Bundling Information Goods: Pricing, Profits and Efficiency
Bundling Information Goods: Pricing, Profits and Efficiency
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DOI：
10.2139/ssrn.11488
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发表时间：
1998-04
期刊：
影响因子：
--
通讯作者：
Yannis Bakos;Erik Brynjolfsson
中科院分区：
文献类型：
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作者：
Yannis Bakos;Erik Brynjolfsson
我们研究的策略是捆绑大量的信息产品，例如那些在互联网上越来越多的信息产品，并以固定价格出售。我们分析了多产品垄断者的最优捆绑策略，发现捆绑大量不相关的信息产品可以带来惊人的利润。原因在于，大数定律使得预测消费者对一捆商品的估价要比预测他们对单独销售的单个商品的估价容易得多。因此，这种“捆绑的预测价值”使得从一捆信息商品中获得比单独销售相同商品时更高的销售额、更高的经济效率和更高的每件商品利润成为可能。我们的主要结果并不适用于大多数实物商品，因为买方不使用的商品的边际生产成本通常会抵消大规模捆绑销售的预测价值带来的任何好处。虽然确定两种以上商品的最优捆绑策略是一个众所周知的难题，但我们使用统计技术为任意大小的捆绑提供了强渐近结果和利润界限。我们展示了如何使用我们的模型来分析互补和替代品的捆绑，预算约束下的捆绑，以及具有各种相关性的商品的捆绑，以及这些条件如何导致最优捆绑大小的限制。特别是我们发现，当不同市场的消费者对商品的估值存在系统性差异时，简单的捆绑将不再是最优的。然而，通过提供针对每个细分市场的不同捆绑套餐，捆绑销售减少了估值中不可预测的特殊成分的作用，从而使传统的价格歧视策略变得更加强大。我们分析的预测似乎与对互联网和在线内容、有线电视节目和版权音乐市场的经验观察相一致。
We study the strategy of bundling a large number of information goods, such as those increasingly available on the Internet, and selling them for a fixed price. We analyze the optimal bundling strategies for a multiproduct monopolist, and we find that bundling very large numbers of unrelated information goods can be surprisingly profitable. The reason is that the law of large numbers makes it much easier to predict consumers' valuations for a bundle of goods than their valuations for the individual goods when sold separately. As a result, this "predictive value of bundling" makes it possible to achieve greater sales, greater economic efficiency, and greater profits per good from a bundle of information goods than can be attained when the same goods are sold separately. Our main results do not extend to most physical goods, as the marginal costs of production for goods not used by the buyer typically negate any benefits from the predictive value of large-scale bundling. While determining optimal bundling strategies for more than two goods is a notoriously difficult problem, we use statistical techniques to provide strong asymptotic results and bounds on profits for bundles of any arbitrary size. We show how our model can be used to analyze the bundling of complements and substitutes, bundling in the presence of budget constraints, and bundling of goods with various types of correlations and how each of these conditions can lead to limits on optimal bundle size. In particular we find that when different market segments of consumers differ systematically in their valuations for goods, simple bundling will no longer be optimal. However, by offering a menu of different bundles aimed at each market segment, bundling makes traditional price discrimination strategies more powerful by reducing the role of unpredictable idiosyncratic components of valuations. The predictions of our analysis appear to be consistent with empirical observations of the markets for Internet and online content, cable television programming, and copyrighted music.