We consider how to make dynamic pricing decision for Chinese Online (COL) at time-points, an online publisher that allow authors to sell their ongoing book projects. Instead of paying for a book, readers pay for each chapter (pay-per-chapter mode) of the ongoing book project. This mode allows readers to pay for as many chapters as they want without taking the risk that the releasing of new chapters might be delayed or stopped. Despite of the dynamics of chapter-by-chapter released of COL products, the fixed pricing strategy (FPS) does not make fully use of the reading data generated by releasing chapters of the ongoing book. We propose a learning-based dynamic pricing strategy (LDPS) that exploits the newly information to maximize cumulative revenue for the publisher. The LDPS captures the ever changing features of readers. It employs the Thompson sampling method to balance the exploration of investigating different prices sufficiently with the exploitation of settling on the optimal price. Taking COL as a case study and implementing our strategy in the context of the aforementioned real-life data set, we show that LDPS outperform several classical strategies such as Greedy, Prior-Free TS and Prior-Given TS, and average revenue of LDPS is increased by 0.5 % average per time-point compared to the publisher's historical decisions. We also provide some management implications for the COL publisher by analyzing the pricing range of different genres of books and the choice of the exploration threshold parameter.

中文翻译：

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基于学习的按章付费模式在线出版商动态定价策略——以COL为例


我们考虑如何在时间点为中文在线（COL）做出动态定价决策，这是一个允许作者销售其正在进行的图书项目的在线出版商。读者无需为书籍付费，而是为正在进行的图书项目的每一章付费（按章付费模式）。这种模式允许读者根据需要支付任意数量的章节，而无需承担新章节发布可能被延迟或停止的风险。尽管COL产品的逐章发布具有动态性，但固定定价策略（FPS）并没有充分利用当前书籍发布章节所产生的阅读数据。我们提出了一种基于学习的动态定价策略（LDPS），该策略利用新信息来最大化发布商的累积收入。 LDPS 捕捉了读者不断变化的特征。它采用汤普森抽样方法来平衡充分调查不同价格的探索与确定最优价格的利用。以 COL 作为案例研究，并在上述现实数据集的背景下实施我们的策略，我们表明 LDPS 优于 Greedy、Prior-Free TS 和 Prior-Given TS 等几种经典策略，LDPS 的平均收入为与出版商的历史决策相比，每个时间点平均增加 0.5%。我们还通过分析不同类型图书的定价范围和探索阈值参数的选择，为 COL 出版商提供了一些管理启示。