7 WACC
11.6 How can the findings be generalized to user-based companies?
As mentioned previously, there are three user-based models. The first is the subscription-based model (ex. Netflix), the second is the advertising-based model (ex. Yelp), and the third is a transaction-based model (ex. Uber) (A. Damodaran, 2018b). Compared with the DCF model, the UBV is likewise flexible to different business models, where the general rule is that the value of user-based company’s operations is the value of existing users adding the value of new users and subtracting the value drag from corporate expenses. Hence, the UBV model can be applied to all user-based firms, including subscription-based, advertising-based and transactions-based firms.
By applying the user-based valuation on different user-based companies, it provides insight into how different user-based companies work and how the existing and new users create value. For example, looking at the transaction-based company Uber, the UBV model can be utilized to value the company,
where the value lies in how often the users use the service, through transactions, instead of subscription fees. Further, the UBV can be applied to other subscription-based companies, for example, Spotify, which generates its revenues from how often users play music. A comparison of different firms through the UBV gives a more in-depth insight into the value of each user, and the change in value when the user base increases. While some firm has numerous advantages in acquiring new users, others might obtain higher value in focusing on maintaining its existing user base. However, some trade-offs need to be taken into consideration when choosing if the user-based valuation model is beneficial.
The first and most critical determinant when deciding the valuation method is the availability of information. Netflix focuses heavily on its subscribers, which can be seen from its information about subscribers in their annual report. To value a company using the UBV method we need access to historical information concerning the firm’s renewal rate, average monthly fee, total subscribers, new subscribers, cost of servicing existing users and lastly, the cost of acquiring a subscriber. Within the industry, especially for Netflix’s peers, there is a lack of this information which can be recognized from the limited user-information disclosure. One alternative could be to substitute company-specific information with industry average when information is minimal. However, if the information is insufficient in several areas, an aggregated valuation method, like the DCF model, might yield more accurate and reliable results (A. Damodaran, 2009).
The benefit of separating a company into existing subscribers, new subscribers, and the corporate drag is most significant when there are differences across the different components, in terms of risk, growth, and profitability. When considering existing and new subscribers for Netflix, there is a substantial difference in growth and profitability. If the value of the two components where equal, the estimates from the two valuation models would be more aligned, making it less beneficial to apply the disaggregated UBV method. Further, the differences in the segments will affect how many components the valuation will consist of. For some user-based firms, it might be beneficial to split the users into additional parts than existing and new users, for example depending on if the user is a premium or a standard user. If the value of users varies widely, it may be better to value the company on an aggregated level, through the DCF model, and look at the averages instead (A. Damodaran, 2009).
In the last decade, there has been a shift where companies measure their success based on the number of users they have, rather than on the traditional metrics of total revenues and cash flows. The UBV model is a universal model, and the user-based valuation can, therefore, be applied to any firm that is user-based, relying on the available information about the user base. It is essential that accounting practices change with the development of businesses in order to make the valuations as accurate as possible. Hence, for the UBV to be the first choice when valuing a user based firm, there has to be an improvement in accounting practices and information closure concerning users.
12 Conclusion
From our thesis, we have found that the video entertainment industry is characterized by increasing competition and a rapidly changing environment. The digital transformation and the increasing use of Big Data to personalize the services has led to a shift in focus from the value of units to the value of users. This transformation is challenging the current framework of the discounted cash flow (DCF) method, leading to a new way of valuing user-based firms, called the User-Based Valuation (UBV). By analyzing the value of Netflix, using both the DCF and UBV, as well as multiple valuations, we have been able to determine the advantages of examining a firm through a user-based perspective.
From the strategic analysis, we discovered that the video entertainment industry is characterized by moderate to high rivalry. Moderate barriers to entry, suppliers becoming competitors, a high threat of substitutes, low switching costs as well as high exit barriers all contribute to enhancing the rivalry among the streaming services. However, Netflix’s focus on technological improvement through the usage of Big Data and algorithms has helped the firm to maintain its market position, where the firm has developed tremendously from starting as a DVD service.
By conducting a profitability and risk analysis on Netflix, we were able to assess the circumstances influencing Netflix’s operations and liquidity risk. However, using a positive net income, both analyzes gave a misconception of Netflix’s financial health as Netflix has had a negative free cash flow (FCF) during the historical period. From the cash flow statement, we learned that Netflix is pushing costs into the future by amortizing the expenses, and the firm has substantial future content obligations which will have a significant impact on future results. Considering that the financial health of Netflix is contingent on the users, a subscription analysis is crucial to get an accurate picture of the financial health of the firm. Through a subscription analysis, we achieved supplementary information regarding the drivers of the revenue and costs. Netflix’s value is driven by a higher subscription growth, as well as high revenues per subscriber, compared to the competitors. Additionally, the firm has high costs per subscriber, mainly due to its high original content spending.
Throughout the valuation and the discussion, we found the UBV framework to be better suited to capture the value drivers of Netflix, by focusing on the value of the subscriber, and more directly incorporating the use of Big Data and network effects. Because of this, the framework provides a higher value per share for Netflix, of $309, compared to the estimated value of $192 per share in the DCF model. Furthermore, we find the equity value in UBV to be less sensitive to several of the forecast estimates, including revenue and costs. As of 31th of March 2019, the share price for Netflix was $359.
Hence, both of the applied models indicate a lower value than the actual price, meaning that we find
Netflix overvalued. These value estimates where further supported through the earnings multiples.
However, looking at growth and subscription multiples, we find that Netflix has a much higher growth and subscriber value compared to other user-based firms, supporting a higher value. This matter is mostly due to their subscription-based model, with high renewal rates, sticky revenue and low variables costs per subscribers.
Although the DCF framework is a universal model, adaptable to any business type, we find it beneficial to replace the model with the UBV when analyzing user-based companies. Because of the increasing importance of user economics, the UBV provides a more informative framework for valuing user-based firms. However, the replacement of the DCF model is conditional on detailed information regarding the users. The validity of the forecasts is crucial since valuation models are highly sensitive to all assumptions made.
13 Perspectivation
After finishing our analysis and writing this thesis, we have gained extensive in-depth knowledge about the valuation of Netflix using both the DCF and the UBV method. As a result, new perspectives regarding Netflix and possible valuation methods have emerged.
The user-based valuation is, analogous with the DCF, based on various assumptions concerning growth rate and risk, and the dynamic market has made people question the quality of these models for high-growth user-based firms. Resulting in a third approach to valuing user-based firms called Metcalf’s law, which could be an alternative way to investigate Netflix’s value (Peterson, 2017). This model uses the value of a network to determine the valuation of an asset of a firm. Since all user-based companies are affected by network effects, this approach could have given us more in-depth knowledge of how the users are changing the value of the business. If we had the opportunity to expand our thesis, we would have included Metcalf’s law.
Metcalf’s law states that the effect of a telecommunications network is proportional to the square of the number of connected users of the system (Peterson, 2017). The law characterized many of the network effects of a network such as the internet and social networking and gives an understanding of the mechanisms of the internet. This model provides a great indication of the value of networks like Netflix – as the number of subscribers increases the value of the service.
By looking at the Metcalf valuation, we might obtain an additional indication of the value of Netflix’s user base, and value the network effect more directly than the UBV. This model has previously been used to value Bitcoin, which likewise has a value based on the user network. We anticipate that a combination of the UBV and Metcalf could have given us a more accurate value of user-based companies.
A limitation to the model is that each user in the network is of equal benefit. However, each user does not have the same value for Netflix, as we previously acknowledge, because different users have different values based on their usage of the service (Cochran, 2018). The different user values are significant when considering new versus old users, as there are higher costs related to the acquisition of new subscribers compared to existing subscribers.
Looking at the thesis in perspective, we acknowledge that taken this delimitation into account the thesis would have been more in-depth and detailed and thus potentially strengthened our analysis.
However, we found this delimitation necessary and predict that we have used enough information to achieve a reliable result.
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