IDEAS home Printed from https://ideas.repec.org/a/kap/mktlet/v14y2003i4p307-320.html
   My bibliography  Save this article

An Easily Implemented Framework for Forecasting Ticket Sales to Performing Arts Events

Author

Listed:
  • Daniel S. Putler
  • Shilpa Lele

Abstract

This paper presents an easily used framework for modeling ticket sales to performing arts and entertainment events. Unlike existing efforts in this area, our framework allows us to: (1) model demand for events that consist of more than a single performance; (2) account for the influence of promotional effort on ticket sales; and (3) account for "sellouts" of some performances. The framework is applied to ticket sales for a university theater company, where it predicts ticket sales well in both an estimation and holdout sample. We discuss how the framework has influenced the company's marketing decisions.

Suggested Citation

  • Daniel S. Putler & Shilpa Lele, 2003. "An Easily Implemented Framework for Forecasting Ticket Sales to Performing Arts Events," Marketing Letters, Springer, vol. 14(4), pages 307-320, December.
    • Handle: RePEc:kap:mktlet:v:14:y:2003:i:4:p:307-320
    as

    Download full text from publisher

    File URL: http://journals.kluweronline.com/issn/0923-0645/contents
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ana Suárez-Vázquez, 2011. "Critic power or star power? The influence of hallmarks of quality of motion pictures: an experimental approach," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 35(2), pages 119-135, May.
    2. Ateca-Amestoy, Victoria & Prieto-Rodriguez, Juan, 2013. "Forecasting accuracy of behavioural models for participation in the arts," European Journal of Operational Research, Elsevier, vol. 229(1), pages 124-131.
    3. Hong Chen, 2010. "Using Financial and Macroeconomic Indicators to Forecast Sales of Large Development and Construction Firms," The Journal of Real Estate Finance and Economics, Springer, vol. 40(3), pages 310-331, April.
    4. Kamakura, Wagner A. & Schimmel, Carl W., 2013. "Uncovering audience preferences for concert features from single-ticket sales with a factor-analytic random-coefficients model," International Journal of Research in Marketing, Elsevier, vol. 30(2), pages 129-142.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:mktlet:v:14:y:2003:i:4:p:307-320. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.