The Adoption of Analytics in Handling Netflix’s Business Challenges
DOI:
https://doi.org/10.58776/ijitcsa.v3i3.222Keywords:
Text Analytics, Web Analytics, Social Media Analysis, Geospatial Analysis, SegmentationAbstract
Text analytics is a combination of various kinds of text analysis that combines a set of linguistic, statistical and machine learning that extract the meaning out of the text. For example, the analyzing of customer reviews written by Netflix’s customers can be used to find out common patterns and trends that happen around the business for better action-taking, customer experience, and new business models to be built in the future. By doing text analytics, the text data can be grouped with the purposes of creating word frequency distribution (word cloud) and sentiment analysis. Whereas web analytics is referring to the process of collecting data from website through data crawling before processing it into useful info to improve the website experience by using social media analytics (Saini et al., 2022). To find out ways to aid in the formulation of marketing strategies, clustering techniques have been used. The clustering techniques will include behavioral segmentation that focus on human behavior, demographic segmentation that focuses on age, and occupation, and psychographic segmentation that focus on human’s psychological characteristics such as interest, personalities, and so on.
References
. C. A. Gomez-Uribe and N. Hunt, “The Netflix Recommender System: Algorithms, Business Value, and Innovation,” ACM Trans. Manag. Inf. Syst., 2015, doi: 10.1145/2843948.
. H. Lotz, “Portable TV: Netflix and Mobility,” Television & New Media, 2017, doi: 10.1177/1527476417708244.
. A. Tryon, “TV Got Better: Netflix’s Original Programming Strategies,” Cinema Journal, 2015, doi: 10.1353/cj.2015.0005.
. M. Wayne, “Netflix, Amazon, and Branded Television Content in the SVOD Era,” Media, Culture & Society, 2018, doi: 10.1177/0163443718754650.
. D. Susser, E. Roessler and H. Nissenbaum, “Online Manipulation: Hidden Influences in the Streaming Economy,” Journal of Ethics and Information Technology, 2019, doi: 10.1007/s10676-019-09517-5.
. T. M. Cunningham and D. E. Eastin, “Second Screening and Connected Viewing Practices,” Journal of Broadcasting & Electronic Media, 2017, doi: 10.1080/08838151.2017.1350751.
. S. Pittman and T. Tefertiller, “Facebook, Netflix, and Entertainment Choice,” Journal of Broadcasting & Electronic Media, 2015, doi: 10.1080/08838151.2015.1029122.
. P. Jenner, “Is This TVIV? The Rise of Netflix and SVOD,” Critical Studies in Television, 2018, doi: 10.1177/1749602018758698.
. J. Matrix, “The Netflix Discovery Problem,” International Journal of Communication, 2014, doi: 10.5167/uzh-101424 (DOI resolves through Handle → DOI).
. M. Lobato, “Rethinking International TV Flows in the Age of Netflix,” Television & New Media, 2019, doi: 10.1177/1527476419834517.
. S. Gopalakrishnan and B. Reagle, “Subscriber Churn in OTT Platforms: Retention Dynamics,” Telecommunications Policy, 2020, doi: 10.1016/j.telpol.2020.102017.
. H. Shao et al., “Predicting Customer Churn in Subscription Services,” Expert Systems with Applications, 2019, doi: 10.1016/j.eswa.2019.07.017.
. H. Idris et al., “Churn Prediction in Telecom Using ML,” Journal of King Saud University – Computer and Information Sciences, 2019, doi: 10.1016/j.jksuci.2016.10.007.
. V. Vafeiadis et al., “A Comparison of Machine Learning Techniques for Customer Churn Prediction,” Simulation Modelling Practice and Theory, 2015, doi: 10.1016/j.simpat.2015.04.001.
. S. Amin et al., “Customer Churn Prediction in the Telecommunication Sector Using Rough Set Theory,” Expert Systems with Applications, 2019, doi: 10.1016/j.eswa.2019.01.029.
. A. Idris and A. Khan, “Churn Prediction Using Neural Networks,” Applied Soft Computing, 2012, doi: 10.1016/j.asoc.2011.12.021.
. J. Verbeke et al., “Customer Churn Prediction with Decision Rules,” Expert Systems with Applications, 2012, doi: 10.1016/j.eswa.2011.09.076.
. S. Coussement and D. Van den Poel, “Churn Prediction in the Online Industry,” Data Mining and Knowledge Discovery, 2008, doi: 10.1007/s10618-008-0095-5.
. T. Neslin et al., “Defection Detection: Measuring Customer Churn,” Marketing Science, 2006, doi: 10.1287/mksc.1050.0198.
. G. F. Hughes, “Predictive Analytics for Customer Retention,” International Journal of Forecasting, 2014, doi: 10.1016/j.ijforecast.2013.07.006.
. M. Wedel and P. Kannan, “Marketing Analytics for Data-Rich Environments,” Journal of Marketing, 2016, doi: 10.1509/jm.15.0413.
. G. L. Urban et al., “Digital Advertising Effectiveness,” Journal of Advertising Research, 2014, doi: 10.2501/JAR-53-2-180-190.
. C. E. Tucker, “Social Networks, Personalized Advertising,” Marketing Science, 2014, doi: 10.1287/mksc.2013.0805.
. A. M. Kaplan and M. Haenlein, “Users of the World, Unite! The Challenges of Social Media,” Business Horizons, 2010, doi: 10.1016/j.bushor.2009.09.003.
. J. Y. M. Nip and B. Berthelier, “Social Media Sentiment Analysis,” Encyclopedia, 2024, doi: 10.3390/encyclopedia4040104.
. W. H. Hsiao et al., “Understanding Social Media Engagement Metrics,” Information & Management, 2017, doi: 10.1016/j.im.2016.11.005.
. X. Zhang et al., “Mining Online Reviews for Marketing Insights,” Decision Support Systems, 2019, doi: 10.1016/j.dss.2019.04.002.
. A. Giachanou and F. Crestani, “Opinion Mining and Sentiment Analysis of Social Media,” ACM Computing Surveys, 2016, doi: 10.1145/2931664.
. Y. Kim and S. Park, “Digital Platform Competition and User Behavior,” Journal of Business Research, 2021, doi: 10.1016/j.jbusres.2020.10.005.
. S. Mei and X. Zhan, “Social Media Monitoring for Market Intelligence,” Decision Support Systems, 2016, doi: 10.1016/j.dss.2016.02.002.
. B. Pang and L. Lee, “Opinion Mining and Sentiment Analysis,” Foundations and Trends in Information Retrieval, 2008, doi: 10.1561/1500000011.
. P. D. Turney, “Thumbs Up or Thumbs Down?: Semantic Orientation Applied to Unsupervised Classification,” ACL, 2002, doi: 10.3115/1073083.1073153.
. Z. Zhang et al., “Deep Learning for Text Understanding,” IEEE Access, 2021, doi: 10.1109/ACCESS.2021.3052812.
. C. C. Aggarwal and C. Zhai, “A Survey of Text Classification Algorithms,” Mining Text Data, 2012, doi: 10.1007/978-1-4614-3223-4_6.
. S. Debortoli et al., “Text Mining for Business Analytics,” Business & Information Systems Engineering, 2016, doi: 10.1007/s12599-016-0433-8.
. A. K. Uysal and M. Gunal, “Sentiment Classification Using TF–IDF and ML,” Expert Systems with Applications, 2014, doi: 10.1016/j.eswa.2014.03.029.
. A. Severyn and A. Moschitti, “Twitter Sentiment with Deep Convolutional Neural Networks,” SIGIR, 2015, doi: 10.1145/2766462.2767830.
. Q. Chen et al., “Neural Sentiment Classification with User and Product Information,” EMNLP, 2016, doi: 10.18653/v1/D16-1171.
. M. A. Hearst, “Untangling Text Data Mining,” ACL, 1999, doi: 10.3115/1034678.1034679.
. J. Han, J. Pei and M. Kamber, “Data Mining: Concepts and Techniques,” Morgan Kaufmann, 2011, doi: 10.1016/C2009-0-61819-5.
. J. P. Walsh and G. R. Ungson, “Organizational Memory,” Academy of Management Review, 1991, doi: 10.5465/amr.1991.4278992.
. I. Nonaka, “A Dynamic Theory of Organizational Knowledge Creation,” Organization Science, 1994, doi: 10.1287/orsc.5.1.14.
. M. Huber, “Organizational Learning,” Organization Science, 1991, doi: 10.1287/orsc.2.1.88.
. A. Halevy, A. Rajaraman and J. Ordille, “Data Integration: The Teenage Years,” VLDB, 2006, doi: 10.14778/2335472.2335478.
. L. Getoor and A. Machanavajjhala, “Entity Resolution in Big Data,” Synthesis Lectures on Data Mining and Knowledge Discovery, 2013, doi: 10.2200/S00433ED1V01Y201311DMK008.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Amani Bunga Cahya, Orked Pavitra, Dharma Fahim Jamal

This work is licensed under a Creative Commons Attribution 4.0 International License.
Attribution 4.0 International
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.


