Impact of ratings of content on OTT platforms and prediction of its success rate
- Published: 29 May 2023
- Volume 83 , pages 4791–4808, ( 2024 )
Cite this article
- Krishna Kumar Singh ORCID: orcid.org/0000-0003-3849-5945 1 ,
- Jeroz Makhania 1 &
- Madhumita Mahapatra 2
275 Accesses
1 Altmetric
Explore all metrics
A Correction to this article was published on 20 December 2023
This article has been updated
The world of media and entertainment is evolving significantly. The popularity of newly released movies or TV shows significantly depends on public opinion. The general audience and critics have different ways of expressing their views, and one such method is rating a film or a TV show on platforms such as IMDb or Rotten Tomatoes. With the inception of Over-The-Top (OTT) platforms and their releasing new content on a routine basis, they must garner good ratings from the public to retain their audience. This study uses random forest, k-nearest neighbors, and logistic regression models to predict the IMDb and Rotten Tomatoes ratings. The outcome of these ratings can also be used to predict further the amount a film might gross. Linear Regression predicts the inflation-adjusted amount grossed by a particular film based on its IMDb ratings. After implementing algorithms, KNN, Random Forest, and logistic Regression have 89%, 91%, and 86% accuracy. The main results reveal that the random forest model gives the best accuracy for predicting IMDb and Rotten Tomatoes ratings. Linear Regression also shows promising results for predicting the inflation-adjusted amount grossed by a particular film. With the help of this methodology, OTT platforms will be able to see the impact of content on the viewers using this result for various means like designing new content, increasing profit by showing the most demanding content, etc. Finally, this study offers some clues to OTT platforms about how users might react to new content released. The results can also give an economic advantage to OTT platforms.
This is a preview of subscription content, log in via an institution to check access.
Access this article
Price includes VAT (Russian Federation)
Instant access to the full article PDF.
Rent this article via DeepDyve
Institutional subscriptions
Similar content being viewed by others
Machine Learning Applications on Box-Office Revenue Forecasting: The Taiwanese Film Market Case Study
Predicting annus mirabilis with machine learning: Turkish movie industry
Kamil Topal, Ali Can Günhan & G. Baris Bagci
Revisiting predictions of movie economic success: random Forest applied to profits
Thaís Luiza Donega e Souza, Marislei Nishijima & Ricardo Pires
Data availability
All the data is collected from the simulation reports of the software and tools used by the authors. Authors are working on implementing the same using accurate world data with appropriate permissions.
Change history
20 december 2023.
A Correction to this paper has been published: https://doi.org/10.1007/s11042-023-17900-7
Abu Amra IA, Maghari AYA (2017) Students performance prediction using KNN and Naïve Bayesian. 8th International Conference on Information Technology
Bagkar P, Borude A, Aga Z (2021) Sentiment analysis on Netflix. Iconic Res Eng J 4(11):121–126
Bayramli I, Castro V, Barak-Corren Y, Madsen EM, Nock MK, Smoller JW, Reis BY (2022) Predictive structured–unstructured interactions in EHR models: a case study of suicide prediction. Digit Med 5:15
Article Google Scholar
Chen X, Wu Q-F, Yan G-Y (2017) RKNNMDA: ranking-based KNN for MiRNA-disease association prediction. RNA Biol 14:952–962
Daniel D (2021) Disney plus: launch and beyond. The Faculty of the College of Business & Economics, California State University, Los Angeles
Google Scholar
Francoeur B (2004) Brand image and walt disney: a qualitative analysis of “magical gatherings”. J Undergrad Res 1(1):1–66
Gururaj V, Ray SV, Kay A (2019) Stock market prediction using linear regression and support vector machines. Int J Appl Eng Res 14:1931–1934
Havard CT (2021) Disney, Netflix, and Amazon Oh My! An analysis of streaming brand competition and the impact on the future of consumer entertainment. Findings in sport, hospitality, entertainment, and event management
Huang W, Ying TW, Chin WLC, Baskaran L, Marcus OEH, Yeo KK, Kiong NS (2022) Application of ensemble machine learning algorithms for cardiovascular risk prediction. Nature Portfolio Scientific Reports, Complexity 1:1–10. https://doi.org/10.1155/2021/6663455
Keerthi Kumar HM, Harish BS, Darshan HK (2018) Sentiment analysis on IMDb movie reviews using hybrid feature extraction method. Int J Interact Multimed Artif Intell 5:109–114
Kouloumpis E, Wilson T, Moore JD (2011) Twitter sentiment analysis: the good, the bad, and the OMG! Fifth international AAAI conference on weblogs and social media
Lee K, Ayyasamy MV, Delsa P, Hartnett TQ, Balachandran PV (2022) Phase classification of multi-principal element alloys via interpretable machine learning. Comput Mater 8:25
Mahapatra M, Singh KK (2022) Prediction of causes and effects of obesity in India by supervise learning approaches. Obes Med 34:100436. https://doi.org/10.1016/j.obmed.2022.100436 . ISSN 2451-8476
Moochhala Q (2018) The future of online OTT entertainment services in India, vol 1, issue 1, pp 2–25. https://actionesque.com/research-papers_pdf/QM-OTT-future-2018.pdf
Mood C (2009) Logistic regression: why we logistic regression: why we can do, and what we can do about it. Eur Sociol Rev 26(1):67–82 (February 2010)
Nanehkaran FH, Lajevardi SM, Bidgholi MM (2022) Nearest neighbors algorithm and genetic-based collaborative filtering. Concurrency and computation: practice and experience
Park E-A (2017) Why the networks cannot beat Netflix: speculations on the US OTT Services Market. Digit Policy Regul Gov 19:21–39
Pritam N et al (2019) Assessment of code smell for predicting class change proneness using machine learning. IEEE Access 7:37414–37425. https://doi.org/10.1109/ACCESS.2019.2905133
Putri DA (2021) Customer satisfaction with disney plus hotstar streaming service in Indonesia: the impact of the user experience. Faculty of Economics and Business, Universitas Mercu Buana, Jakarta
Singh KK (2023) Study of early risks of depression by analysing social media posts. IIMS J Manag Sci 14(01):9–25
Singh T, Kumari M (2016) Role of text pre-processing in Twitter sentiment analysis. Twelfth international multi-conference on information processing
Sperande S (2014) Understanding logistic regression analysis. Biochem Med 24:12–8
Vadloori KB, Sanghishetty SM (2021) Exploratory and sentiment analysis of Netflix data. Int J Eng Res Technol 10:9 (September 2021)
Vimal S, Khari M, Dey N, Crespo RG, Robinson YH (2020) Enhanced resource allocation in mobile edge computing using reinforcement learning based MOACO algorithm for IIOT. Comput Commun 151:355–364. https://doi.org/10.1016/j.comcom.2020.01.018 . ISSN 0140-3664
Yoo B, Donthu N, Pilling BK (1998) Channel efficiency: franchise vs. non-franchise systems. J Mark Chennals 6(3/4):1–16
Zhang L, Liu Q, Yang W, Wei N, Dong D (2013) An improved k-nearest neighbor model for short-term traffic flow prediction. 13th COTA international conference of transportation professionals
Download references
Author information
Authors and affiliations.
Symbiosis Centre for Information Technology, Pune, India
Krishna Kumar Singh & Jeroz Makhania
Delhi Technical Campus, Greater Noida, UP, India
Madhumita Mahapatra
You can also search for this author in PubMed Google Scholar
Corresponding author
Correspondence to Krishna Kumar Singh .
Ethics declarations
Conflict of interest.
The authors declare that they have no conflict of interest.
Additional information
Publisher's note.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The original online version of this article was revised: The original publication of this article contains an error in the second affiliation.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Reprints and permissions
About this article
Singh, K.K., Makhania, J. & Mahapatra, M. Impact of ratings of content on OTT platforms and prediction of its success rate. Multimed Tools Appl 83 , 4791–4808 (2024). https://doi.org/10.1007/s11042-023-15887-9
Download citation
Received : 27 January 2023
Revised : 21 April 2023
Accepted : 22 May 2023
Published : 29 May 2023
Issue Date : January 2024
DOI : https://doi.org/10.1007/s11042-023-15887-9
Share this article
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative
- Random forest
- Find a journal
- Publish with us
- Track your research
To read this content please select one of the options below:
Please note you do not have access to teaching notes, ott platforms resilience to covid-19 – a study of business strategies and consumer media consumption in india.
International Journal of Organizational Analysis
ISSN : 1934-8835
Article publication date: 26 April 2022
Issue publication date: 13 January 2023
COVID-19 preventive measures disrupted the media and entertainment business ecosystem, increased over the top (OTT) consumption, brought new OTT players, thus increased competition, and shaped consumer behaviour and habits. Despite this knowledge, in-depth insights into OTT's consumer behaviour, new usage habit and strategies used by subscription-based OTT platforms to maintain resilience during the COVID-19 pandemic are unknown. This paper aims to fill the two gaps in the extant OTT literature.
Design/methodology/approach
This study used Eisenhardt's multiple case studies approach to derive the strategies used by the top-performing subscription-based OTT platforms in India. Moreover, a purposive semi-structured Google survey was used to explore consumers' OTT experience during the pandemic. This study analysed data using NVivo 12 (survey) and MS Excel 2010 (case studies).
This study derived seven resilient OTT strategies; competitive low pricing, enhancing customer experience, launching innovative service plans, content localisation, strategic collaboration, flexibility in technology adoption and proactive sales promotion. Consequent to adopting these strategies, consumers' usage of OTT evolved from occasional to habitual. Convenience, ease of accessibility, risk of contracting COVID-19, variety and quality of content, online reviews and affordability drive consumer preference for OTT. Also, this study revealed consumers' varied OTT experiences.
Originality/value
The contribution is two-fold; the derived strategies for maintaining resilience and the in-depth insights into habit formation and consumer behaviour during and after the COVID-19 pandemic. This study is valuable for media and entertainment stakeholders like streaming service providers, OTT services, cable operators, etc.
- Consumer behaviour
- Business strategy
- COVID-19 resilience
- OTT platforms
- Situational factors
Acknowledgements
Funding : This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Sharma, K. and Lulandala, E.E. (2023), "OTT platforms resilience to COVID-19 – a study of business strategies and consumer media consumption in India", International Journal of Organizational Analysis , Vol. 31 No. 1, pp. 63-90. https://doi.org/10.1108/IJOA-06-2021-2816
Emerald Publishing Limited
Copyright © 2022, Emerald Publishing Limited
Related articles
We’re listening — tell us what you think, something didn’t work….
Report bugs here
All feedback is valuable
Please share your general feedback
Join us on our journey
Platform update page.
Visit emeraldpublishing.com/platformupdate to discover the latest news and updates
Questions & More Information
Answers to the most commonly asked questions here
IMAGES
VIDEO
COMMENTS
Mohammed A. Al-Sharafi. PDF | On Aug 15, 2016, Md. Motaleb Hossain published A Literature Review of OTT- related Policy and Regulatory Issues | Find, read and cite all the research you need on ...
Accordingly, a review of current literature spanning 2007 to 2021 identified twelve factors influencing the adoption of OTT platforms. This literature review is the first one in the domain of over-the-top media to identify the influencing factors across the streaming platforms.
LITERATURE REVIEW Saha, Sumitra et al.(2021) noted that entertainment is the main driver on digital platforms that ... OTT platform and the other one that resembles relationship or commonality in features between the OTT platforms. As far as importance of features is concerned in Figure-3, the wide variety of content ...
This study is a thematic literature review undertaken to identify and highlight key themes related to the adoption and usage of OTT entertainment services. It includes a thorough literature review ...
The findings showed that the model reasonably explained the respondents' acceptance of OTT. More specifically, the attitude towards technology is determined by performance expectancy and effort expectancy while the attitude towards technology, facilitating conditions and social influence positively affect the intention to adopt OTT.
As a result, popular OTT service providers such as YouTube, Netflix and Spotify have seen an instrumental role in the growth of data streaming, recording a staggering 140% rise in video streaming apps in Australia, India, Indonesia, South Korea and Thailand (App Annie, The state of the mobile 2019).These statistics show that there exists a strong opportunity for OTT service providers to ...
OTT platforms are further divided into subscription-based services (SVOD), free and ad-supported services (AVOD) and transactional services (TVOD) which let the user pay for the content. ... 2 Literature Survey. ... A review conducted by Lab42 research shows that viewers split between Netflix, Amazon Prime Video and Hulu. ...
Overall, the literature suggests that price is a critical factor that impacts the adoption, usage, and satisfaction of OTT platforms. OTT platform providers need to carefully consider their pricing strategies to attract and retain users while also ensuring that the pricing is perceived as fair by their users.
The shift from conventional media to over-the-top (OTT) media, particularly in the lockdown period due to the COVID-19, has resulted in a war between streaming service providers to attract and retain customers. In the light of this change, the present study conducts partial least squares structural equation modeling (PLS-SEM) analysis to ...
The world of media and entertainment is evolving significantly. The popularity of newly released movies or TV shows significantly depends on public opinion. The general audience and critics have different ways of expressing their views, and one such method is rating a film or a TV show on platforms such as IMDb or Rotten Tomatoes. With the inception of Over-The-Top (OTT) platforms and their ...
120. ISSN 2394 - 7780. A STUDY ON CONSUMER BEHAVIOUR TOWARDS OTT PLATFORMS IN INDIA DURING. COVID ERA. Bhavyarajsinh D. Jhala and Vivek B. Patadiya. Assistant Professor, School of Management, RK ...
This paper aims to fill the two gaps in the extant OTT literature.,This study used Eisenhardt's multiple case studies approach to derive the strategies used by the top-performing subscription-based OTT platforms in India. Moreover, a purposive semi-structured Google survey was used to explore consumers' OTT experience during the pandemic.
However, the developed principles and heuristics are specific to TV-related services, and not OTT platforms. Our literature review reveals that only the study by Jang and Yi developed a user experience measurement scale designed for smart TVs. To our knowledge, there has not been a usability measurement scale explicitly created for OTT platforms.
But OTT platforms are buzzing with activity. Especially after the lockdown in India, with the OTT channel gaining such a large number of viewers, people have gained a ... To examine the factors influencing to prefer OTT platform. LITERATURE REVIEW Meghan McAdams 2019, "What is OTT- Understanding the Modern Media Streaming Landscape". The
Platforms before Covid-19. Customer‟s satisfaction level is very high towards OTT Platforms. The factors that influence the customers to watch OTT is it can be watched anytime, anywhere. Keywords: customer preference, OTT platform theatres, digital media platform, 1. INTRODUCTION The acronym OTT stands for OVER-THE-TOP MEDIA SERVICE.
6) As a finding of my research objective found that 28% people agree that there is change. in their consumption pattern of OTT platform due to lockdown. 44.7% people think. that there is incr ease ...
Next objectives is to identify the relation between times spend on OTT platform and number of OTT platform subscribed here Pearson Correlation tool is used on R software. There is low degree of positive correlation between these two variables. Last objective is to understand the preference of different occupation group towards different OTT ...
including the movie business. This study investigates how OTT platforms are affecting the movie business, with a particular emphasis on moviegoers. This study discovered that the way audiences consume content has significantly changed as a result of OTT platforms through a review of the literature and interviews with industry experts.
group adds 1.8% to the OTT platform viewership in In dia. AltBalaji scored highest among 25-35 age. group users, and ErosNow has the largest share in the age group of 25-39. In 2019, a share of 48 ...
The study shows that 2/3rd.of the respondents are aware of OTT Platforms before Covid-19, most people prefer Amazon Prime & the least preferred is MX Player. Most people use Mobiles for watching OTT & Comedy is the genre they prefer. Customers‟ satisfaction level is very high towards OTT Platforms.
On the other hand, among the same set of respondents, 13.2% watch a film every day, 38.5% watch every week and 40.8% every month on an OTT platform. 51.3% of the youth who responded to the survey ...
Through the review, this article sets a research agenda for platforms in emerging markets. Four elements are specifically analyzed. First, the definition of multisided platforms; second, technology and behavioral enablers; third, the emerging business models; and, fourth, platform dynamics.
The top media platform is a streaming media service offered directly to viewers via the internet.It bypasses cable, broadcast, and satellite television platforms. The players among the OTT ...