How Netflix Uses Digital Data to Provide Personalized Services

As of January 2019, Netflix has reached 139 million paying subscribers globally (Fiegerman, 2019). With the large user base, Netflix has the possibilities to collect enormous amount of digital data regarding user behaviors and preferences, in order to optimize their decision on what services and contents to provide and recommend to the users. For example, Netflix can better understand why users drop from TV series and thus develop strategies to promote target behaviors among users (Bulygo, n.d.). As assumed by Bulygo (n.d.), this can be achieved by first getting an overview about the overall rate of completing the whole season, as well as at which point users commonly stop watching the series. After acquiring general ideas about the current situation, Netflix can also track specific user behaviors like time to start watching, pause point and so on to make informed decisions about where adjustments are necessary to make users follow their content, as well as provide personalized recommendations at individual levels (Bulygo, n.d.) and identify user categories like “at-risk” users (DeAsi, n.d.).

As the rapid development of cloud computing, it is not always clear what the user-generated and system-generated data are used for, which is exactly the situation Netflix’s users in the previous case are facing (van den Hoven et al., 2018). Recently, social media providers are gathering and exploiting vast amounts of personal data without the consent of the individuals(Wigan & Clarke, 2013). The way Netflix monitoring and analyzing users’ behavioral data to make better recommendation and other service improvements is obviously without the consent from its users, or even awareness for typical uses.

Due to the increasing privacy concern, the European Union’s General Data Protection Regulation (hereafter: GDPR) was published last year to protect consumers’ privacy and give them greater control over how their data is collected and used by requiring marketers to secure explicit permission for data-use activities within the EU. The behavioral data collection isn’t going to disappear entirely, however, it will keep going most likely with aggressively enforced transparency and consumer oversight. (Ghosh, 2018). Till the GDPR’s effectiveness and benefits seen by all players, there might be bigger improvement in normative consideration and better privacy regulation.

There is no doubt that digital analytics is of great significance to the companies in the digital age. It can help companies to improve decision making, which is of direct interest to business (Provost & Fawcett, 2013). By collecting and analyzing a large amount of users’ data, Netflix can know more about audiences and make better decisions for the future. Brynjolfsson, Hitt and Kim (2011) also found the more data-driven a firm is the more productive it is. The use of digital data not only can help analysts analyze more data faster to increase personal productivity, but also can affect firm performance. In addition, digital analytics can promote innovation. The platform that Netflix offers personalized recommendations to meet each user’s needs makes television and cinema cannot compete. If companies can gather insights that other competitors don’t have, they can be in the lead with new products and services.

But obviously there might be a downside to having access to all this information which in this case is the over-reliance on this data (Horst & Duboff, 2015), that has brought Netflix to bad commercial decisions. With the huge amount of new original content that Netflix has produced from 2015 onwards due to a policy of priming quantity over quality, and the intention of satisfying everyone, the company was pushed to invest ridiculous amounts of money to produce shows that weren’t that successful. As FX president John Landgraf said “Television shows are not like cars or operating systems, and they are not best made by engineers or coders in the same assembly line manner as consumer products which need to be of uniform size, shape and quality” (Alexander, 2018).

Reference

Alexander, J. (2018, January 05). Netflix had very few wins and too many failures in 2017 [Blog Post]. Retrieved February 27, 2018, from https://www.polygon.com/2018/1/5/16850376/netflix-2017-bright-13-reasons-why-american-vandal-bojack

Brynjolfsson E., Hitt L.M., and Kim H.H. Strength in numbers: How does data-driven decision making affect firm performance? Working paper, 2011.

Bulygo, Z. (n.d.). How Netflix Uses Analytics To Select Movies, Create Content, and Make Multimillion Dollar Decisions [Blog Post]. Retrieved from https://neilpatel.com/blog/how-netflix-uses-analytics/

DeAsi, G. (n.d.). How to Use Customer Behavior Data to Drive Revenue (Like Amazon, Netflix & Google) [Blog Post]. Retrieved from https://www.pointillist.com/blog/customer-behavior-data/

Fiegerman, S. (2019, January 18). Netflix adds 9 million paying subscribers, but stock falls. Retrieved from https://edition.cnn.com/2019/01/17/media/netflix-earnings-q4/index.html

Ghosh, D. (2018). How GDPR Will Transform Digital Marketing. Harvard Business Review. Retrieved from https://hbr.org/2018/05/how-gdpr-will-transform-digital-marketing

Horst, P., & Duboff, R. (2015). Don’t Let Big Data Bury Your Brand. Harvard Business Review (November 2015 Edition), 78-86.

Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. O’Reilly Media, Inc.

van den Hoven, Jeroen, Blaauw, Martijn, Pieters, Wolter and Warnier, Martijn, “Privacy and Information Technology”, The Stanford Encyclopedia of Philosophy (Summer 2018 Edition), Edward N. Zalta (ed.). Retrieved from https://plato.stanford.edu/archives/sum2018/entries/it-privacy/

Wigan, M. R., & Clarke, R. (2013). Big Data’s Big Unintended Consequences. Computer, 46(6), 46–53