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Designing Effective Music Excerpts

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  • Emaad Manzoor
  • Nikhil Malik

Abstract

Excerpts are widely used to preview and promote musical works. Effective excerpts induce consumption of the source musical work and thus generate revenue. Yet, what makes an excerpt effective remains unexplored. We leverage a policy change by Apple that generates quasi-exogenous variation in the excerpts of songs in the iTunes Music Store to estimate that having a 60 second longer excerpt increases songs' unique monthly listeners by 5.4% on average, by 9.7% for lesser known songs, and by 11.1% for lesser known artists. This is comparable to the impact of being featured on the Spotify Global Top 50 playlist. We develop measures of musical repetition and unpredictability to examine information provision as a mechanism, and find that the demand-enhancing effect of longer excerpts is suppressed when they are repetitive, too predictable, or too unpredictable. Our findings support platforms' adoption of longer excerpts to improve content discovery and our measures can help inform excerpt selection in practice.

Suggested Citation

  • Emaad Manzoor & Nikhil Malik, 2023. "Designing Effective Music Excerpts," Papers 2309.14475, arXiv.org.
  • Handle: RePEc:arx:papers:2309.14475
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    References listed on IDEAS

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    1. Hannes Datta & George Knox & Bart J. Bronnenberg, 2018. "Changing Their Tune: How Consumers’ Adoption of Online Streaming Affects Music Consumption and Discovery," Marketing Science, INFORMS, vol. 37(1), pages 5-21, January.
    2. Clément de Chaisemartin & Xavier D'Haultfœuille, 2020. "Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects," American Economic Review, American Economic Association, vol. 110(9), pages 2964-2996, September.
    3. Jehoshua Eliashberg & Anita Elberse & Mark A.A.M. Leenders, 2006. "The Motion Picture Industry: Critical Issues in Practice, Current Research, and New Research Directions," Marketing Science, INFORMS, vol. 25(6), pages 638-661, 11-12.
    4. Alberto Abadie & Susan Athey & Guido W Imbens & Jeffrey M Wooldridge, 2023. "When Should You Adjust Standard Errors for Clustering?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 138(1), pages 1-35.
    5. Dmitry Arkhangelsky & Susan Athey & David A. Hirshberg & Guido W. Imbens & Stefan Wager, 2021. "Synthetic Difference-in-Differences," American Economic Review, American Economic Association, vol. 111(12), pages 4088-4118, December.
    6. Luis Aguiar & Joel Waldfogel, 2018. "Platforms, Promotion, and Product Discovery: Evidence from Spotify Playlists," NBER Working Papers 24713, National Bureau of Economic Research, Inc.
    7. Simon Freyaldenhoven & Christian Hansen & Jorge Perez Perez & Jesse Shapiro, 2021. "Visualization, Identification, and stimation in the Linear Panel Event-Study Design," Working Papers 21-44, Federal Reserve Bank of Philadelphia.
    8. John J. Horton, 2023. "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?," NBER Working Papers 31122, National Bureau of Economic Research, Inc.
    9. Ramnath K. Chellappa & Shivendu Shivendu, 2005. "Managing Piracy: Pricing and Sampling Strategies for Digital Experience Goods in Vertically Segmented Markets," Information Systems Research, INFORMS, vol. 16(4), pages 400-417, December.
    10. Yi Xiang & David A. Soberman, 2011. "Preview Provision Under Competition," Marketing Science, INFORMS, vol. 30(1), pages 149-169, 01-02.
    11. Shijie Lu & Xin (Shane) Wang & Neil Bendle, 2020. "Does Piracy Create Online Word of Mouth? An Empirical Analysis in the Movie Industry," Management Science, INFORMS, vol. 66(5), pages 2140-2162, May.
    12. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2004. "How Much Should We Trust Differences-In-Differences Estimates?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(1), pages 249-275.
    13. Kristelia García & James Hicks & Justin McCrary, 2020. "Copyright and Economic Viability: Evidence from the Music Industry," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 17(4), pages 696-721, December.
    14. Ken Hendricks & Alan Sorensen, 2009. "Information and the Skewness of Music Sales," Journal of Political Economy, University of Chicago Press, vol. 117(2), pages 324-369, April.
    15. Nicola Montecchio & Pierre Roy & François Pachet, 2020. "The skipping behavior of users of music streaming services and its relation to musical structure," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-16, September.
    16. John J. Horton, 2023. "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?," Papers 2301.07543, arXiv.org.
    17. Jonathan Lee & Peter Boatwright & Wagner A. Kamakura, 2003. "A Bayesian Model for Prelaunch Sales Forecasting of Recorded Music," Management Science, INFORMS, vol. 49(2), pages 179-196, February.
    18. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, June.
    19. Xin (Shane) Wang & Shijie Lu & X I Li & Mansur Khamitov & Neil Bendle & J. Jeffrey Inman & Andrew T Stephen, 2021. "Audio Mining: The Role of Vocal Tone in Persuasion," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 48(2), pages 189-211.
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