& Big Data

[Audio] Hi everyone, welcome to week 3 discussion. This week's learning objective is to be more aware of how pervasive an influence ICT in business and in our culture in general. This week's presentation is prepared by Michael Cortez, Kyle Renwick, Manuel Quinones, and Vivian Pan..

[Audio] Netflix is an American digital content streaming and production company that was founded in 1997 by Reed Hastings and Marc Randolph. The company initially offered a mail-based DVD rental subscription service, but it introduced digital video streaming services in 2007. In 2016, the company separated its DVD service onto a new platform, known as DVD.com: A Netflix Company. The company then transitioned its main platform to subscription-based digital video streaming. The company is regarded as the world's largest subscription streaming service, with 2020 estimates suggesting that the company now has 193 million subscribers around the world. Netflix's recommendation system is an important contributor to its revenue generation model, driving approximately 80 percent of hours of content streamed on the platform. Unlike YouTube and Amazon, the platform does not deliver targeted advertisements to its users. Rather, the company relies on subscriptions to both its digital video streaming service and DVD-delivery service to generate revenue. As competition between video streaming platforms has heated up, the company has also invested heavily in producing original content. The platform's financial success relies on attracting and maintaining user attention, and preventing users from leaving Netflix in favor of competitors. Its recommendation engine is vital to achieving this, and it is therefore key to the company's business model. To stay competitive in the rivals, Netflix must utilize big data in order to better serve their customers and to plan strategically..

[Audio] With the advent of new technologies that allowed companies to begin collecting vast amounts of data on their consumers, the issue of processing this data in a timely manner to make it useful came to the forefront. In the past companies only had access to their own data from their consumers but now with a wide range of access to other public data (due to the internet), companies now can compare & contrast their data with public data to get even more accurate information. However, with the public data also into the equation, the sheer volume of data grows even larger. With all this data available to corporations, it is key to have fast & efficient means of processing the data & making it understandable & useful to a company's decision makers..

[Audio] Netflix uses data in a variety of ways to achieve their main business objectives. Netflix goals were to improve member retention, reduce cancellations, achieve long term fidelity, & satisfy their subscribers. Netflix is able to use data to assess the success of their acquired content rights. This is vital to Netflix, as licensed content is a large expense to acquire. Netflix can use data to determine if they should renew a certain rights deal at the end & use their data to determine which types of content they should acquire in the future based upon the tastes of their subscriber base (determined via data). Each Netflix user gets personalized recommendations based upon their history of watching with Netflix. This does a few things… for one, the longer you are a subscriber, the more tailored your recommendations get to you. This is a huge portion of Netflix's low churn rate ( 9%). In addition, Netflix knows that the average user will only spend about 60- 90 seconds deciding what they want to watch. Given this very short time window, Netflix needs to get something the use will like based upon historical data quickly to ensure the customer is satisfied with their experience & they enjoy what they are watching. House of Cards was a show that Netflix produced & invested serious money into before an episode ever aired (similar to Orange is the New Black). Netflix is able to invest in shows early & heavily, as they have such confidence, via their data, that they know what their customers will like. While this is not foolproof, more often than not Netflix's data has been right on whether or not a show will be successful..

[Audio] Netflix's data analysis systems is collecting information from every individual that uses its service as a method of figuring out what their next show will be. Since licenses often expire between media and their owners, a show with low viewership will be taken away to bring in popularity to help keep the user engaged. If shows especially Netflix originals prove to be successful, Netflix will continue the series or create spinoffs out of the source material. One thing to consider is that Netflix original series will not be taken off the service as the licensing is dealt with directly not with another firm that requires continuous negotiations to keep it on the service such as to why The Office went off Netflix and is now exclusively on NBC's service " Peacock". Ratings however are taken differently as even bad press on a show can reap giant viewership for Netflix however they would not wish to keep distributing badly rated content as then it would become flooded with content individuals wouldn't want to see, instead they may continue the series but change the series in additional seasons in an attempt to keep the view returnship.

[Audio] Considering that each individual that watches something on Netflix typically chooses a specific time of day to watch their content. Netflix records the individuals users time, date and location of a show being watched to recommend the same content to an individual that has similar watching patterns as another user. If someone would to record their Netflix feed daily at different times, they'd begin to see that there is a different watching pattern depending on the time of day it is as Netflix has begun to document what kind of content an individual wants and why. This data also aligns with the shows than an individual started and did not finish and where in the series the person left on in. Whether it being a particular scene or casting member than the individual felt distaste over, Netflix sees this data and begins to not show the viewer similar shows and cast members and also takes in consideration if there is a pattern between thousands of viewers when they develop casting for future programs and series. This also helps aid to " Continue Watching" as there are going to be consistent reminders such as the feed page and what's next page upon the conclusion of the end of the content being watched to get an individual to continue watching a show to get that engagement rating up..

[Audio] Netflix's growth is largely due to its ability to personalize content recommendations for users across the globe. To do so, Netflix collects data and uses algorithms to curate a personal experience for each user. Some of Netflix's data is built from information that users voluntarily provide, like their name, e-mail, address, payment method, telephone number, and content reviews..

[Audio] While users voluntarily provide personal information to Netflix, Netflix itself automatically collects other forms of data, such as the platform used to watch Netflix, user's IP address, user's watch history, search queries, and time spent watching a show. The company also collects some bits of data from other sources, such as demographic data, interest-based data, and Internet browsing behavior..

[Audio] The personalization of the Netflix experience is multi-faceted. For instance, the company personalizes images, text descriptions, tags, and trailers. It also considers how much content should be shown to users as they browse and adapts the size of the content's cover art. Additionally, it offers content recommendations specific to the watch history of the device. There are four modes Netflix uses to build recommendations. The continuation mode encourages the user to continue watching a TV show. The discovery mode helps a user find a new movie to watch. The list mode feature titles the user added to the " My List" section. Finally, the re-watch mode is set up to enable the user to view a previously watched title. This data helps provide valuable answers about what motivates users to watch some content versus other contents. This ethnographic research helps reshape the audiovisual structure as far as production and distribution is concerned..

[Audio] Netflix, like many other large firms, utilize AWS or Amazon Web Service as a cloud infrastructure. The sheer volume of data around Netflix is massive, both in terms of content, and raw data around consumer behavior. Monthly streaming volumes often near one billion hours. Data quality is huge, and Netflix employs an analytical layer all raw data must pass through to both ensure quality, and to categorize data so it can established into various KPIs. This is one of the key functions of their BI team, or Business Intelligence. BI enables the broader organization to make better decisions, through the use of data. Which is very fitting giving the CEO believes in the use of data to guide strategy. There is a massive amount of synergy around Netflix's use of data, algorithms, and Business Intelligence to deliver a truly custom product for end users..

[Audio] Netflix operates in over 190 countries and provides personalized experiences to users through big data management. The company has implemented a regionalization approach to data analysis, allowing algorithms to operate individually in each region while still processing data globally. This has allowed Netflix to identify viewer segments in different territories, personalize catalogs for users, and provide niche content for different regions. Yet, data analysis presents numerous challenges for Netflix as it continues to expand its operations internationally. One major challenge is the ability to accurately collect and analyze data in various markets, as data privacy laws and regulations differ from country to country. This can make it difficult to obtain and make use of the necessary data to make informed business decisions. Another challenge is ensuring consistency in data analysis across different countries, as data analysts may have different levels of experience and use different methods for analyzing data. Furthermore, cultural differences may also impact the way data is analyzed and interpreted. These challenges highlight the importance of having a robust and adaptable data analysis system in place as Netflix expands globally..

[Audio] This concludes our reading assignment for the week on " Guided Case 1 - Netflix 02". Please discuss below question(s) in conjunction with our reading from Chapter 5 of " Information Technology and Changing Business Processes", or update us with current big data news. Question 1: Considering how much privacy is being discussed in terms of the hardware and services we use on a day to day basis, should Netflix be trusted with this much data because they're only 1 company with 1 service in mind or should they be given the same level of scrutiny that companies like Amazon and Facebook get? Question 2: With how much data Netflix knows about a users interests in content, could there a possibility of AI generated content becoming successful and will the high cost of the technology become financially viable? Question 3: Does better content or better recommendations based on data collected lead to increased user engagement? Question 4: Why do you think Netflix spends so much time on building personalized recommendations for its users? What do you like or dislike about this option? Question 5: How much value do you believe Netflix's original content production has added to their subscriber base? Do you believe that Netflix would still be successful without this, or do you find their original content imperative to their success?.

References. Sharp, K. (2022, May 28). 6 Ways Netflix Uses the Power of Analytics . You are being redirected... Retrieved February 5, 2023, from https://www.ascentt.com/6-ways-netflix-uses-power-analytics/ Mixson, E. (2021, March 30). Data Science at netflix: How advanced data & analytics helps Netflix generate billions . AI, Data & Analytics Network. Retrieved February 5, 2023, from https://www.aidataanalytics.network/data-science-ai/articles/data-science-at-netflix-how-advanced-data-analytics-helped-netflix-generate-billions How netflix uses data to pick movies and curate content . Ohio University. (2022, July 29). Retrieved February 5, 2023, from https://onlinemasters.ohio.edu/blog/netflix-data/.