the competitor’s pricing (Ariel, & Stucke, 2016, p. 49). It is imperative to note that the self-learning algorithm is not meant to stabilize the market dynamics. Instead, it conducts an independent analysis to offer the best recommendations on the best price to set for a specific location (Ariel, & Stucke, 2016, p. 51). The pricing system works like Bayesian optimal pricing because it sets the price without considering customer bids.
Artificial intelligence (AI) and machine learning are essential elements of the price discrimination system. AI works in an algorithm where variables such as demand, supply, customer satisfaction, competitor pricing, and time are required to determine the best-fitted price to maximize profits. Machine learning takes place in different epochs considering environmental factors. It is essential to supervise the people carrying out the pricing procedures because the price is determined by multiple factors (Calvano et al. 2020, p. 65). Failure to control the people who set the price may encourage them to declare a new price without considering the market dynamics. When a price is set without proper knowledge of the market factors, it is likely to lower the organization’s competitive advantage. Supervision is vital because it is supportive, administrative, and educational. The dynamic corporate domain requires the people setting the price to be educated on the trends, be supported, and allow better admiration for optimum success.
Regulation of Pricing Systems
Price regulation enables the price setter to operate within the required threshold for optimum performance. Overpricing leads to lower sales and, consequently, the closure of the business. Therefore, lower prices reduce the return on investment and lead to the failure of the company. The price controls set the floor and the ceiling for the price and introduce ethics and professionalism to make the brand more appealing to the markets (Ariel & Stucke, 2016, p. 18). The costs must be ethical and within the acceptable price limits set by the governmental regulations of the day. The people setting the price must therefore ensure that the price reflects compliance with the local authorities and the ethical standards.
The right professionals to navigate and control the pricing systems are the Information Technology (IT) Officers. The latter have profound knowledge of big data and have the necessary tools to analyze complex data for decisions. A particular course in big data, machine learning, and business administration makes it possible for the person to make better decisions. The IT officials would be more reliable because data analysis offers the most accurate scenarios, and judgment based on valid statistical analysis yields the best results. The governmental agencies in charge of quality controls play an important role in ensuring that the price set is not only friendly to the customer but also legally permissible (Calvano et al. 2020, p. 25). However, IT professionals with business basics must take special courses such as decision-making for optimal prices. Indeed, prices founded on the principle of customer satisfaction help improve customer experience and maintain customer loyalty in the discourse (Sánchez-Cartas et al. 2020, p. 77). Overall, governmental agencies should create working groups of IT professionals with access to these algorithms to regularly check that the pricing system is fair for local customers.
Conclusion
Algorithmic pricing aims to maximize profits and enhance sustainability in the organization. If the pricing fails to improve profits or sustainability, it requires an improvement. Business owners and investors must ensure that customers’ views, opinions, and concerns are included in the pricing mechanisms. Since customers are the primary determinant in the business domain, incorporating their ideas makes the prices strategic. The experiments to improve algorithmic pricing must be preceded by thorough research into the market, testing the market, and offering unique prices for different regions depending on financial capability.
References
Ariel, E., & Stucke, M. E. (2016). Virtual competition. The promise and perils of the algorithm-driven economy. Harvard University Press.
Calvano, E., Calzolari, G., Denicolo, V., & Pastorello, S. (2020). Artificial intelligence, algorithmic pricing, and collaboration. American Economic Review, 110(10), 3267-3297.
Kim, K. (2021). Amazon-induced price discrimination under the Robinson–Patman act. Columbia Law Review, 121(6), 160-185.
Sánchez-Cartas, J. M., Tejero, A., & León, G. (2021). Algorithmic pricing and price gouging. Consequences of high-impact, low probability events. Sustainability, 13(5), 2542. Web.