An Empirical Analysis of Digital Music Bundling Strategies
利用面板数据和准随机实验估计数字歌曲和专辑的需求弹性，构建结构模型评估不同定价策略下的福利变化，发现分层定价加降低专辑价格能同时增加唱片公司收入和消费者剩余。
We use panel data on digital song and album sales coupled with a quasi-random price experiment to determine own- and cross-price elasticities for songs and albums. We then develop a structural model of consumer demand to estimate welfare under various policy relevant counterfactual scenarios. This approach represents an early application of the “big data” management paradigm within the media industries and provides managers with detailed guidance on optimal pricing and marketing strategies for digital music. Our results show that tiered pricing coupled with reduced album pricing increases revenue to the labels by 18% relative to uniform pricing policies traditionally preferred by digital marketplaces while also increasing consumer surplus by 23%. Thus, optimal tiered pricing can yield a Pareto improvement over the prior status quo. Additionally, our results indicate that even without tiered pricing, unbundling albums outperforms “album-only” pricing policies that dominated the era of physical CD/cassette sales. This paper was accepted by Alok Gupta, special issue on business analytics.