This study proposes a novel framework for designing business rule analytics to assist businesses offering digital content in effectively converting free-only users (FOUs) into paying customers. Based on the theory of expected utility, we expand upon traditional frequency-driven rule analytics by integrating three business-relevant factors (target size, conversion profit, and conversion likelihood) into the process of generating recommendations for FOUs in digital content markets. The framework was tested using two different types of empirical analysis. We conducted a field experiment collaborating with a nationwide e-book store to determine how FOUs responded to the recommendations generated under the proposed framework. Furthermore, we analyzed over 5 million transactions collected from the e-book seller and a mobile application provider to examine the impact of customer segmentation on the effectiveness of our approach. Our findings suggest that business analytics derived from the utility-based mechanisms can significantly enhance digital content providers' business performance.

本研究提出了一种设计业务规则分析的新框架，以帮助提供数字内容的企业有效地将免费用户 (FOU) 转换为付费客户。基于预期效用理论，我们扩展了传统的频率驱动规则分析，将三个业务相关因素（目标规模、转换利润和转换可能性）集成到为数字内容市场中的 FOU 生成建议的过程中。使用两种不同类型的实证分析对该框架进行了测试。我们与一家全国性的电子书店合作进行了一项实地实验，以确定 FOU 如何响应在拟议框架下产生的建议。此外，我们分析了从电子书销售商和移动应用程序提供商处收集的超过 500 万笔交易，以检查客户细分对我们方法有效性的影响。我们的研究结果表明，源自基于效用机制的业务分析可以显着提高数字内容提供商的业务绩效。