地 点：商学院116 远东厅
Platforms of digital content, such as Amazon Kindle and YouTube Original, commonly leverage freemium pricing – offering initial content (e.g., first few book chapters) for free in hope to monetize later content. While offering scant free content risks customer churn, offering a lavish amount undermines profit. More importantly, distinct from freemium pricing for consumer goods or technology products, optimal freemium pricing for digital content is connected to content dynamics (e.g., charging at an emotional peak with the greatest willingness-to-pay). Collaborating with a leading e-book platform with $270 million annual revenues, we determine optimal charging points via a large-scale randomized field experiment with 1.3 million customers; and further leverage text analytics to uncover the connection between optimal charging points and content dynamics. The identified optimal charging points, often occur after the second sentiment culmination, escalate revenues by 50%, translating into $100+ million lift for the platform. We further explore the underlying mechanisms by analyzing the moment-to-moment synchrony between the book content and customer comments; and finally automate personalized ricing. This research optimizes a prominent, yet under-studied, form of freemium pricing; and offers AI-driven acceleration of digital transformation to automate conventionally intuition-laden, labor-intensive pricing.