Abstract
Emerging tracking data enable precise predictions of individuals' reservation values. However, firms may be reluctant to overtly adopt personalized pricing. This paper proposes a strategy that embeds personalization within a dynamic pricing framework, tailoring prices privately while committing to infrequent adjustments to obscure its use. Simulation analyses based on both theoretical and empirically estimated distributions of consumer valuations reveal that profits rise most when consumer arrivals are moderately frequent. Increasing the precision of individual‐level demand estimates broadens the range of products for which this strategy is profitable. These findings suggest the approach may be an auspicious strategy for online platforms.