Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Review of Marketing Science

Editor-in-Chief: Haruvy, Ernan / Popkowski-Leszczyc, Peter T.L.


CiteScore 2018: 0.12

SCImago Journal Rank (SJR) 2018: 0.114
Source Normalized Impact per Paper (SNIP) 2018: 0.070

Online
ISSN
1546-5616
See all formats and pricing
More options …

Role of Advertising and Consumer Interest in the Motion Picture Industry

Dongling Huang / Andrei Strijnev
  • Naveen Jindal School of Management, University of Texas at Dallas, 800 West Campbell Road, Richardson, TX 75080, USA
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Brian Ratchford
  • Naveen Jindal School of Management, University of Texas at Dallas, 800 West Campbell Road, Richardson, TX 75080, USA
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2015-03-10 | DOI: https://doi.org/10.1515/roms-2014-0005

Abstract

Advertising is commonly used as a major marketing tool by many firms to support their new product release. Quantifying the effectiveness of pre-release advertising campaigns, however, is both challenging, since no sales data are available, and costly, because of the need to conduct consumer surveys. This is especially true for movie industry for which the majority of advertising dollars are spent before the movie’s release. Using the recent availability of online data on consumer search behaviors on a popular website dedicated to the movie industry, we construct a consumer interest measure to help the decision makers evaluate their advertising effectiveness. We build a dynamic model to show how this cost-efficient measure of consumer interest can be used to capture pre-release advertising dynamics, and the impact of advertising on weekly movie revenues starting with the opening week. Using a procedure based on Naik, Mantrala, and Sawyer (1998) and controlling for endogeneity, we estimate response, forgetting and wear-out of pre-release and post-release advertising for a large sample of movies. Our empirical results show that advertising generates goodwill, and that the resulting goodwill is associated with increased revenues. Our results suggest that big-budget movies can increase advertising effectiveness by making small adjustments to their advertising dynamics, but that spending more advertising dollars will likely not be effective. The data on the evolution of consumer interest can be a valuable and inexpensive tool for measuring advertising effectiveness and understanding sales dynamics.

Keywords: new products; dynamic effect of advertising; dynamic linear models; consumer interest; Bayesian estimation

References

  • Bass, F. M., N. Bruce, S. Majumdar, and B. P. S. Murthi. 2007. “Wearout Effects of Different Advertising Themes: A Dynamic Bayesian Model of the Advertising-Sales Relationship.” Marketing Science 26 (2): 179–95.CrossrefGoogle Scholar

  • Basuroy, S., K. K. Desai, and D. Talukdar. 2006. “An Empirical Investigation of Signaling in the Motion Picture Industry.” Journal of Marketing Research 43 (2): 287–95.CrossrefGoogle Scholar

  • Boulding, W., R. Morgan, and R. Staelin. 1997. “Pulling the Plug to Stop the New Product Drain.” Journal of Marketing Research 34 (1): 164–76.CrossrefGoogle Scholar

  • Chevalier, J., and A. Goolsbee. 2003. “Measuring Prices and Price Competition Online : Amazon Vs. Barnes and Noble.” Quantitative Marketing and Economics 1 (2): 203–22.CrossrefGoogle Scholar

  • Chevalier, J., and D. Mayzlin. 2006. “The Effect of Word of Mouth on Sales: Online Book Reviews.” Journal of Marketing Research 43 (3): 345–54.CrossrefGoogle Scholar

  • Chintagunta, P. K., S. Gopinath, and S. Venkataraman. 2010. “The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets.” Marketing Science 29 (5): 944–57.Web of ScienceCrossrefGoogle Scholar

  • Chow, G. C. 1960. “Tests of Equality between Sets of Coefficients in Two Linear Regressions.” Econometrica 28 (3): 591–605.CrossrefGoogle Scholar

  • Colley, R. H. 1961. Defining Advertising Goals for Measured Advertising Results. New York: Association of National Advertisers.Google Scholar

  • Dellarocas, C., X. M. Zhang, and N. F. Awad. 2007. “Exploring the Value of Online Product Reviews in Forecasting Sales: The Case of Motion Pictures.” Journal of Interactive Marketing 21 (4): 23–45.CrossrefGoogle Scholar

  • Duan, W., B. Gu, and A. B. Whinston. 2008. “The Dynamics of Online Word-of-Mouth and Product Sales – An Empirical Investigation of the Movie Industry.” Journal of Retailing 84 (2): 233–42.Web of ScienceCrossrefGoogle Scholar

  • Elberse, A., and B. Anand. 2007. “The Effectiveness of Pre-Release Advertising for Motion Pictures: An Empirical Investigation Using a Simulated Market.” Information Economics and Policy 19: 319–43.CrossrefGoogle Scholar

  • Elberse, A., and J. Eliashberg. 2003. “Demand and Supply Dynamics for Sequentially Released Products in International Markets: The Case of Motion Pictures.” Marketing Science 22 (3): 329–54.CrossrefGoogle Scholar

  • Eliashberg, J., A. Elberse, and M. A. A. M. Leenders. 2006. “The Motion Picture Industry: Critical Issues in Practice, Current Research, and New Research Directions.” Marketing Science 25 (6): 638–61.CrossrefGoogle Scholar

  • Eliashberg, J., J. -J. Jonker, M. S. Sawhney, and B. Wierenga. 2000. “MOVIEMOD: An Implementable Decision-Support System for Prerelease Market Evaluation of Motion Pictures.” Marketing Science 19 (3): 226–43.CrossrefGoogle Scholar

  • Eliashberg, J., and S. M. Shugan. 1997. “Film Critics: Influencers or Predictors.” Journal of Marketing 61 (2): 68–78.CrossrefGoogle Scholar

  • Hanssens, D. M., L. J. Parsons, and R. L. Schultz. 2001. Market Response Models: Econometric and Time Series Analysis, 2nd ed. Boston, MA: Kluwer Academic Publishers.Google Scholar

  • Ho, J. Y., C. Tirtha Dhar, and C. B. Weinberg. 2009. “Playoff Payoff: Super Bowl Advertising for Movies.” International Journal of Research in Marketing 26 (3): 168–79.Web of ScienceCrossrefGoogle Scholar

  • Huang, D., Y. Ying, and A. Strijnev. 2012. “Identifying Social Learning and Network Externalities in Decision-making: Evidence from the Movie Industry.” Working Paper. Troy: Rensselaer Polytechnic Institute.Google Scholar

  • Joo, H. H. 2009. “Social Learning and Optimal Advertising in the Motion Picture Industry.” Working Paper. Athens: Ohio University.Google Scholar

  • Joshi, A. M., and D. Hanssens. 2010. “Movie Advertising and the Stock Market Valuation of Studios: A Case of “Great Expectations?”.” Marketing Science 28 (2): 239–50.CrossrefWeb of ScienceGoogle Scholar

  • Klein, B., and K. B. Leffler. 1981. “The Role of Price in Guaranteeing Quality.” Journal of Political Economy 89: 615–41.CrossrefGoogle Scholar

  • Lavidge, R., and G. Steiner. 1961. “A Model for Predictive Measurements of Advertising Effectiveness.” Journal of Marketing 25 (4): 59–62.CrossrefGoogle Scholar

  • Litman, B. R. 1983. “Predicting Success of Theatrical Movies: An Empirical Study.” Journal of Popular Culture 16 (4): 159–75.CrossrefGoogle Scholar

  • Liu, Y. 2006. “Word of Mouth for Movies: Its Dynamics and Impact on Box Office Revenue.” Journal of Marketing 70 (3): 74–89.CrossrefGoogle Scholar

  • Luan, Y. J., and K. Sudhir. 2010. “Forecasting Marketing-Mix Responsiveness for New Products.” Journal of Marketing Research 47 (3): 444–57.CrossrefWeb of ScienceGoogle Scholar

  • Naik, P. A., M. K. Mantrala, and A. G. Sawyer. 1998. “Planning Media Schedules in the Presence of Dynamic Advertising Quality.” Marketing Science 17 (3): 214–35.CrossrefGoogle Scholar

  • Naik, P. A., A. Prasad, and S. P. Sethi. 2008. “Building Brand Awareness in Dynamic Oligopoly Markets”. Management Science 54 (1): 129–38.Web of ScienceCrossrefGoogle Scholar

  • Neelamegham, R., and P. Chintagunta. 1999. “A Bayesian Model to Forecast New Product Performance in Domestic and International Markets.” Marketing Science 18 (2): 115–36.CrossrefGoogle Scholar

  • Nerlove, M., and Arrow, K.J. (1962). “Optimal Advertising Policy Under Dynamic Conditions.” Economica 129–142.CrossrefGoogle Scholar

  • Nelson, P. 1974. “Advertising as Information.” Journal of Political Economy 82 (4): 729–54.CrossrefGoogle Scholar

  • Prag, J., and J. Casavant. 1994. “An Empirical Study of the Determinants of Revenues and Marketing Expenditures in the Motion Picture Industry.” Journal of Cultural Economics 18 (3): 217–35.CrossrefGoogle Scholar

  • Sawhney, M. S., and J. Eliashberg. 1996. “A Parsimonious Model for Forecasting Gross Box-Office Revenues of Motion Pictures.” Marketing Science 15 (2): 113–31.CrossrefGoogle Scholar

  • Shapiro, C. 1982. “Consumer Information, Product Quality, and Seller Reputation.” The Bell Journal of Economics 13 (1): 20–35.CrossrefGoogle Scholar

  • Srinivasan, S., M. Vanhuele, and K. Pauwels. 2010. “Mind-Set Metrics in Market Response Models: An Integrative Approach.” Journal of Marketing Research 47 (4): 672–84.CrossrefGoogle Scholar

  • The Hollywood Reporter. 2004. “If You Spend It They Will Come.” The Hollywood Reporter, May 18, 2004: S–3.Google Scholar

  • The Motion Picture Association of America (MPAA). 2006. “U.S. Entertainment Industry: 2005 MPA Market Statistics.” www.mpaa.org

  • Vakratsas, D., and T. Ambler. 1999. “How Advertising Really Works: What Do We Really Know.” Journal of Marketing 63 (1): 26–43.CrossrefGoogle Scholar

  • Vogel, H. L. 2007. Entertainment Industry Economics – A Guide for Financial Analysis, 7th ed. Cambridge:Cambridge University Press.Google Scholar

  • Zufryden, F. S. 1996. “Linking Advertising to Box Office Performance of New Film Releases: A Marketing Planning Model.” Journal of Advertising Research 36: 29–41.Google Scholar

About the article

Published Online: 2015-03-10

Published in Print: 2015-11-01


Citation Information: Review of Marketing Science, Volume 13, Issue 1, Pages 1–40, ISSN (Online) 1546-5616, ISSN (Print) 2194-5985, DOI: https://doi.org/10.1515/roms-2014-0005.

Export Citation

©2015 by De Gruyter.Get Permission

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[2]
Dongling Huang, Dmitri G. Markovitch, and Yuanping Ying
European Journal of Marketing, 2017, Volume 51, Number 1, Page 157

Comments (0)

Please log in or register to comment.
Log in