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New measures of clumpiness for incidence data

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  • Yao Zhang
  • Eric T. Bradlow
  • Dylan S. Small

Abstract

In recent years, growing attention has been placed on the increasing pattern of 'clumpy data' in many empirical areas such as financial market microstructure, criminology and seismology, and digital media consumption to name just a few; but a well-defined and careful measurement of clumpiness has remained somewhat elusive. The related 'hot hand' effect has long been a widespread belief in sports, and has triggered a branch of interesting research which could shed some light on this domain. However, since many concerns have been raised about the low power of the existing 'hot hand' significance tests, we propose a new class of clumpiness measures which are shown to have higher statistical power in extensive simulations under a wide variety of statistical models for repeated outcomes. Finally, an empirical study is provided by using a unique dataset obtained from Hulu.com, an increasingly popular video streaming provider. Our results provide evidence that the 'clumpiness phenomena' is widely prevalent in digital content consumption, which supports the lore of 'bingeability' of online content believed to exist today.

Suggested Citation

  • Yao Zhang & Eric T. Bradlow & Dylan S. Small, 2013. "New measures of clumpiness for incidence data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(11), pages 2533-2548, November.
    • Handle: RePEc:taf:japsta:v:40:y:2013:i:11:p:2533-2548
    DOI: 10.1080/02664763.2013.818627
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    Cited by:

    1. Yao Zhang & Eric T. Bradlow & Dylan S. Small, 2015. "Predicting Customer Value Using Clumpiness: From RFM to RFMC," Marketing Science, INFORMS, vol. 34(2), pages 195-208, March.
    2. Ryosuke Igari & Takahiro Hoshino, 2018. "A Bayesian Gamma Frailty Model Using the Sum of Independent Random Variables: Application of the Estimation of an Interpurchase Timing Model," Keio-IES Discussion Paper Series 2018-021, Institute for Economics Studies, Keio University.
    3. Lizhen Xu & Jason A. Duan & Andrew Whinston, 2014. "Path to Purchase: A Mutually Exciting Point Process Model for Online Advertising and Conversion," Management Science, INFORMS, vol. 60(6), pages 1392-1412, June.
    4. Ivelina IVANOVA-KADIRI, 2023. "Decoding the DNA of Customer Relationships: the Role of Marketing Diagnostics in the Digital Age," Business & Management Compass, University of Economics Varna, issue 2, pages 101-109.
    5. Michael Platzer & Thomas Reutterer, 2016. "Ticking Away the Moments: Timing Regularity Helps to Better Predict Customer Activity," Marketing Science, INFORMS, vol. 35(5), pages 779-799, September.
    6. Park, Chang Hee, 2017. "Online Purchase Paths and Conversion Dynamics across Multiple Websites," Journal of Retailing, Elsevier, vol. 93(3), pages 253-265.
    7. Reutterer, Thomas & Platzer, Michael & Schröder, Nadine, 2021. "Leveraging purchase regularity for predicting customer behavior the easy way," International Journal of Research in Marketing, Elsevier, vol. 38(1), pages 194-215.

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