Data-driven pricing in markets with single and multi-unit buyers: Winners and losers
研究了数据驱动定价（统一定价、分组定价和个性化定价）在竞争市场中对企业和消费者的影响，发现分组定价通常优于其他策略，而个性化定价仅在特定条件下有效，且可能引发“个性化困境”。
Digital technologies increasingly allow firms to move beyond uniform pricing toward group pricing (such as volume discounts) and fully personalized pricing. This paper examines how data-driven pricing affects firm profitability and consumer outcomes in competitive markets where consumers differ in purchase volume and where switching or transport costs scale with quantity. We show that group pricing often dominates both uniform and personalized pricing by exploiting demand heterogeneity without triggering the intense price competition associated with full personalization. Personalized pricing, by contrast, outperforms uniform pricing only under specific conditions—namely when demand heterogeneity is high, the share of low-demand consumers is large, and high-volume buyers remain relatively mobile because switching or transport costs rise slowly with quantity. Outside these environments, personalization frequently intensifies competition and erodes margins. When adopting personalized pricing involves fixed implementation costs, firms may nonetheless adopt it defensively—even when it lowers profits—giving rise to a personalization dilemma.