Extant research has popularized the perspective that strong network effects produce “winner-take-all” outcomes, which leads platforms to invest in user growth and encourages investors to subsidize these platforms. However, user growth does not necessarily imply strong user stickiness. Without user stickiness, strong network effects in the current period may fade in future periods, thus rendering a user growth strategy ineffective. By adding a time dimension to network effects, we developed a model of cross-period and within-period network effects to explain how different types of network effects drive value. We emphasize that the cross-period same-side network effect contributes to user stickiness, while the within-period cross-side network effect persists conditional on user stickiness. We propose that one reason for platforms having heterogeneous cross-period same-side network effects is because of the “product learning” mechanism: it is expected that products with higher uncertainty have a stronger cross-period same-side network effect. Based on different drivers, we extend the customer lifetime value model (CLV2) to two-sided platform markets, allowing us to measure how different interventions drive platform value. Using Groupon data, we verify our insights and discuss platform design choices that enhance user stickiness when the cross-period same-side network effect is weak.

中文翻译：

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用户如何在双边市场中驱动价值：重要的平台设计

现有的研究已经普及了这样一种观点，即强大的网络效应会产生“赢者通吃”的结果，这会导致平台投资于用户增长，并鼓励投资者补贴这些平台。但用户增长并不一定意味着用户粘性强。如果没有用户粘性，当前时期强大的网络效应可能会在未来时期消失，从而导致用户增长策略失效。通过为网络效应添加时间维度，我们开发了跨周期和周期内网络效应模型，以解释不同类型的网络效应如何驱动价值。我们强调，跨时期的同边网络效应有助于用户粘性，而时期内的跨边网络效应则以用户粘性为条件而持续存在。我们提出，平台具有异构跨周期同边网络效应的原因之一是“产品学习”机制：预期不确定性较高的产品具有更强的跨周期同边网络效应。基于不同的驱动因素，我们将客户终身价值模型（CLV2）扩展到双边平台市场，使我们能够衡量不同的干预措施如何驱动平台价值。利用 Groupon 数据，我们验证了我们的见解，并讨论了在跨时期同边网络效应较弱时增强用户粘性的平台设计选择。