Business Analytics Assignment: Case Analysis of Netflix

Question

Task: Scenario:
Netflix managers have tirelessly collected and analyzed information to build lucrative markets where none previously existed. Those insights allowed it grow into the only worldwide streaming network. Today, the service has 137 million subscribers and is on pace to do $15 billion in sales through fiscal 2018. [Source: https://www.forbes.com/sites/jonmarkman/2019/02/25/netflix-harnesses-big-data-to-profit-from-your-tastes/#53f0ed5b66fd]

The following questions have to be answered in this business analytics assignment with reference to the preceding scenario: (The student is encouraged to refer to appropriate literature sources and provide proper referencing wherever needed.)

What problem is big data helping the company to resolve
Identify the data used by the company and evaluate the practices followed to use this data.
Analyze the challenges the company had to overcome in order to successfully use data for decision-making.
Note: For Questions 2 to 4, use MS Excel for data analysis.
Refer to data file BankClosures.
This file gives you the data on failures of banks between 2000 and 2012. Create a PivotTable in Excel which group the closing dates of the banks into yearly bins and display the counts of bank closures each year in columns. Make sure that the row labels include the bank locations and allow for grouping the locations into states or viewing by city. Sort the PivotTable so that the states with the greatest number of total bank failures between 2000 and 2012 appear at the top of the PivotTable.

Which state had the greatest number of insured bank closings between 2000 and 2012
How many bank closings occurred in the state of Nevada in 2010 In what cities did these closings occur
Use the PivotTable’s filter option to view bank closings in California, Florida, Texas and New York for the years 2009 through 2012. What is the total number of bank closings in these states between 2009 and 2012 Create a PivotChart to display a column chart that shows the total number of bank closings in each year 2000 through 2012 in the state of Florida. Make sure that your formatting of the chart best conveys the data. Based on the chart, suggest about bank closings between 2000 through 2012 in Florida. Briefly discuss your findings.

Refer to VehicleProduction.
The file gives the details on the number of vehicles produced by different manufacturers in five different years.

Create a line chart for the time series data for years 1 through 5 showing the number of vehicles manufactured by each company. The time series for all manufacturers should be displayed on the same graph.

Based on the line chart that you have created, comment on what you infer about the production amounts between years 1 through 5.

Construct a clustered bar chart showing vehicles produced by vehicle manufacturers using the data for years 1 through 5. Make sure that you represent the years of production along the horizontal axis and cluster the production amounts for the four manufacturers in each year. Identify which is the leading manufacturer in each year.

Answer

1. Answer to question 1 of this business analytics assignment:
1.1 Utilisation of Big Data in problem solving of the company:
With the growth of the internet there has been rise in the utilisation of the Artificial Intelligence and one of the major instruments of the same is the Big Data analytics. As the world is moving towards higher digitisation, data-based decision making and data driven analytics is getting higher preference instead of conventional method of decision making through discussion and iteration process. As one of the best examples of the utilisation of Big Data analytics, case of the Netflix can be mentioned. Here, system engineers have developed such algorithm that allowed the viewers to move from high demand content to low demand content and allowing business success to the company.

Figure 1: Netflix earning

As per the figure 1, it can be seen that earning of Netflix has increased at 56x speed since 2002 and major credit of the same goes to the big data analytics. Initially, Netflix was a mail-based DVD supplier company introduced back in 1997, however, over the years it has become one of the largest host of the online content through introduction of online streaming. Now in present day, introduction of OTT services, Netflix has reached to 137 million of users making $15 billion sales every year since 2018.

One of the major issues that Netflix faced during the initial years was getting customers. There were already many competitors in the market and TV shows in BBC, Hulu has captured large section of the market. Shows aired in TV captured attention most of the consumers during initial days giving little for growth to the DVD service. On the other hand, marketing of DVD suppliers through mail was useful, however, internet was not available to everyone back then. Besides, the service was restricted to US mainland only as OTT services were not introduced largely by then. Consumers preferred high demand DVDs only, that made Netflix face scarcity in supply of the same. This led to business loss for the brand allowing it to overcome the issue with the disruptive techniques.

As the alternative to the present issues, Netflix focused on the plotlines rather than focusing on budget and cast. Through releasing Bandersnatch, Netflix bring in new regime in tele content, where plotline was mainly focused. Through utilising data driven approach in decision making through big data analytics, Netflix focused on allowing consumers to have higher language option, remote option to select Netflix programs, branding through Spotify and other media apps allowed the brand to gain consumer attention. Moreover, backend algorithm of the Netflix allowed the consumer to shift from high demand content to low demand content that has plots and stories that otherwise be failed if published in theatres. Providing service at cost of half of visiting theatre, Netflix solved the viewership problem and issue of its decision making through big data.

1.2 Identification of the data used by the company and evaluation of practices followed:
The utilisation of new advances in the general media field has prompted a progression of fundamental changes in plans of action. Certain areas and organisations have rushed to adjust to the new patterns and work processes, however for the incredible larger part the change has not been finished. As the instance of huge information examination usage in dynamic, instance of Netflix can be referenced. The cycle stream of an organisation gave to on request general media content conveyance, mindful of the convenience of information, starts with a vital business approach in which destinations are set; this will set up their boundaries and execution markers, otherwise called KPIs (key execution pointers). Applying this rationale to Netflix includes understanding the client as an information generator. The 81 million supporters around the world, as per 2016 1T information, speak to 125 million review hours out of each day. They secure such size inside the organisation that it goes past the only quantitative. Practically speaking, every one of these hours devoured by methods for a gadget associated with the web (streaming) is connected to singular data clumps which, when controlled and dissected, encourages Netflix to comprehend its specialty and recognise its objectives. This permits them to plan a progression of information the executives' measure as per their goals. Netflix, self-announced "world's driving web TV administration", puts together its plan of action with respect to membership instead of on the publicising run of the mill of open TV, regardless of whether it be public or business (Sadeh 2019). It utilises information gathered from its clients, not to haggle with sponsors or media offices, to improve individuals' maintenance, lessen scratch-offs, accomplish long haul devotion, and get positive fulfillment evaluations for their item (Markman 2020). Without dismissing the fundamental goal, it is foremost to list a total arrangement of sub-targets, firmly connected to this primary one, to ensure its consistence. Subsequently, information serves different capacities, for example, that recorded underneath:

• Quality control of obtained rights
• Optimal size of their index. As Fernandez et al. (2016) clarifies in the Netflix fast guide for programming, Netflix doesn't try to have everything, except ensures that their substance is intriguing and consumable. Consequently, their inventory turns and the non-reestablishment titles less mainstream among its clients. Month to month pages containing data about forthcoming titles are exceptionally well known.
• The opportunity to offer endorsers customised suggestions about substance and assist them with picking without being overpowered by the size of the index.
• Produce content in light of their customers' inclinations.

Every one of these components joined assistance to ensure consistence with the primary business objective referenced: pull in and evade supporter scratch-off. Most importantly, the recommender framework is at the centre for keeping endorsers and long-haul client devotion. Administration retractions, as Belanova and Gonzalez (2018) point out, are more continuous during the free preliminary month in light of the fact that the proposal administration makes a palatable showing yearning standing customers, yet isn't as powerful with new endorsers, of whom little is known. In information source determination lies the verifiable business objectives. On account of Netflix, it would be characterised by the data gave by the clients themselves. We are discussing an assistance requiring a web associated gadget, which considers a critical number of information social occasion, cooperation, and union prospects.

1.3 Analysing the challenges the company had to overcome for decision-making:
The data created by information from client perusing experience is connected to the following industry layer, known as Business knowledge. It has the mission of settling on better choices. In this sense, having a substance range demonstrated as per the clients' demonstrated tastes assumes past business dynamic. This business rationale comes from past cycles, for example, the utilisation of calculations. For clients of an online stage it is essential to have the option to set up review and utilisation designs. To this end, Machine learning (a part of man-made reasoning meaning to get PCs to learn) plays out a valuable assignment, on account of which information models are surveyed to affirm their legitimacy and, subsequently, apply business rationales.

For a worldwide leaning organisation that, as from April 2016, has had a functioning help in more than 190 nations, tweaking is the best way to gracefully general media substance to every one of those regions in which it works, without disregarding social variety and taking into account nearby general media inclinations. As Maddodi (2019) clarify, until 2001, proposals were primarily founded on the spot of living arrangement. Each time Netflix went to another nation the administration, just as neighbourhood content labelling, was reset physically. This proposal approach expected to dodge normalisation from harming the survey insight, and, along these lines, separating itself from general media societies. Worldwide extension, reinforced in 2016, has forced an employable change. Instead of scaling the algorithmic way to deal with each market, the framework has advanced by putting nations into districts with comparative lists and letting the calculations work exclusively in every locale, except handling their information all around the world. To keep more modest nations from ruining the calculation, in bigger nations, commitment is adjusted dependent on each separate list size. We ought not fail to remember that the decision offered by Netflix in its different working domains is unique and fluctuates in substance and volume (Sang et al. 2017). The administration of general media rights, in spite of the absence of regional limits on the web, is as yet regional and attached to the distinctive existing dissemination windows: home-video, VOD, and TV. Then again, its large information the board has been developing as more supporters join the administration (Quico 2019) and it has been improved with new layers because of worldwide extension. Projects would now be able to utilise new regions as proving ground and it encourages them to refine their customers' general media content inclination maps and recognise possible crowds for their items. The juncture of effectiveness in program plan, the aftereffect of significant customer mindfulness and new market openings, has likewise run corresponding to the ascent in Netflix Originals. This would be the situation of Narcos (2015), a show arrangement dependent on the figure of Pablo Escobar, with a huge extent of discourse in Spanish. This creation came during a period of solid union and expanding Hispanic crowds, both nearby, from the United States, just as global, and corresponding to the administration's arrival in various Latin American nations (Ivanov et al. 2018). Worldwide extension has permitted them to resize their crowd and, gratitude to large information, distinguish a similar watcher sections in the various regions. Their review specialties are currently worldwide.

2. Answer to question 2:
2.1 Pivot table:
Table 1: Pivot table of bank failure dataset

2.2 Bank closing:
Underpinning the table 1, it can be seen that Georgia (GA) has highest number of insured bank closings between 2000 and 2012. As per the table 1, the number of bank closer was 80 in Gorgia.

2.3 Nevada bank closing in 2010:

Table 2: Nevada bank closing

As per the pivot table, during 2010, there were 4 bank closures in Nevada state

During 2010, Las Vegas has 2 bank closing, Reno and Carson City has one bank closure each during the same year.

2.3 California, Florida, Texas and New York bank closure:

Table 3: California, Florida, Texas and New York bank closure

As per the table 3, it can be seen that during 2009 to 2012, there were 58 bank closure in Florida, 33 bank closure in California, 7 bank closure in Texas and 4 bank closure in Bew York. Summing the, as per table 3, there were total 103 bank closures in the mentioned states during 2009 to 2012.

2.4 Bank closing in Florida:

Figure 2: PivotChart for Florida bank closing

As per the figure 1, bank closing of Florida since 2000 to 2012 can be observed. As per the same it can be seen that, during 2000, 2001, 2003, 2005, 2006 and 2007, there were no bank closure in the state. During 2010, there were highest bank closure, however, over the next two years, this has been reduced largely. There has been rise in the bank closure in the state since 2009 and it reached peak in 2010. One of the main reasons of the same is the Global Financial Crisis during 2008 that hit US economy. As the economy propelled out of the global recession, bank closure number falls.

3. Answer to question 3:

3.1 Line chart:

Figure 3: Line graph of year wise car production
Figure above demonstrates the brand wise car production in the 5 year through line graph. As per the figure above it can be seen that during first year, GM produced highest amount of car, whereas Hyundai car production was lowest. This trend continued till 5th year as well, where Hyundai is the lowest car manufacturer, however, Toyota overtook the GM in car production. During the first year, Toyota produced second highest number of cars next to GM, although, during the 5th year, it produced cars higher in number than GM. Volkswagen remained in the same position, where, its car production has increased for first year, however, the change was marginal. Coming to the change in the car production, rapid fall for GM and Toyota can be seen during the 4th year, where both faced fall in car production by approximately 30% compared to the 3rd year. However, during the same year, there has been high rise in the car production by the Hyundai.

3.2 Clustered bar chart:

Figure 4: Cluster bar chart of year wise car production

Underpinning the above figure, it can be seen that combined car production has been highest in the 5th year and lowest during the 4th year. during first year, it can be seen that GM was the leading manufacturer as it has higher share in combined production bar for first year. During 2nd year, leading producer was GM as well. However, since 3rd year onwards, Toyota has been the leading car manufacturer demonstrated through the higher cluster of Toyotas in each bar of 3rd, 4th and 5th year.

4. Answer to question 4:
4.1 Scatter chart:

Figure 5: Scatter plot with hours spent studying as independent variable and total points earned as dependent variable

As per the figure above, it can be seen that scatter plot has been drawn between two variables considering the hours spent studying has been as independent variable and total points earned considered as dependent variable. Hence, hours spent studying has been mentioned in the horizontal axis, whereas, total points earned has been mentioned in the vertical axis. Scatter plot demonstrates the relation between two variable and if there is linear upward movement of the dependent variable, with the rise in the value of independent variable, then the relation can be mentioned as positive in nature and close association between two variables can also be mentioned. Hence, from the figure above it can be mentioned that there is a strong positive relationship between the two variables; change in independent variable will lead to change in the dependent variable in the same direction.

4.2 Regression model:

Underpinning the regression output generated through Excel calculation, regression equation is as follows:
Y = 8.6742 + 0.8014X1 [Where, Y is dependent variable (total points earned) and X1 is the independent variable]

4.3 Model fitness:
Goodness of fit of a model can be explained with the help of the R Square value that defines how much variability of dependent variable data can be explained by the independent variable. higher the value of the R Square, better the prediction of the regression model will be. Apart from this, there is F value, that demonstrates how statistically significant model is. If the F value is higher than critical value, then it would define the model to be a good fit model, whereas, if the F value is lower than critical value, then it would demonstrate the model is not a good fit model. Estimated regression demonstrate that the R Square value is .8277 and this defines, independent variable can explain 82.77% of variability in the dependent variable (Daoud 2017). Besides, F value was significantly high, demonstrating the model is a good fir model.

4.4 Predicted score:
Regression equation: Y = 8.6742 + 0.8014X1 [Where, Y is dependent variable (total points earned) and X1 is the independent variable] Now, if student studies statistics for 95 hours, then predicted score is as follows:

Y = 8.6742 + 0.8014 * 95 = 84.8072 + 76.133 = 84.8072 ~ 84.81
Hence, predicted score of the student with 95 hours of study of statistics will be 84.81

Reference:
Bellanova, R. and González Fuster, G., 2018. No (Big) Data, no fiction Thinking surveillance with/against Netflix. The Politics and Policies of Big Data: Big Data Big Brother.
Daoud, J.I., 2017, December. Multicollinearity and regression analysis. In Journal of Physics: Conference Series (Vol. 949, No. 1, p. 012009). IOP Publishing.
Fernández-Manzano, E.P., Neira, E. and Clares-Gavilán, J., 2016. Data management in audiovisual business: Netflix as a case study.Business analytics assignment El profesional de la información (EPI), 25(4), pp.568-576.
Ivanov, I., Engineer, SCR and Connect, NO, 2018. Mastering Near-Real-Time Telemetry and Big Data: Invaluable Superpowers for Ordinary SREs. Maddodi, S., 2019. Netflix Bigdata Analytics-The Emergence of Data Driven Recommendation.
Markman, J. 2020 Netflix Harnesses Big Data To Profit From Your Tastes, Forbes. Available at: https://www.forbes.com/sites/jonmarkman/2019/02/25/netflix-harnesses-big-data-to-profit-from-your-tastes/sh=5697522e66fd Quico, C., 2019. Television reshaped by big data: impacts and implications for Netflix-like platforms in the age of dataism.
Sadeh, G. (2019). How Netflix uses big data to create content and enhance user experience. Available at: https://www.clickz.com/how-netflix-uses-big-data-content/228201/
Sang, G.M., Xu, L. and De Vrieze, P., 2017, September. Simplifying big data analytics systems with a reference architecture. In Working Conference on Virtual Enterprises (pp. 242-249). Springer, Cham.