Introduction
Netflix made two significant strategic moves that led to its success. First, the company did not explore all the available markets at once but in phases. It ensured that the market it exploits has been analyzed and is suitable for expansion. For instance, the first phase of its entrance into Canada in 2010 was greatly enhanced by its closeness to the United States and the similarities the two countries have. This enables the company to face a few challenges and adapt quickly. Additionally, this strategic move enabled the company to learn the best moves for expansion. The second phase involved an extensive and quick expansion strategy using lessons learned from the first phase (Brennan, 2018). It made the company make important choices based on the market’s attractiveness. Investments in data science greatly enhanced this phase. The third phase enabled the company to expand its operation to 190 countries using the understanding from the first two phases, which involves adding more features to their services, such as more languages, subtitles, and payment partnerships.
Discussion
The second strategic move was the company’s response rate in every new market. Netflix partnered with other companies such as Vodafone, leading to win-win situations. The company focused on filling the gap in customer preferences and producing original content for local and global clients (Brennan, 2018). This has enabled the company to attract viewers at all levels. Through deep customer insights, Netflix determines the best content for its customers and strives to achieve them. Netflix being a company that offers streaming services to its client from television shows, movies, and animations, it requires an insight into its customers’ preferences. Investing in big data provides the company with this insight making them identify various ways that they can use to improve client satisfaction and the quality of the services they provide (Brennan, 2018). This also enabled the company to identify potential risks. The data retrieved by the big data analytics include the content their clients prefer, language, geographical information, and devices used for streaming.
Exponential globalization is the expansion strategy that Netflix Company uses to achieve its current success. This strategy is carefully selected in that it is done at a significant speed leading to an increased number of viewers and countries. It has enabled the company to expand its network quickly compared to other companies such as Amazon prime and local companies. Expansion from one country to another can be a challenging factor leading to failure. For example, Walmart failed when trying to expand to the Japanese and German markets. There is a wide variation between American culture and German culture. This includes personal space, business hours, and the location of the shopping centers (“Case Study,” 2022). This geared the company’s failure in Germany as the variation were significantly wide, forcing the company’s exit. I agree with the assessment because culture defines a group of people, and conflict arises when there is a significant variation, leading to failure.
Expansion of various companies in the past has failed because of inappropriate planning, lack of adaptability, and poor market acceptance. First, inappropriate planning makes companies enter markets that they are not well prepared for. This results in cultural conflict leading to losses because of differences between the local and global cultures. Second, a lack of adaptability makes companies suffer losses in foreign markets as they fail to involve the local communities, suppliers, and government officials hence lacking support (Robles & Jauregui, 2017). Lastly, poor market acceptance makes the companies fail. Good products may not always mean that the foreign markets will accept them. A company requires promotional strategies that appeal to the locals’ way of life and their culture.
Hypothesis testing
Table 1: One sample t-test
Table 2: Independent t-test
Summary
From table 1 above, it can be noted that the average time in queue is lower than the standard industry time of 150 seconds. However, the p-value above is greater than 0.05, hence null hypothesis average time in queue is equal to the standard industry time of 2.5 minutes is not rejected. Therefore, it not necessary for the company to allocate significant resources to ensure that they are able to improve their average time in queue. This will not result in significant changes as the average time in queue is hypothetically the same as the standard industry time.
Table 2 above shows that the two groups have a significant variation in mean. The independent t-test shows that at α=0.05, there is a significant variation between the new protocol (PE) and the traditional protocol (PT). This is because the two-tail p-value is less than 0.05 and thus PE is not lower than the PT protocol. Therefore the null hypothesis PE being equal to PT is rejected. The new protocol (PE) did not serve its purpose because the independent t-test above shows that the two groups have varying mean difference hence PE is not significant as the p-value is less than 0.05.
References
Brennan, L. (2018). How Netflix Expanded to 190 Countries in 7 Years. Harvard Business Review. Web.
Case Study: Companies That Failed Internationally From a Lack of Social Understanding | MediaBeacon. MediaBeacon. (2022). Web.
Robles, F., & Jauregui, K. (2017). International markets entry strategy determinants: an exploratory study in Peru. Cuadernos De Administración, 33(59), 2-19. Web.