The Big Story
Why we’re all paying different prices online
Nov 10, 2024
Colin Horgan, a Toronto-based writer known for his insights on algorithmic pricing, discusses the fascinating world of personalized pricing in online shopping. He reveals how algorithms set different prices for the same item, influenced by factors like location and shopping history. Horgan contrasts personalized pricing with traditional dynamic pricing, exploring its implications on fairness and accessibility. The conversation also highlights the need for transparency in data usage and the growing role of AI in shaping consumer experiences.
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Quick takeaways
- Dynamic pricing algorithms utilize extensive user data and behavior to adjust prices, often leading to different charges for similar items.
- The rise of personalized pricing raises significant concerns regarding data privacy and transparency, leaving consumers unaware of potential discrimination.
Deep dives
Understanding Dynamic and Personalized Pricing
Dynamic and personalized pricing has become a common practice in online shopping, influenced by algorithms that utilize user data to set prices. This strategy allows companies to adjust prices based on supply and demand, as well as individual consumer behavior, thereby determining the maximum price a user is likely to pay without disengaging. A notable example is Orbitz, which started displaying higher hotel rates to Apple users based on their purchasing habits, demonstrating how user data can skew pricing. This raises concerns about fairness, as consumers may unknowingly pay different prices for the same item at different times or from different locations.
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