Netflix is a data-based streaming service that provides most movies, TV shows, documentaries, anime, and most of the thousands of devices. Subscribers can watch as many as they want, all for a cheap monthly price. There are often new TV shows, and movies are uploaded weekly. This composition will focus on how Netflix uses Big Data and analytics to provide quality services to its subscribers; it will also discuss challenges Netflix encounters while using data analytics.
Early last year, Netflix announced that it had 50 million new subscribers all over the world. Data from all of the subscribers are gathered and monitored in a trial to know the viewing habit through a combination of this information with cutting-edge analytical methods, Netflix a real Big Data company (Luca & Bazerman, 2021). A quick check of the Netflix jobs page is enough to indicate how seriously analytics and data are taken. Professionals are employed to join teams professionally skilled in using analytical skills in specific business areas such as; messaging analytics, device analytics, personalization analytics, and content delivery analytics. However, although Big Data is utilized across every area of the Netflix Business, its main goal has always been to forecast what its customers will enjoy viewing. Big Data is the fuel that triggers recommendation engines formed to serve the following purpose. First, Predict watching habits. Efforts here started back in 2006 when Netflix was still basically a DVD mailing company. The company started a Netflix prize, giving $ 1 million to the team to create the best algorithm for forecasting how its viewers will rate a movie referring to their previous ratings.
The winning entry announcement was made in 2009; however, the algorithms are continually added and revised. Initially, analysts were limited by the deficiency of customers’ information they had, only movie ID,; only, customers ID, and the date they viewed the movie were the only available data for analysis (Alpaydin, 2020). When streaming became the basic deliver method, delivery data on their viewers became accessible. Data like time taken to choose, how often playback was paused, and time of day that video was watched become measurable. Effects that this caused to viewers’ enjoyment could be seen, and models made to predict the best storm situation of customers constantly being provided with movies they will like.
Second, Big Data has helped Netflix find the upcoming smash hit series. Currently, the company has moved towards basing itself as a content creator, not only a distribution technics for a movie studio and other networks (Hansson & Manfredsson, 2020). Netflix plans have also been fueled by its data, which indicated that its customers had a voracious desire for content directed by starring Kevin Spacey and David Fincher. Data-informed every part of production under the company’s control; this describes even how the color range used on the cover picture of the series was chosen to draw subscribers.
Lastly, Big Data helps the company to analyze the quality of experience. Some factors that affect the quality of experience are closely followed, and models are made to explore user’s behavior. Although Netflix’s vast database of TV shows and movies is hosted internally, it is also mirrored all over the globe by ISPs and other hosts (Zelalem, 2019). As well as providing a quality users experience, by minimizing lag when streaming content all over the world, it also decreases the price for the ISPs. By gathering end-user information on ways, the physical location of the content affects the customers’ experience, arithmetic concerning the placement of data can be built to sustain an optimal service to as many customers as possible. Data points like delays caused by buffering are gathered to inform this analysis. The company has used Big Data to make itself the best leader of the pack. Netflix encounters tuff competition currently and in the future. Commonly notably, Amazon, which bought UK based company’s rival Lovefilm. Will Amazon unseat Netflix from its position as the leader of the streaming content provider? The competition to build more insightful and accurate analytic will be the primary decider.
Netflix uses Analytics to create content, make multimillion dollars decisions, and select movies. In 2006 the company announced the Netflix Prize. The algorithm was created, which improved the prediction accuracy by 10%, although the company did not implement the algorithm. It may appear like the company’s analytics go just as far as views. The majority may think Show House of Cards was selected because the company thought customers would enjoy it (Venkatesan & Lecinski, 2021). The show was not selected solely because it appeared like a good plot. The choice was based on several factors and entirely on data. The truth is that the company is a data-driven company. Believing that Netflix selects content based on whoever they can acquire a license with is very thin and vague. The company requires licenses from studios; however, Netflix does not just select television show and movies at random. Netflix uses analytics to make its decision.
The main work of analytics is to help organizations acquire insight into their clients To optimize their market and provide a better service. Analytics gives companies the quantitative information to make quality, more informed decisions and upgrade their services (Akter et al., 2019). In July 2018, Netflix had 130 million subscribers all over the globe. This huge number of subscribers allows the company to collect a tremendous amount of data. Having this data, the company can make quality decisions and make subscribers enjoy their services. Traditional TV networks do not have these types of privileges. Rating is approximated, green-lighting a pilot is held on intuition and tradition. Netflix is advantaged; being an internet company helps the company to learn their subscribers well. For instance, if a person is watching a series, Netflix can monitor the completion rate of the subscribers. For example, a person at Netflix may require knowing how many subscribers who started a series watched it to the end, and then they get the result. They later collect this data and observe customers trends to know engagement in a clear way. The following are some of the events Netflix tracks;
Because the subscriber leaving the app after viewing content may indicate, they are more likely to quit. Through Netflix analytics, the company may know the number of content subscribers requires watching to be less likely to cancel. If, for example, Netflix knows that if they get each subscriber watching at least 15 hours of content monthly, they are 75 percent less likely to quit. With this information, Netflix will find out what to do to help their subscribers watch at least 15 hours of content monthly. Some of the ideas bellowed through analytics include: enable post-play, this automatically plays next episode, unless the subscriber opts out. On movies, indicate movie suggestions immediately after the credit begins rolling and allows subscribers to play right from the screen. The company can add these features to their mobile apps and their webs through analytics. This is the theory behind why Netflix decided to implement post-play and how analytics can help the Netflix Company.
Challenges of Data Analytics
Data analytics are very necessary for Netflix, especially risk managers. Analytics improve decision-making, monitor performance, and help employees forecast losses; however, attaining these advantages is said than done. The data analytics operation contains components that can help some of the initiatives. By putting together these components, the data analytics initiative creates a clear picture of where the company is, where the company has been, and the company’s goal. Many challenges can prevent Netflix from gathering and use analytics.
Netflix being data-driven company use of Big Data, employees and risk managers are usually overwhelmed by the amount of data gathered (Sedkaoui, 2018). The company may receive data from every incident and event daily, leaving analysts with overwhelming interlocking information. There is a necessity for a data system that automatically gathers and analysis the data. Performing this process manually is time-consuming.
With a lot of data available, it becomes hard to dig deeper and collect the necessary information. When workers are overwhelmed, they might not completely analyze the information or concentrate on the easiest data to gather instead of digging deeper into those that truly add value. Outdated information can have a negative result on making decisions.
Netflix being a data-driven company, collected data requires to be visually presented in charts or graphs. Although this tool is significantly useful, it is overwhelming to create them manually. It is time-consuming and frustrating to pull data from different sources and present them in a reporting tool.
Moving information into a common centralized system has less impact if it is not easily available to people who require that information. Risk managers and the decision-making team requires access to all of the company’s data to know what is happening every moment. Accessing data should be the simplest part of data analytics.
Inaccurate data is very harmful to data analytics. Bad input will lead to the unreliable output. Manual error during data entry is the key contribution of inaccurate data. This might lead to negative outcomes if the company uses such data for decision-making.
Users may be anxious or confused about shifting from traditional data analysis technic, even though they know the advantages of automation. People do not like changes, especially when they are familiar and comfortable with their traditional things.
Another challenge Netflix risk managers face is budget. The risk department is small; its budget to purchase items such as an analytics system is not approved or takes time before it is approved.
Netflix, as a data-based company, struggles with analytics because of a lack of kills. Employees may not have the skills to run in-depth data analysis.
Analytics can be difficult to scale as a company, and the amount of information it gathers grows. Gathering data and creating reports build up into a complex. A system that can grow with the company is necessary to manage this challenge.
Netflix might accept the output of information as conclusive, yet it is not. Data analysis should be showcases correlation, but not causation. The company might fall into the trap of believing the output of data analysis and end up confusing correlation with causation.
As the analyses team becomes more popular in the company, company executives demand more results from the analysis team. Executives expect great returns and a huge amount of reports on all kinds of data.
Analysis for Bank Closer
Analyzing and keeping bank records is one way of identifying failures and assisting in the establishment of models that can aid in detecting the probability of banks collapsing and helps to develop preventive behavior in advance. It also helps identify possible foreshadows of any failure and establish ways of how to prevent its occurrence. This also helps in being alert to note if there is an increase in the effect of management quality factors that may be implemented during a crisis of financial instability. In 12 years, the united states have suffered many failures in banking leading to many bank closures and others collapsing at times and recording very few closures per year in different states. The pivot table below is derived from data collected on the number of closures per year in foreign banks located in other states from the year 2000 to the year 2012.
The table above shows a very low number of bank closures recorded in the initial stages of the 12 years, and the record increased in numbers as time advanced. The table shows that GA has the highest bank closures, followed by FL recording 80 and 63 closures. Many states recorded below 10 bank closures the whole period, with others recording only one closure. The average number of bank closures rages around 10.65 for the entire period. In the state of Nevada, bank closings occurred only three times throughout the whole period. Between the years 2009 and year 2012, the states of California, Texas, and New York recorded a total of 4 closures, with Florida recording zero closure.
The column chart below shows the total number of bank closures in Florida from the year 2000 to 2012. These closures were a result of longstanding capitals and issues in quality asset finance.
Vehicles Production Analysis
Automobile demand is attached to the prices of vehicles with disposal of per capita income, prices of fuels, and products that end the supply chain. Prices of vehicles are comprised of the cost of material and equipment with higher steel for ultimate retail prices. During the past five years, the automobile industries have experienced high steal and prices of manufacturing materials and products and manufacturing costs. On the other side, demand for these automobiles’ affordability is dependent on the amount of their disposable income. An increase in revenue increases the propensity of the prospective buyers to purchase and vice versa. During periods of low economy, incentives received have increased the affordability and rising demand, especially for Toyota and GM motors. This has caused a significant increase for these two companies, thus leading to the increased rate of their vehicle production.
In five years, four companies involved in manufacturing Toyota, GM, Volkswagen, and Hyundai vehicles produced millions of these vehicles. The table below shows the production record with the number of cars produced by each manufacturer per year in a five-year production period. A line chart for the time series data for year 1 through to year five was created to analyze to show the number of manufactured vehicles by each company. Consumers are increasingly informed and have information on a vehicle. In a period of inflation, customers familiarize themselves with a dealer’s costs from a consumer publication. This has widened consumer awareness, and access to such information has resulted in changes in demand and curves.
From the line chart above, it is evident that all through the whole period, Toyota made the highest number of production followed by GM company while Hyundai recorded the lowest number of manufactured motors the most increased production was recorded during the second year of production and was made by GM company with 9.35 million, while Toyota made a 9.24million during the third year. The lowest production recorded belonged to Hyundai in their first year of production with a record of 2.51 million. This indicates a broad market for Toyota and GM motor vehicles, which increases their demand, leading to high production.
The rate of output is varying throughout the five years. It is decreasing at a point and increasing at another except for Volkswagen and Hyundai, which have recorded rising curves throughout the whole period. On average, the four companies average their production. A bar chart was created showing vehicles produced by each vehicle manufacture through the whole production period. The chart shows the leading manufacturer in each year and the number of vehicles they manufacture. Several things affect the production of vehicles in a company. They include; a large number of suppliers, the accessibility of materials, any threat that the supplier may pose to forward integration (Nkomo, 2019). Buyer power may influence the demand curves by the bargaining power that may cause swelling in lower prices and the desire of a customer to switch from one brand to another and choose an alternative car brand. Buyers are also susceptible to costs, and deciding which car to purchase may take a lot of time. The threat of substitutes may also affect the production rate. Since there are many alternative modes of transportation, replacements can also offer the same convenient service, which may cost them less and are more friendly to the environment than automobiles. The competitive rivalry also affects production in a significant way.
A high number of competitors can reduce the production motivation the size of competing firms may vary but may cause competition for different consumer segments. For any company, this can only be good if the customers remain loyal to the firm and moderate the chances of being acquired by a competitor.
One way of measuring the performance of any project is the amount of effort put into the work or the energy invested into it. A common principle accepted in economics is that there should be a positive link between input and output or between effort invested verse performances. There are mixed results, and some are somewhat disconcerting; regarding the relationship between the amount of time spent studying and the performance output, the association is positive though not significant in magnitude. However, an explanation postulated to discuss the lack of a standard measure of time spent in studying.
A statistical Professor at Eaton business school wanted to measure the relationship between the amounts of time invested by his students who attended the course in a previous semester with the score they attained. He recorded the amount of time the students participated in the class sessions with their scores in this subject. He took data of 156 students and recorded the time in hours spent studying the subject and the total points earned by each student. A scatter point was drawn with studying as the independent variable. A regression equation was developed from the data provided that can help predict the score gained by each student by the hours of studying. From the model developed above, the variation in the sample values. Some other studies have discussed the extensive aptitude of scholars as a critical factor in determining their academic performance, to be more specific. There exists a positive link.
To be able to predict the score gained relative to the hours of study, a regression equation was developed as below
y= a+b x +e
Given that n = 5, a=
∑ (∑ xy)(∑ x^2 )-(∑ x)(∑ xy)/n(∑ x^2)-(∑ x^2
b=n(∑xy)-( ∑x)( ∑y/n(∑ x)^2
This model explains the relationship between the time spent studying and the relevant score gained.
If a student spent 95 hours studying statistics from the model developed above, he would gain a score of;
The scatter chart shows that the amount of time studying is directly proportional to the student’s score. The regression analysis above has yielded mixed findings. Still, with statistical significance, if we would consider a pearl of conventional wisdom, we expect that the amount of time spends during studies is positively related to the score of each of the students; however, there could also be another dependent variable if we put in mind the that the way the time of studying is managed would also influence the performance since good it also requires skills. If a student spends a lot of time studying but has not managed this time well, their performance will not be positively related to their performance. Class attendance is as well something to be considered. Suppose gives an assured reflection that at least the student was actively involved in class and that the credit hours are cumulative of the total time spent and analyzed out of their prominent involvement in educative literature. In addition, the cumulative hours are the credit hours that are completed by the end of the learning term. For an average student to raise their performance, their study time should be spent with many technicalities. They should also have the willingness and the ability to re-prioritize their desired goals.