混合收益模式下在线内容免费数量与广告强度的组合优化

Combinatorial Optimization of Free Size and Advertising Intensity of Online Content in Mixed-Revenue Mode

在线内容的混合收益模式是指运营商从免费内容中获取广告收益,并从高服务品质的付费内容中获取订阅收益的运营模式。在该模式下,运营商的一个主要决策问题是如何确定在线内容免费数量与广告强度的组合,从而获取最大收益。基于免费体验前后消费者品质感知的差异建立了从免费体验到付费升级的随机行为模型,在此基础上构建免费数量与广告强度的组合优化模型。结合数值实验,分别分析了消费者对内容网站品质“高估”与“低估”的情境下,免费数量与广告强度组合的优化策略。研究发现:①在“低估”情境下,运营商需采取“高免费数量+低广告强度”的组合策略来吸引客户,但随着客户群趋于稳定且消费者对网站品质感知不确定性水平大幅降低时,运营商可适当通过加大广告强度来进一步提高收益;②在“高估”情境下,如果消费者品质感知的不确定水平较低,则运营商可采取“低免费数量+高广告强度”的组合策略来提高收益;反之,运营商则要采取“高免费数量+低广告强度”的组合策略。

The mixed-revenue mode of online content refers to the fact that providers obtain advertising revenue from free online content and subscription revenue from paid content with high service quality, in which, one of the main decision-making problems for providers is the determination of the optimal combination of free size and advertising intensity to maximize profits. Based on the difference of consumers’ perception of quality before and after free-experience, this paper constructed a stochastic behavior model of ‘free to pay'. Besides, it developed an optimization model for the combination of free size and advertising intensity. Moreover, through numerical experiments, it analyzed the optimization combination strategies under two different contexts, where consumers might overvalue or undervalue the quality of the online content websites. The results show that in the context of undervaluation, providers should adopt the strategy of “greater free size and smaller advertising intensity” to attract customers. However, as the consumer base tends to stabilize and perceived uncertainty decrease, providers should increase advertising intensity to improve revenue. In addition, in the context of overvaluation, if consumers have a low perceived uncertainty, providers should adopt the strategy of “smaller free size and greater advertising intensity”, otherwise, providers should adopt the strategy of “larger free size and lower advertising intensity”because it is the best choice where consumers have a high perceived uncertainty.