Behavior-based algorithmic pricing
研究了企业利用数据对老客户实施个性化定价时，算法定价如何影响市场竞争，发现最优策略是对高支付意愿客户个性化定价、对低支付意愿客户统一定价，从而缓和竞争并提高行业利润。
This article studies the impact of algorithmic pricing on market competition when firms collect data to charge personalized prices to their past customers. Pricing algorithms offer to each firm a rich set of pricing strategies combining first and third-degree price discrimination: they can choose for each of their past customers whether to charge them personalized or homogeneous prices. The optimal targeting strategy of each firm consists in charging personalized prices to past customers with the highest willingness to pay and a homogeneous price to the remaining consumers, including past customers with a low valuation on whom a firm has information. This targeting strategy maximizes rent extraction while softening competition between firms compared to classical models where firms target all past customers. In turn, price-undercutting and poaching practices are not sustainable with behavior-based algorithmic pricing, resulting in greater industry profits.