Main consumer choice drivers for fi lm products in the Russian market
https://doi.org/10.55959/MSU0130-0105-6-58-4-9
Abstract
Film distribution market is a highly competitive one, which only emphasizes the need to optimize costs for the production and promotion of domestic films while increasing box office potential. The purpose of this research is to identify the primary choice drivers for movies in the movie theater in current market conditions as a required prerequisite for effective promotion. The results of a content analysis of open data tracker Cinema Viewer in 2021 allow us to identify several drivers defining the choice of moviegoers. They can be divided into two main categories: content elements and promotional elements. Among content elements, the most important are film's genre, plot, and cast. The most important promotional element is the trailer. Furthermore, two findings stand out among the results of this study, which require detailed research in the further works on the subject, as they can significantly influence the promotional strategies for film products in the Russian market. Firstly, the study showed that information itself, rather than its source, is more important for the viewer, which means that further optimization of marketing budgets is possible by reducing spending on most expensive platforms (primarily TV). Secondly, recommendations play just as important a role in the choice of film as advertising, which also makes it possible to optimize the marketing budget through social media marketing.
About the Authors
V. V. Gerasimenko
Lomonosov Moscow State University
Russian Federation
Russian Federation
Moscow
Iu. A. Kheirbeik
Lomonosov Moscow State University
Russian Federation
Russian Federation
Moscow
References
1. Burykin, E. S., & Ovsyannikova, T. S. (2016). Advertisement in fi lm industry: the contemporary trend. Sovremennye Problemy Upravleniya Prirodnymi Resursami i Razvitiem Social'no-Ekonomicheskih Sistem. Materialy XII megdunarodnoi nauchnoi koferencii: v 4 ch. Ch. 2. M.: Izdatelstvo Mosc. un-ta im. S. Yu. Vitte, 71–76.
2. Petrash, N. D. (2020). Sovremennaya kinoindustriya: osnovnye marketingovye tendencii i reklamnye hody po prodvizheniyu produkta. Nauka Segodnya: Fakty, Tendencii, Prognozy. Materialy megdunarodnoi nauchno-practicheskoi koferencii. M.: Marker, 86–87.
3. Abidi, S. M. R., Xu, Y., Ni, J., Wang, X., & Zhang, W. (2020). Popularity prediction of movies: from statistical modeling to machine learning techniques. Multimedia Tools and Applications, 79(47–48), 35583–35617. https://doi.org/10.1007/s11042-019-08546-5
4. Ahmed, S., & Sinha, A. (2016). When it pays to wait: Optimizing release timing decisions for secondary channels in the fi lm industry. Journal of Marketing, 80(4), 20–38. https://doi.org/10.1509/jm.15.0484
5. An, Y., An, J., & Cho, S. (2021). Artifi cial intelligence-based predictions of movie audiences on opening Saturday. International Journal of Forecasting, 37(1), 274–288. https://doi.org/10.1016/j.ij forecast.2020.05.005
6. Basuroy, S., Chatterjee, S., & Ravid, S. A. (2003). How Critical Are Critical Reviews? The Box Offi ce Effects of Film Critics, Star Power, and Budgets. Journal of Marketing, 67(4), 103–117. https://doi.org/10.1509/jmkg.67.4.103.18692
7. Bogaert, M., Ballings, M., Bergmans, R., & van den Poel, D. (2021). Predicting Selfdeclared Movie Watching Behavior Using Facebook Data and Information-Fusion Sensitivity Analysis. Decision Sciences (Т. 52).
8. de Vany, A., & Walls, W. D. (1999). Uncertainty in the Movie Industry: Does Star Power Reduce the Terror of the Box Offi ce? Journal of Cultural Economics (Т. 23), 285–318.
9. del Vecchio, M., Kharlamov, A., Parry, G., & Pogrebna, G. (2021). Improving productivity in Hollywood with data science: Using emotional arcs of movies to drive product and service innovation in entertainment industries. Journal of the Operational Research Society, 72(5), 1110–1137. https://doi.org/10.1080/01605682.2019.1705194
10. DQL 2022 — Surfshark (б. д.). Дата обращения 15.12.2022, https://surfshark.com/dql2022?country=RU
11. Gaenssle, S., Budzinski, O., & Astakhova, D. (2019). Conquering the Box Offi ce: Factors infl uencing Success of International Movies in Russia. Information-Wissenschaft Und Praxis, 70(5–6), 245–266. www.econstor.eu
12. Gao, W., Ji, L., Liu, Y., & Sun, Q. (2020). Branding Cultural Products in International Markets: A Study of Hollywood Movies in China. Journal of Marketing, 84(3), 86–105. https://doi.org/10.1177/0022242920912704
13. Hsiao, C. H., & Yang, C. C. (2015). Exploring the eff ect of experiential marketing on movie-watching intention — The example of mobile movie theme games. Proceedings of the Annual Hawaii International Conference on System Sciences, 2015 March, 1179–1186. https://doi.org/10.1109/HICSS.2015.143
14. Huang, Y., Wang, S., & Yan, J. (2020). Research on Infl uencing Factors of Chinese Movie Microblog Marketing Eff ect Based on Multiple Linear Regression Model: Offi cial Microblog of Ne Zha as an Example. Proceedings — 2020 12th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2020, 1, 79–83. https://doi.org/10.1109/IHMSC49165.2020.00026
15. Kamal, S., El-Gabalawy, O., Zhao, N., MacLeod, J., & Contributions, E. (2020). Depiction of Mental Illness in Film and Association with Financial and Critical Success. https://doi.org/10.1101/2020.10.20.20215707
16. Khalid, J., Abbas, A., Qasim Mahmood, M., Tariq, A., Khatoon, M., Azhar, S., Campus, G., Akbar, R., Akbar, A., Meer, A., & Junaid Ud Din, M. (2020). Signifi cance of Electronic Word of Mouth (e-WOM) in Opinion Formation. IJACSA) International Journal of Advanced Computer Science and Applications (Т. 11, Issue 2). www.ij acsa.thesai.org
17. Kim, J. M., Xiao, X., & Kim, I. (2020). Hollywood movie data analysis by social network analysis and text mining. International Journal of Electronic Commerce Studies, 11(1), 75–92. https://doi.org/10.7903/ijecs.1731
18. Kim, M., & Yoo, S. (2020). The 4th V? The eff ect of word of mouth volatility on product performance. Electronic Commerce Research and Applications, 44. https://doi.org/10.1016/j.elerap.2020.101016
19. Li, B., Liu, L., Huang, R., & Ren, J. (2016). Analyzing the impact of OWOM on box offi ce revenue in the world of big data. Proceedings — 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016, 166–169. https://doi.org/10.1109/BigMM.2016.92
20. Litman, B. R. (1983). Predicting Success of Theatrical Movies: An Empirical Study. The Journal of Popular Culture, 16(4), 159–175. https://doi.org/https://doi.org/10.1111/j.0022-3840.1983.1604_159.x
21. Litman, B. R., & Kohl, L. S. (1989). Predicting Financial Success of Motion Pictures: The ’80s Experience. Journal of Media Economics, 2(2), 35–50. https://doi.org/10.1080/08997768909358184
22. Liu, A., Liu, Y., & Mazumdar, T. (2014). Star power in the eye of the beholder: A study of the infl uence of stars in the movie industry. Marketing Letters, 25(4), 385–396. https://doi.org/10.1007/s11002-013-9258-x
23. Liu, Y., & Whitman, M. J. (2006). Word of Mouth for Movies: Its Dynamics and Impact on Box Offi ce Revenue. Journal of Marketing, 70, 74–89. http://www.marketingpower.com/jmblog.
24. Ma, J., Huang, D., Markovitch, D. G., & Ratchford, B. (2018). High or low season? Contrasting launch timing considerations for big-budget and low-budget entertainment products. European Journal of Marketing, 52(9–10), 1956–1980. https://doi.org/10.1108/EJM-08-2017-0513
25. Mahmud, Q. I., Shuchi, N. Z., Tawsif, F. M., Mohaimen, A., & Tasnim, A. (2020). A machine learning approach to predict movie revenue based on pre-released movie metadata. Journal of Computer Science, 16(6), 749–767. https://doi.org/10.3844/JCSSP.2020.749.767
26. Mediascope: Social Media (б. д.). Дата обращения 15.12.2022, https://mediascope.net/upload/iblock/5ab/8bh9sab0ioqdvufiv52lhw3ccruhq585/%D0%9D%D0%A0%D0%A4_SocialMedia_%D0%A1%D1%83%D0%B0%D0%BD%D0%BE%D0%B2%D0%B0_11.11.22.pdf
27. Mohr, I. (2007). Buzz marketing for movies. Business Horizons, 50(5), 395–403. https://doi.org/10.1016/j.bushor.2007.04.001
28. Park, J., & Lim, C. (2021). Predicting Movie Audience with Stacked Generalization by Combining Machine Learning Algorithms. Communications for Statistical Applications and Methods, 28(3), 217–232. https://doi.org/10.29220/CSAM.2021.28.3.217
29. Sachdev, S., Agrawal, A., Bhendarkar, S., Prasad, B. R., & Agarwal, S. (2018). Movie box-offi ce gross revenue estimation. Advances in Intelligent Systems and Computing (Т. 709, 9–17). Springer Verlag. https://doi.org/10.1007/978-981-10-8633-5_2
30. Shi, Y., & Karniouchina, E. V. (2020). (When) Can Social Media Buzz Data Replace Traditional Surveys for Sales Forecasting? Rutgers Business Review, 5(1), 43–60.
31. Speedtest Global Index — Internet Speed around the world — Speedtest Global Index (б. д.). Дата обращения 15.12.2022, https://www.speedtest.net/global-index
32. Taubman, J. (1973). Mayer, michael f., the fi lm industries practical business/legalproblems in production, distribution and exhibition, hastings house, new york, n.y. 10016. pp. xii, 212 ($10.00). Performing Arts Review, 4(3–4), 213–215. https://doi.org/10.1080/00315249.1973.10593743
33. Worldwide Mobile Data Pricing 2022 | 1GB Cost in 233 Countries (б. д.). Дата обращения 15.12.2022, https://www.cable.co.uk/mobiles/worldwide-data-pricing/
34. Zhang, H., Yuan, X., & Song, T. H. (2020). Examining the role of the marketing activity and eWOM in the movie diff usion: the decomposition perspective. Electronic Commerce Research, 20(3), 589–608. https://doi.org/10.1007/s10660-020-09423-2
Supplementary files
Review
For citations:
Gerasimenko V.V., Kheirbeik I.A. Main consumer choice drivers for fi lm products in the Russian market. Moscow University Economics Bulletin. 2023;(4):201-222. (In Russ.) https://doi.org/10.55959/MSU0130-0105-6-58-4-9
Views:
534
Email this article 