Personalization and Data-Driven Pricing

E-commerce generates vast amounts of user data—browsing history, purchase history, search queries, device type, location, time of visit. Retailers use this data to personalize both product recommendations and prices. Machine learning algorithms predict what products users want and what prices they'll accept. Personalization increases firm revenue by showing relevant products and capturing more consumer surplus through price discrimination. However, personalization raises ethical and privacy concerns: Are users aware their prices are personalized? Is it fair to charge users differently based on algorithms they don't understand? Does it exploit behavioral biases like loss aversion or overconfidence? Regulators increasingly scrutinize data practices.…

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