Journal of Intelligence and Information Systems (지능정보연구)

Korea Intelligent Information System Society (한국지능정보시스템학회)
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Prediction of a hit drama with a pattern analysis on early viewing ratings

초기 시청시간 패턴 분석을 통한 대흥행 드라마 예측

  • Nam, Kihwan (College of Business, Korea Advanced Institute of Science and Technology (KAIST)) ;
  • Seong, Nohyoon (College of Business, Korea Advanced Institute of Science and Technology (KAIST))
  • 남기환 (한국과학기술원 경영대학 경영공학부) ;
  • 성노윤 (한국과학기술원 경영대학 경영공학부)
  • Received : 2018.05.20
  • Accepted : 2018.11.26
  • Published : 2018.12.31
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Abstract

The impact of TV Drama success on TV Rating and the channel promotion effectiveness is very high. The cultural and business impact has been also demonstrated through the Korean Wave. Therefore, the early prediction of the blockbuster success of TV Drama is very important from the strategic perspective of the media industry. Previous studies have tried to predict the audience ratings and success of drama based on various methods. However, most of the studies have made simple predictions using intuitive methods such as the main actor and time zone. These studies have limitations in predicting. In this study, we propose a model for predicting the popularity of drama by analyzing the customer's viewing pattern based on various theories. This is not only a theoretical contribution but also has a contribution from the practical point of view that can be used in actual broadcasting companies. In this study, we collected data of 280 TV mini-series dramas, broadcasted over the terrestrial channels for 10 years from 2003 to 2012. From the data, we selected the most highly ranked and the least highly ranked 45 TV drama and analyzed the viewing patterns of them by 11-step. The various assumptions and conditions for modeling are based on existing studies, or by the opinions of actual broadcasters and by data mining techniques. Then, we developed a prediction model by measuring the viewing-time distance (difference) using Euclidean and Correlation method, which is termed in our study similarity (the sum of distance). Through the similarity measure, we predicted the success of dramas from the viewer's initial viewing-time pattern distribution using 1~5 episodes. In order to confirm that the model is shaken according to the measurement method, various distance measurement methods were applied and the model was checked for its dryness. And when the model was established, we could make a more predictive model using a grid search. Furthermore, we classified the viewers who had watched TV drama more than 70% of the total airtime as the "passionate viewer" when a new drama is broadcasted. Then we compared the drama's passionate viewer percentage the most highly ranked and the least highly ranked dramas. So that we can determine the possibility of blockbuster TV mini-series. We find that the initial viewing-time pattern is the key factor for the prediction of blockbuster dramas. From our model, block-buster dramas were correctly classified with the 75.47% accuracy with the initial viewing-time pattern analysis. This paper shows high prediction rate while suggesting audience rating method different from existing ones. Currently, broadcasters rely heavily on some famous actors called so-called star systems, so they are in more severe competition than ever due to rising production costs of broadcasting programs, long-term recession, aggressive investment in comprehensive programming channels and large corporations. Everyone is in a financially difficult situation. The basic revenue model of these broadcasters is advertising, and the execution of advertising is based on audience rating as a basic index. In the drama, there is uncertainty in the drama market that it is difficult to forecast the demand due to the nature of the commodity, while the drama market has a high financial contribution in the success of various contents of the broadcasting company. Therefore, to minimize the risk of failure. Thus, by analyzing the distribution of the first-time viewing time, it can be a practical help to establish a response strategy (organization/ marketing/story change, etc.) of the related company. Also, in this paper, we found that the behavior of the audience is crucial to the success of the program. In this paper, we define TV viewing as a measure of how enthusiastically watching TV is watched. We can predict the success of the program successfully by calculating the loyalty of the customer with the hot blood. This way of calculating loyalty can also be used to calculate loyalty to various platforms. It can also be used for marketing programs such as highlights, script previews, making movies, characters, games, and other marketing projects.

TV 드라마는 타 장르에 비해 시청률과 채널 홍보 효과가 매우 크며, 한류를 통해 산업적 효과와 문화적 영향력을 확인시켜줬다. 따라서, 이와 같은 드라마의 흥행 여부를 예측하는 일은 방송 관련 산업에서 매우 중요한 부분임은 주지의 사실이다. 이를 위해서 본 연구에서는 2003년부터 2012년까지 10년간, 지상파 채널을 통해 방송된, 총 280개의 TV 미니시리즈 드라마를 분석하였다. 이들 드라마 중 평균 시청률 상위 45개, 하위 시청률 45개를 선정하여 흥행 드라마의 시청시간 분포 (5%~100%, 11-Step) 모형을 만들었다. 이들 기준 모형과 신규 드라마의 시청시간 분포와의 이격 거리를 Euclidean/Correlation으로 측정한 유사도(Similarity)를 통해, 시청자의 초기(1~5회) 시청시간 분포로 신규 드라마의 성패 여부를 예측하는 모델을 만들었다. 또한 총 방송 시간 중 70% 이상 시청한 시청자를 열혈 시청층(이하 열혈층) 으로 분류하고, 상위/하위 드라마의 평균값과 비교하여, 신규 드라마의 흥행여부를 판별할 수 있도록 설계하였다. 연구 결과 드라마의 초반 시청자 충성도(시청시간)는 드라마의 대흥행 여부를 예측하는데 중요한 요소임을 밝혔으며, 최대 75.47%의 확률로 대흥행 드라마의 탄생을 예측할 수 있었다.

Keywords

JJSHBB_2018_v24n4_33_f0001.png 이미지

Distribution of viewing time of the top 10 programs with average ratings.

JJSHBB_2018_v24n4_33_f0002.png 이미지

Cross validation

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Loyalty distribution of Jumong

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Loyalty distribution of Jumong

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Loyalty distribution of Thornbird

Results of the analysis

JJSHBB_2018_v24n4_33_t0001.png 이미지

References

  1. Adeniyi, D., Z. Wei, and Y. Yongquan, "Automated Web Usage Data Mining and Recommendation System Using K-Nearest Neighbor (Knn) Classification Method," Applied Computing and Informatics, Vol.12, No.1(2016), 90-108. https://doi.org/10.1016/j.aci.2014.10.001
  2. Ahn, J., K. Ma, O. Lee, and S. Sura, "Do Big Data Support Tv Viewing Rate Forecasting? A Case Study of a Korean Tv Drama," Information Systems Frontiers, Vol.19, No.2(2017), 411-420. https://doi.org/10.1007/s10796-016-9659-5
  3. Bae, J., "An Analysis on the Factors in Drama Ratings - Focusing on the Drama Attributes and Audience Factors," Korean Association for Broadcasting & Telecommunication Studies, Vol.19, No.2(2005), 270-309.
  4. Corporation, B. B., Building Public Value: Renewing the Bbc for a Digital World. BBC, 2004.
  5. Dick, A. S., and K. Basu, "Customer Loyalty: Toward an Integrated Conceptual Framework," Journal of the academy of marketing science, Vol.22, No.2(1994), 99-113. https://doi.org/10.1177/0092070394222001
  6. González, M., C. Bergmeir, I. Triguero, Y. Rodríguez, and J. M. Benítez, "On the Stopping Criteria for K-Nearest Neighbor in Positive Unlabeled Time Series Classification Problems," Information Sciences, Vol.328 (2016), 42-59. https://doi.org/10.1016/j.ins.2015.07.061
  7. Hu, L.-Y., M.-W. Huang, S.-W. Ke, and C.-F. Tsai, "The Distance Function Effect on K-Nearest Neighbor Classification for Medical Datasets," SpringerPlus, Vol.5, No.1(2016), 1304-1312. https://doi.org/10.1186/s40064-016-2941-7
  8. Jeong, Y., and J. Shin, "Communication Industry Report." Korea Communications Commission, 2013.
  9. Kotler, P., K. L. Keller, M. Brady, M. Goodman, and T. Hanser, Marketing Management, Harlow: Pearson Education, 2016.
  10. Kim, K., "In Search of Blockbuster Drama: The Effects of Genre Familarity, Star Power, and Internet Accounts on Rating Performance," Journal of Strategic Management, Vol.12, No.1 (2009): 25-54. https://doi.org/10.17786/jsm.2009.12.1.002
  11. Kwon, H., W. Park, S. Yoon, and S. Kim, "Analysis of Success Factor on Tv Drama," KOCCA Research Report, 2009.
  12. Lee, W., N. Lee, and J. Kim, "An Empirical Study on Forecasting Model of Popularity Rating for Drama Programs," Journal of Digital Contents Society, Vol.13, No.3(2012), 325-334. https://doi.org/10.9728/dcs.2012.13.3.325
  13. McNames, J., "A Nearest Trajectory Strategy for Time Series Prediction," Proceedings of the International Workshop on Advanced Black-Box Techniques for Nonlinear Modeling: KU Leuven Belgium, 1998.
  14. Pareto, V., and A. N. Page, "Manual of political economy," AM Kelley, 1971.
  15. Park, J., and H. Jeon, "What Did They Read in the Newspapers? - a New Method of Measuring Readership," Korean Association For Communication and Information Studies, Vol.35(2006), 211-249.
  16. Pedro, H. T., and C. F. Coimbra, "Nearest-Neighbor Methodology for Prediction of Intra-Hour Global Horizontal and Direct Normal Irradiances," Renewable Energy, Vol.80(2015), 770-782. https://doi.org/10.1016/j.renene.2015.02.061
  17. Reinartz, W. J., and V. Kumar, "The Impact of Customer Relationship Characteristics on Profitable Lifetime Duration," Journal of marketing, Vol.67, No.1(2003), 77-99. https://doi.org/10.1509/jmkg.67.1.77.18589
  18. Ryoo, W., "Globalization, or the Logic of Cultural Hybridization: The Case of the Korean Wave," Asian Journal of Communication, Vol.19, No.2(2009), 137-151. https://doi.org/10.1080/01292980902826427
  19. Sabavala, D. J., and D. G. Morrison, "A Model of Tv Show Loyalty," Journal of Advertising Research, Vol.17, No.6(1977), 35-43.
  20. Singh, S. S., D. C. Jain, and T. V. Krishnan, "Research Note-Customer Loyalty Programs: Are They Profitable?," Management science, Vol.54, No.6(2008), 1205-1211. https://doi.org/10.1287/mnsc.1070.0847
  21. Kim, T., and H. Ahn, "A Hybrid under-Sampling Approach for Better Bankruptcy Prediction," Journal of Intelligence and Information Systems, Vol.21, No.2(2015), 173-190. https://doi.org/10.13088/jiis.2015.21.2.173
  22. Tebert, M., "Erfolg Durch Qualität. Programmcontrolling Beim Wdr Fernsehen," Media Perspektiven, Vol.2, No.2000(2000), 85-93.
  23. Wittebols, J. H, The Soap Opera Paradigm: Television Programming and Corporate Priorities. Rowman & Littlefield, 2004.
  24. Yakowitz, S., "Nearest‐Neighbour Methods for Time Series Analysis," Journal of time series analysis Vol.8, No.2(1987), 235-247. https://doi.org/10.1111/j.1467-9892.1987.tb00435.x
  25. Yu, B., X. Song, F. Guan, Z. Yang, and B. Yao, "K-Nearest Neighbor Model for Multiple-Time-Step Prediction of Short-Term Traffic Condition," Journal of Transportation Engineering, Vol.142, No.6(2016), 040160181-10.
  26. Yu, S., and S. Kim, "An Analysis of Factors Which Affect the Rating of Drama ; Focusing on the Production Factors of Outsourcing Drama," Media economy and culture, Vol.8, No.3(2010), 7-48.
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