More and more of the video streaming services are producing in-house content, and this has become a way for the streaming services to differentiate their services. There is plenty of hype around Netflix’s use of Big Data and algorithms in their content production, but does it merely offer operational advantages, or can it provide the firm with a sustainable competitive advantage? Unlike traditional Hollywood studios, Netflix makes use of Big Data and algorithms to attract viewers, in relation to solely relying on marketing campaigns. We will explore the advantage of Netflix’s use of algorithms and Big Data in decision making through the ‘resource-based view of the firm,’ or VRIN, which states that, for a resource to provide a competitive advantage, it must be valuable, rare, inimitable, and non-substitutable (Barney, 1991). We have chosen to only evaluate Big Data and algorithms in the VRIN analysis, based on the findings in the external analysis and we moreover find this asset to be an essential driver in our forecasting. We will refer to Big Data and algorithms as Big Data-Driven Decisions in the following analysis.
3.3.0.1 Is Big Data-Driven Decisions Valuable?
Companies in the top third of their industry, in the use of data-driven decision making, were, on average, 5% more productive and 6% more profitable than their competitors (McAfee & Brynjolfsson, 2012). Managers can measure, and accordingly know, fundamentally more about their businesses, and translate that knowledge into improved decision making and performance (Davenport et al., 2012).
Netflix is known for using Big Data and analyzing every moment of a customer’s journey on their online interface. As subscribers watch more, Netflix’s recommendation engine collects more data, the understanding of subscribers behavior enhances and enables better personalization of the service. The improved service results in more engaging user experience and serves as an internal trigger to use the service again. Furthermore, binge-watching, as mentioned in PESTEL, is another example of how subscribers invest time on the service. The more information the subscribers invest in the service, the more committed they become to the service. Hence, this time investment creates value for Netflix.
Through their data analysis, Netflix builds its digital strategy, and it has allowed them to create successfully original premium content (MUSO, 2019). Netflix uses Big Data Analysis when deciding which programs will be of interest to the customers. For example, their recommendations allow Netflix to surface niche titles that would not find an audience on a traditional cable network, but that its viewers love (Levy, 2016). This allows the firm to save money on its content expenditure since it can maximize the value of inexpensive titles. Netflix estimates that their algorithms help them save 1 billion dollars a year (Levy, 2016).
Although Netflix can use the data to access customers’ preferences, the theory suggests that Big Data alone is unlikely to give them any value. It is merely when Big Data is combined with managerial, engineering, and analytic skill in determining the experiment or algorithm to apply to such data that it proves value to firms (Bean, 2016). Considering the high savings Netflix achieves from their algorithms, one can argue that the firm has the proper managerial, engineering and analytic skills to drain value out of the data.
The Big Data and algorithms become even more profitable joined with Netflix’s in-house production.
Using viewers’ viewing habits, Netflix can, by combining data and creativity, engineer shows that have all the elements to become a phenomenon. The success rates for Netflix’s original shows are 80% as compared to the 30%-40% success rates of traditional TV shows (Intelligence, 2018).
3.3.0.2 Is Big Data-Driven Decisions rare?
For Big Data-driven decisions to be a rare resource, it would mean that few other firms possess it.
Computer-mediated transactions allow for new databases that can store magnitudes of data and can process queries on more than a trillion records in few seconds. Previously, this was extremely costly, but the digital transformation has made it a variable cost, making the barriers to entry in this area low (Varian, 2014). Reducing the costs of collecting and analyzing Big Data allows for more firms to incorporate Big Data analysis in their strategies.
Nowadays, several of Netflix’s competitors are using Big Data when making decisions. One example is Amazon, who claims that Big Data analysis is a necessary core of their corporate strategy (MUSO, 2019). The fact that one of Netflix’s most significant competitor has incorporated Big Data analysis suggests that there are many opportunities to gather customer data and that it is no longer rare.
Likewise, Hulu captures substantial value from its search recommendation engine, which use consumer behavior data to drive the majority of the recommendation. Hulu aims to promote content to users that align with the customer’s interests (HBS, 2019).
Regardless of Amazon and Hulu using Big Data, many of Netflix’s competitors do not have easy access to data about their customers. Amazon Prime Video has the most significant number of titles, with more than 17 000 films in its library, compared to Netflix with 3 839 films. However, Netflix has had more quality films than Hulu, Amazon, and HBO combined (Brantner, 2019). This is supported by the fact that Netflix is the most viewed paid streaming service. Figure A2.1 in Appendix displays the average time spent per user among US adults, age 18 and older, over the last three months of 2017 for HBO, Amazon Prime Video, Netflix and Youtube. Youtube, as a free streaming service, is way beyond the three other paid subscriptions. Netflix has the highest number of subscribers, giving the firm more
comprehensive information about their subscribers. Taking this into account, it might be difficult for competitors both inside and outside the online community to replicate the same level of success in data-driven decision making.
The strategy of the firms will play a big difference in the value extraction of Big Data. In 2016, TEDX released a video presentation that Wernicke gave, illustrating Netflix and Amazon’s approach to value extraction from data analysis. The presentation indicates that there are substantial differences in the Big-Data strategies of Netflix and Amazon. For example, when launching their first show, Amazon used Big Data to analyze reactions to a controlled subset of pilot episodes, while Netflix used Big Data across the whole value-chain, analyzing subscribers’ preferences from the beginning of the process when they decided what type of series to produce (Grant, 2018).
According to a survey released by investment banking company Morgan Stanley, 39% of US consumers said that Netflix offers the “best original programming” compared with other subscription-video services.
HBO charted a distant second at 14%, while Amazon Prime Video charted third with 5%, Hulu at 4%, Showtime Networks with 3%, Starz at 2% and Cinemax at 1% (Spangler, 2018). This matter might indicate that Netflix’s use of algorithms and Big Data in its content production is better compared to other competitors, which confirms that their content production strategy is rare.
3.3.0.3 Is Big Data-Driven Decisions inimitable?
For Big Data to be inimitable, no other firm should be able to replicate the advantage. Lambrecht
& Tucker (2015) point out two underlying economic reasons for why Big Data in many instances are unlikely to be inimitable. First, Big Data is non-rivalrous, meaning consumption of the good does not decrease its availability to others. Second, Big Data has near-zero marginal cost of production and distribution even over long distances (Shapiro et al., 1999). The low replication costs also mean that its easier for new entrants or small companies to reproduce or copy the information at a low price.
Bits can be replicated by anyone, at near zero cost without degrading the quality of the initial good (Goldfarb & Tucker, 2017).
We argued that Netflix has access to a more substantial amount of Big Data than several of its competitors, because of their extensive catalog and large subscriber base. Further, Netflix used six years to collect enough data for them to be sure that they had the information they needed to produce their successful content. Appropriately, their first production, which was House of Cards, was indeed a huge success (Intelligence, 2018). The time it took for the firm to collect enough data might demonstrate that even though other firms might have access to the same data from external sources, it might take a long time to build suiting algorithms for their customer base.
Another advantage Netflix possesses is its interactive-based shows, such as Bandersnatch, that can give new data insight to the company. According to Damiani (2019), Netflix uses the gathered data from the user participation to create an internal programmatic marketing infrastructure, as mentioned in the PESTEL analysis. Since the viewers need to make real-life decisions about for example their product preference, such as the choice between cereals in Bandersnatch, individual personalized patterns can be discovered. Further, how users handle certain decisions, for example, if the leading role in Bandersnatch has to jump off the balcony, offer insight about what the viewers want out of a story and how they want the characters to act (Damiani, 2019).
On the other hand, external producers are offering a similar amount of data, collected from for example social media platforms. Research in computer science has emphasized that by combining a myriad of external online profiles, external firms can attain colossal insights into any customer (Calandrino et al., 2011). This data could be utilized by Netflix competitors to obtain a similar amount of user-information, without having the same subscriber base.
3.3.0.4 Is Big Data-Driven Decisions non-substitutable?
For a resource such as Big Data or algorithms to provide a sustainable competitive advantage, there have to be no other means of achieving success in the specific industry. However, we see firms like Instagram and Snapchat entering the market with no embedded data advantage or exclusive insights into customers’ preferences. Despite this, the firms were able to attract a significant customer base and extract value. This might implicate that the knowledge gained from Big Data is substitutable. It is not the Big Data that gives a firm an advantage, but the ability to create a superior value proposition to the customers (Lambrecht & Tucker, 2015).
Substitutes with a lower price might manage to attract customers regardless of an unexceptional content selection and quality. If, for example, subscribers are offered movies and series at a reduced price, this can lower their expectations towards the service. This effect is seen from users downloading content illegal on Pirate Bay, where the quality often is much worse compared to the online streaming services.
With the low replication costs, new start-ups might be able to produce these kinds of services at a very low cost and still compete against firms like Netflix.
Another trend seen in the market is understanding and tracking the piracy of films. By examining the searches and downloads of movies and series before its launch on online platforms, firms can understand pre-existing demand, or, even more crucially, regional pre-release demand. This data can further be used to determine the market value for content. Netflix tracks piracy to examining what television shows are ranked highest on popular piracy sites before acquiring their streaming rights. Likewise,
Hulu tracks piracy to decide what shows to license, believing an audience that pirates are an audience
“passionate enough to break the law” (McAlone, 2016). Firms without access to their own collected Big Data could track piracy of content when deciding what content to acquire. This can give firms an insight into the customers’ demands without spending a great deal of money on data collection and analysis.
On the other hand, one can argue that the value extracted from the internal Big Data and algorithms have a higher value than other substitutes. The data collected on Netflix is continuously updated and improved, which give the firm a "live" update of any changes in their subscriber’s preferences. This enables the firm to obtain an extensive profile on their subscribers which is continuously improving and expanding, where this time-consuming information is not available by any substitutes. Further, Netflix also achieves exclusive insight with regards to the interface of the platform that cannot be obtained anywhere else.
3.3.0.5 How to make the data valuable
Big Data by itself does not confer Netflix with a competitive advantage. However, the firm has advanced algorithms, and a good strategy, which enables them to understand the evolving customer needs and extract significant value from Big Data. Some firms use Big Data to make improvements on their services, but Netflix has a unique strategy, where they use Big Data from the beginning of the value chain, to decide what services to produce. However, we see more and more firms adopting Big Data in their decision making, and there is only a question of time before the competitors implement corresponding strategies and algorithms. Likewise, the low replication costs and the demand for low-cost entertainment make it easy for new entrants to enter the market without collections of customer data.
Because of this, the use of Big Data will provide Netflix with a competitive advantage in the short run.
However, the competitive advantage will not be sustainable in the medium and long term.
3.3.1 Conclusion
Table 3.4: Value extraction gained from Big Data
Competitive advantage YES/NO
Valuable YES
Rare YES
Inimitable NO
Non-substitutional NO
Competitive consequence Short term competitive advantage
4 Financial Analysis
In this section, we will conduct a Profitability Analysis, Risk Analysis, Cash Flow Analysis as well as a Subscription Analysis. We will calculate a variety of financial ratios to achieve a detailed picture of the company’s financial health. By examining the financial performance from 2013 throughout 2018, we aim to obtain a picture of the value drivers of Netflix.
4.0.1 Preparing the Financial Statements for Analytical Purposes
A company consists of operating, investing and financial activities, and before calculating financial ratios, it is essential to separate these activities. Operating activities are the primary forces behind value creation and thereby the main focus in the financial analysis. Such activities are generally unique for the firm and challenging for competitors to copy, while financial activities, on the other hand, are easier to replicate (Petersen & Plenborg, 2012). The following paragraphs describe the balance sheet and income statement, and how they are reformulated. The reformulated financial statements can be found in Table 4.1 & Table 4.3.
4.0.1.1 Balance sheet
The purpose of analyzing the balance sheet is to identify various sources of profitability and look at the firm’s ability to create a surplus (Sørensen, 2012). Invested capital equals the sum of operating assets minus operating liabilities and is the combined investment in a firm’s operating activities. Net financial assets and equity represent the two sources used to finance invested capital, and the sum must be equal to invested capital (Petersen & Plenborg, 2012). We will describe the classification of some of the chosen accounting items in the following paragraphs.
Koller et al. (2010) state that cash and cash equivalents categorized as an operating asset should not include excess cash, and as a result of this suggests 2% of revenue as a benchmark (Koller et al., 2010).
The weakness of this benchmark is its general form, where it does not take industry or firm-specific characteristics into account. Netflix’s primary use of cash includes licensing of content, content delivery, marketing programs, and payroll. Further, the company considers investments purchased with an original maturity of 90 days or less to be cash equivalents (Netflix, 2018). These investments can be seen as operating assets, as they are necessary to keep the business running. However, if there is excess cash from the investments, it is classified as financial assets. Since it is difficult to identify if cash and cash equivalents include excess cash from the annual report, we have chosen to classify the whole post as a financial asset.
Non-current content assets include Netflix’s content library and contain content that will be available for streaming within one or a few years (Netflix, 2018). The assets are consequently characterized as operating assets, as it is a part of the core business. Properties and equipment are technological properties, furniture, buildings as well as equipment related to DVD rental (Netflix, 2018). These items relate to the core of the business and classify as operating assets.
Table 4.1: Analytical Balance Sheet
ANALYTICAL BALANCE SHEET 2013 2014 2015 2016 2017 2018
Other current assets 151,937 206,271 215,127 260,202 536,245 748,466 Current content assets, net 1,706,421 2,125,702 2,905,998 3,726,307 4,310,934 5,151,186 Current assets 1,858,358 2,331,973 3,121,125 3,986,509 4,847,179 5,899,652 Non-current content assets, net 2,091,071 2,773,326 4,312,817 7,274,501 10,371,055 14,960,954 Other non-current assets 129,124 192,981 284,802 341,423 652,309 901,030 Property and equipment, net 133,605 149,875 173,412 250,395 319,404 418,281 Non-current assets 2,353,800 3,116,182 4,771,031 7,866,319 11,342,768 16,280,265 Operating assets 4,212,158 5,448,155 7,892,156 11,852,828 16,189,947 22,179,917 Current content liabilities 1,775,983 2,117,241 2,789,023 3,632,711 4,173,041 4,686,019 Accounts payable 108,435 201,581 253,491 312,842 359,555 562,985
Accrued expences 54,018 69,746 140,389 197,632 315,094 477,417
Deferred revenues 215,767 274,586 346,721 443,472 618,622 760,899 Current liabilities 2,154,203 2,663,154 3,529,624 4,586,657 5,466,312 6,487,320 Non-current content liabilities 1,345,590 1,575,832 2,026,360 2,894,654 3,329,796 3,759,026 Other non-current liabilities 79,209 59,957 52,099 61,188 135,246 129,231 Non-current liabilities 1,424,799 1,635,789 2,078,459 2,955,842 3,465,042 3,888,257 Operating Liabilities 3,579,002 4,298,943 5,608,083 7,542,499 8,931,354 10,375,577 Invested Capital 633,156 1,149,212 2,284,073 4,310,329 7,258,593 11,804,340 Cash and cash equivalents 604,965 1,113,608 1,809,330 1,467,576 2,822,795 3,794,483
Short-term investments 595,440 494,888 501,385 266,206 -
-Financial assets 1,200,405 1,608,496 2,310,715 1,733,782 2,822,795 3,794,483 Long-term debt 500,000 900,000 2,371,362 3,364,311 6,499,432 10,360,058 Financial liability 500,000 900,000 2,371,362 3,364,311 6,499,432 10,360,058 Net financial assets 700,405 708,496 -60,647 -1,630,529 -3,676,637 -6,565,575 Total Funds Invested 1,333,561 1,857,708 2,223,426 2,679,800 3,581,956 5,238,765 Equity 1,333,561 1,857,708 2,223,426 2,679,800 3,581,956 5,238,765 Invested Capital 633,156 1,149,212 2,284,073 4,310,329 7,258,593 11,804,340
4.0.1.2 Income statement
The purpose of the analytical income statement is to separate the income arising from operating activities and financial activities. By doing so, we can calculate financial ratios that evaluate the profitability of the firm’s two activities separately, which is essential when understanding the value creation for Netflix (Sørensen, 2012). Netflix is a multinational company where the countries they operate in have different tax rates. The annual reports are not specific enough with regards to the geographical division of revenue, so a calculation of the average tax rate of the company is not possible. Because the company is dealing with a multinational company with uncertainty in the tax rate, A. Damodaran (2001) suggest using the marginal tax rate of the country in which the company is incorporated. We find it appropriate
to use this method, as Netflix is registered in America, and a significant amount of Netflix’s revenue comes from the US market.
The American tax rate has been stable at 35 % in the period 2013-2017. However, in 2018 the corporate tax changed to 21 %. We will use the company’s effective tax rate, which is corporate tax divided by earnings before tax, in the analytical statements, as this represents actual tax payments. The reported income statements do not separate between tax on operating income and financial income. We allocate the tax between these elements in the analytical statement (Petersen & Plenborg, 2012).
Table 4.2: Tax Payments
Tax 2013 2014 2015 2016 2017 2018
Marginal tax 35% 35% 35% 35% 35% 21%
Reported tax -58,671 -82,570 -19,244 -73,829 73,608 -15,216
Tax on extraordinary items 8,795 - - - -
-Reported tax deducted extraordinary items -67,466 -82,570 -19,244 -73,829 73,608 -15,216
Tax shield 11,250 18,648 57,379 41,750 123,675 79,541
Tax on operating activities -78,717 -101,218 -76,623 -115,579 -50,067 -94,757
Cost of revenues includes amortization of the content library, which is how the firm expenses the content expenses. The content is amortized on a straight line or an accelerated basis, depending on if the content is premiering on the Netflix services or not. In the analytical income statement, the amortization of content library will be included in the cost of revenue and will be treated as regular expenses. Further, depreciation and amortization of property, equipment, and intangible assets stated in the cash flow statement will be included in the income statement under depreciation and amortization.
Table 4.3: Analytical Income Statement
ANALYTICAL INCOME STATEMENT FOR NETFLIX 2013 2014 2015 2016 2017 2018
Revenue 4,374,562 5,504,656 6,779,511 8,830,669 11,692,713 15,794,341
Cost of revenue domestic streaming 1,849,154 2,201,761 2,487,193 2,855,789 3,319,230 4,038,394 Cost of revenue international streaming 774,753 1,154,117 1,780,375 2,911,370 4,137,911 5,776,047
Cost of revenue domestic DVD 459,349 396,882 323,908 262,742 202,525 153,097
Gross profit 1,291,306 1,751,896 2,188,035 2,800,768 4,033,047 5,826,803
Total marketing expenses 503,889 607,186 824,092 991,078 1,278,022 2,369,469
Technology and development 378,769 472,321 650,788 852,098 1,052,778 1,221,814
General and administrative 180,301 269,741 407,329 577,799 863,568 630,294
EBITDA 228,347 402,648 305,826 379,793 838,679 1,605,226
Depreciation and amortization of property, equipment and intangibles 48,374 54,028 62,283 57,528 71,911 83,157
EBIT 179,973 348,620 243,543 322,265 766,768 1,522,069
Tax (78,717) (101,218) (76,623) (115,579) (50,067) (94,757)
NOPAT 101,256 247,402 166,920 206,686 716,701 1,427,312
Financial income -3,002 -3,060 -31,225 30,828 -115,154 41,725
Financial expences 29,142 50,219 132,716 150,114 238,204 420,493
Financial expense, net -32,144 -53,279 -163,941 -119,286 -353,358 -378,768
Tax financial expense 11,250.40 18,647.65 57,379.35 41,750.10 123,675.30 79,541.28
Financial expense after tax -20,894 -34,631 -106,562 -77,536 -229,683 -299,227
Income before extraordinary items 80,363 212,771 60,358 129,150 487,018 1,128,085
Loss on extinguishment of debt -25,129 - - - -
-Sum extraordinary items -25,129 - - - -
-Tax extraordinary items 8,795.15 - - - -
-Total extraordinary items after tax -16,334 - - - -
-Net income 64,029 212,771 60,358 129,150 487,018 1,128,085