Dynamic Pricing Algorithms
Dynamic Pricing is a type of pricing method that adjusts prices for items based on real-time market conditions[1]. This is typically achieved by using algorithms inputting variables like competitors' prices, supply and demand quantities, customers' demographics and personal preferences, seasonal factors, and more[2]. As a result of the growth of the internet and consequently an increase in online transactions, a variety of industries utilize dynamic pricing as part of their business model. Common industries that implement dynamic pricing include advertising, entertainment, sports, airline travel, utilities, and the e-commerce industry as a whole[3]. Dynamic pricing can allow companies to collect greater profits because of rapid price adjustments, but sometimes raises ethical concerns related to areas of data privacy, social inequality, information integrity, and more.
Contents
History of Dynamic Pricing
Dynamic pricing has mainly grown out of two areas of research: statistical learning and price optimization[4]. Statistical learning refers to how dynamic pricing algorithms are able to adjust prices based on numerical data collected over time, and price optimization involves calculating prices that maximize revenue for a business. In the late 1970's, the airline industry was one of the first main players to use dynamic pricing as a result of more relaxed legislation from the United States congress.[5]. Up until that point, the prices of airline tickets were set by the government. When the federal government gave airline companies autonomy over ticket pricing, dynamic pricing was developed in the industry.
With the advent of the computer revolution, dynamic pricing has grown increasingly complex. Although early dynamic pricing was mainly based on supply and demand conditions within the market, modern applications can use highly complex variables like customers' purchase histories and locations.
Main Types of Dynamic Pricing Algorithms
Segmented Pricing
Seasonal & Time-based Pricing
Competition Pricing
Peak Pricing
Bulk? Pricing
Industry Usage
Case Studies of Usage
Coca Cola
In 1999, Coca Cola CEO M. Douglas Ivester attempted to deploy Coca-Cola vending machines that used dynamic pricing.[6]. Ivester was attempting to capitalize on company data that showed company's traditional vending machines sold a lot more coke in warmer vending conditions. According to the CEO, a dynamic pricing-based vending machine model that could charge more under hotter temperatures would be a profit-making machine. This announcement was quickly met with public outcry over concerns of price-gouging, and was never put into effect by the Coca-Cola company. The Coca-Cola case study is often cited when analyzing the effects of dynamic pricing on consumer sentiment; price fluctuations have been found to often have negative impacts on brand loyalty and image, which in turn can make people experience "customer betrayal"[7].
Uber
Uber has had controversies related to the app's usage of "Peak Pricing", which is a type of dynamic pricing that increases prices during times of increased demand, such as in a crowded area after a major event[3]. While this concept typically works, it has historically had severe negative impacts during situations like security emergencies or extreme weather scenarios. During a 2017 terrorist attack in London, a 2016 bombing in New York City, and a 2017 taxi drivers' strike in the United States, Uber fares increased as much as 500% [3]. Although raising prices during crowded times isn't inherently controversial, these instances have raised ethical questions about how dynamic pricing algorithms don't take into account factors like human health and safety.
Root Insurance
Started in 2015, Root Insurance is a personal insurance provider that introduced a novel way to measure customer's driving behaviors in order to calculate insurance risk premiums: dynamic pricing[8]. The company uses variables like braking strength, driving speed, and more. Root Insurance has explicitly claimed that they place an emphasis on not using variables in the algorithm that could create socioeconomic inequality: for example, education or occupation. The company also tries to make the algorithm as transparent as possible through usage of the Root insurance app, which breaks down the algorithm's weighting of driving skills, insurance fraud statistics, and more[3]. The company has also pledged to cease using customers' credit scores by the year 2025, which is a variable that some argue could increase inequities. This case study highlights how dynamic pricing has the potential to increase customer satisfaction: Root places an emphasis on price transparency, minimizing costs for customers, and customer relationships[8].
Ethical Dilemmas with Dynamic Pricing
Data Privacy
Social Inequality
Information Integrity
- ↑ pwc.de. (June, 2020). "Ethical Aspects of Dynamic Pricing" PricewaterhouseCoopers GmbH. Retrieved January 25, 2023.
- ↑ Wakabayashi,Daisuke. (February 6, 2022). "Does Anyone Know What Paper Towels Should Cost?" New York Times. Retrieved January 25, 2023.
- ↑ 3.0 3.1 3.2 3.3 Bertini, Marco & Koenigsberg, Oded. (September 2021). "The Pitfalls of Pricing Algorithms" Harvard Business Review. Retrieved January 24, 2023.
- ↑ den Boer, Arnoud. (June 2015). "Dynamic pricing and learning: Historical origins, current research, and new directions" Surveys in Operations Science and Management Science. Retrieved January 24, 2023.
- ↑ Goldstein, Jacob. (June 17, 2015). "The Birth and Death of the Price Tag" NPR's Planet Money. Retrieved January 24, 2023.
- ↑ Leonhardt, David. (June 27, 2005). "Why Variable Pricing Fails at the Vending Machine" New York Times. Retrieved January 24, 2023.
- ↑ Buchh, Ziad. (July 25, 2022). "Online pricing algorithms are gaming the system, and could mean you pay more" NPR's Morning Edition. Retrieved January 24, 2023.
- ↑ 8.0 8.1 Clayton, Alex. (October 27, 2020). "Root Insurance IPO | S-1 Breakdown" Meritech Capital. Retrieved January 24, 2023.
